System for tracking and analyzing welding activity

- Lincoln Global, Inc.

A system and a method for tracking and analyzing welding activity. Dynamic spatial properties of a welding tool are sensed during a welding process producing a weld. The sensed dynamic spatial properties are tracked over time and the tracked dynamic spatial properties are captured as tracked data during the welding process. The tracked data is analyzed to determine performance characteristics of a welder performing the welding process and quality characteristics of a weld produced by the welding process. The performance characteristics and the quality characteristics may be subsequently reviewed.

Skip to: Description  ·  Claims  ·  References Cited  · Patent History  ·  Patent History
Description

More than one reissue application has been filed for the reissue of U.S. Pat. No. 8,274,013. This application is for reissue of U.S. Pat. No. 8,274,013, and is a continuation reissue of application Ser. No. 14/177,692, which is an application for reissue of U.S. Pat. No. 8,274,013, which claims priority to and the benefit of U.S. provisional patent application No. 61/158,578 filed Mar. 9, 2009, and which is incorporated herein by reference in its entirety.

This U.S. patent application claims priority to and the benefit of U.S. provisional patent application Ser. No. 61/158,578 which was filed on Mar. 9, 2009, and which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

Certain embodiments of the present invention pertain to systems for tracking and analyzing welding activity, and more particularly, to systems that capture weld data in real time (or near real time) for analysis and review. Additionally, the embodiments of the present invention provide a system for marking portions of a welded article by indicating possible discontinuities or flaws within the weld joint.

BACKGROUND

In many applications, ascertaining the quality of weld joints is critical to the use and operation of a machine or structure incorporating a welded article. In some instances, x-raying or other nondestructive testing is needed to identify potential flaws within one or more welded joints. However, non-destructive testing can be cumbersome to use, and typically lags the welding process until the inspector arrives to complete the testing. Additionally, it may not be effective for use with all weld joint configurations. Moreover, non-destructive testing does not provide any information about how the weld was completed. In welding applications where identifying waste is vital to producing cost effective parts, non-destructive testing provides no insight into problems like overfill.

Further limitations and disadvantages of conventional, traditional, and proposed approaches will become apparent to one of skill in the art, through comparison of such approaches with the subject matter of the present application as set forth in the remainder of the present application with reference to the drawings.

SUMMARY

The embodiments of the present invention pertain to a system for tracking and analyzing welding activity. The system may be used in conjunction with a welding power supply and includes a sensor array and logic processor-based technology that captures performance data (dynamic spatial properties) as the welder performs various welding activities. The system functions to evaluate the data via an analysis engine for determining weld quality in real time (or near real time). The system also functions to store and replay data for review at a time subsequent to the welding activity thereby allowing other users of the system to review the performance activity of the welding process.

These and other novel features of the subject matter of the present application, as well as details of illustrated embodiments thereof, will be more fully understood from the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of a welder using an embodiment of a system for tracking and analyzing welding activity;

FIG. 2 is a schematic representation of an embodiment of the system of FIG. 1 for tracking and analyzing welding activity;

FIG. 3 is a schematic representation of an embodiment of the hardware and software of the system of FIGS. 1-2 for tracking and analyzing welding activity;

FIG. 4 is a flow diagram of an embodiment of the system of FIGS. 1-3 for tracking and analyzing welding activity;

FIG. 5 is a flowchart of an embodiment of a method for tracking and analyzing welding activity using the system of FIGS. 1-4; and

FIG. 6 illustrates an example embodiment of a graph, displayed on a display, showing tracked welding tool pitch angle versus time with respect to an upper pitch angle limit, a lower pitch angle limit, and an ideal pitch angle.

DETAILED DESCRIPTION

FIG. 1 is a perspective view of a welder 10 using an embodiment of a system 100 for tracking and analyzing welding activity while performing a welding process with a welding system 200. FIG. 2 is a schematic representation of an embodiment of the system 100 of FIG. 1 for tracking and analyzing welding activity. FIG. 3 is a schematic representation of an embodiment of the hardware 110, 130 and software 120 of the system 100 of FIGS. 1-2 for tracking and analyzing welding activity. FIG. 4 is a flow diagram of an embodiment of the system 100 of FIGS. 1-3 for tracking and analyzing welding activity. FIG. 5 is a flowchart of an embodiment of a method 500 for tracking and analyzing welding activity using the system 100 of FIGS. 1-4.

Referring again to the drawings wherein the showings are for purposes of illustrating embodiments of the invention only and not for purposes of limiting the same, FIG. 1 shows a system 100 for tracking and analyzing manual processes requiring the dexterity of a human end user 10. In particular, system 100 functions to capture performance data related to the use and handling of tools (e.g., welding tools). In one embodiment, the system 100 is used to track and analyze welding activity, which may be a manual welding process in any of its forms including but not limited to: arc welding, laser welding, brazing, soldering, oxyacetylene and gas welding, and the like. For illustrative purposes, the embodiments of the present invention will be described in the context of arc welding. However, persons of ordinary skill in the art will understand its application to other manual processes. In accordance with alternative embodiments of the present invention, the manual welder 10 may be replaced with a robotic welder. As such, the performance of the robotic welder and resultant weld quality may be determined in a similar manner.

In one embodiment, the system 100 tracks movement or motion (i.e., position and orientation over time) of a welding tool 230, which may be, for example, an electrode holder or a welding torch. Accordingly, the system 100 is used in conjunction with a welding system 200 including a welding power supply 210, a welding torch 230, and welding cables 240, along with other welding equipment and accessories. As a welder 10, i.e. end user 10, performs welding activity in accordance with a welding process, the system 100 functions to capture performance data from real world welding activity as sensed by sensors 160, 165 (see FIG. 2) which are discussed in more detail later herein.

In accordance with an embodiment of the present invention, the system 100 for tracking and analyzing welding activity includes the capability to automatically sense dynamic spatial properties (e.g., positions, orientations, and movements) of a welding tool 230 during a manual welding process producing a weld 16 (e.g., a weld joint). The system 100 further includes the capability to automatically track the sensed dynamic spatial properties of the welding tool 230 over time and automatically capture (e.g., electronically capture) the tracked dynamic spatial properties of the welding tool 230 during the manual welding process.

The system 100 also includes the capability to automatically analyze the tracked data to determine performance characteristics of a welder 10 performing the manual welding process and quality characteristics of a weld 16 produced by the welding process. The system 100 allows for the performance characteristics of the welder 10 and the quality characteristics of the weld to be reviewed. The performance characteristics of a welder 10 may include, for example, a weld joint trajectory, a travel speed of the welding tool 230, welding tool pitch and roll angles, an electrode distance to a center weld joint, an electrode trajectory, and a weld time. The quality characteristics of a weld produced by the welding process may include, for example, discontinuities and flaws within certain regions of a weld produced by the welding process.

The system 100 further allows a user (e.g., a welder 10) to locally interact with the system 100. In accordance with another embodiment of the present invention, the system 100 allows a remotely located user to remotely interact with the system 100. In either scenario, the system 100 may automatically authorize access to a user of the system 100, assuming such authorization is warranted.

In accordance with an embodiment of the present invention, the system 100 for tracking and analyzing welding activity includes a processor based computing device 110 configured to track and analyze dynamic spatial properties (e.g., positions, orientations, and movements) of a welding tool 230 over time during a manual welding process producing a weld 16. The system 100 further includes at least one sensor array 160, 165 operatively interfacing to the processor based computing device 110 (wired or wirelessly) and configured to sense the dynamic spatial properties of a welding tool 230 during a manual welding process producing a weld 16. The system 100 also includes at least one user interface operatively interfacing to the processor based computing device 110. The user interface may include a graphical user interface 135 and/or a display device (e.g., a display 130 or a welding display helmet 180 where a display is integrated into a welding helmet as illustrated in FIG. 2). The system 100 may further include a network interface configured to interface the processor based computing device 110 to a communication network 300 (e.g., the internet).

In accordance with an embodiment of the present invention, a method 500 (see FIG. 5) for tracking and analyzing welding activity includes, in step 510, setting up a manual welding process, and, in step 520, sensing dynamic spatial properties (e.g., positions, orientations, and movements) of a welding tool 230 during a manual welding process producing a weld using at least one sensor (e.g., sensor arrays 160 and 165). In step 530, the method includes tracking the sensed dynamic spatial properties over time during the manual welding process using a real time tracking module 121 (see FIG. 4). The method also includes, in step 540, capturing the tracked dynamic spatial properties as tracked data during the manual welding process using a computer based (e.g., electronic) memory device (e.g., a portion of the hardware 150 and software 120 of the processor based computing device 110). The method further includes, in step 550, analyzing the tracked data to determine performance characteristics of a welder 10 performing the manual welding process and/or quality characteristics of a weld produced by the welding process using a computer based analysis engine 122. In step 560, at least one of the performance characteristics and the quality characteristics are reviewed using a display device (e.g., display device 130). Alternatively, a visualization module or a testing module may be used in place of the display device 130, as are well known in the art.

The method 500 may initially include selecting welding set up parameters for the welding process via a user interface 135 as part of step 510. The method may also include outputting the performance characteristics of the welder 10 and/or the quality characteristics of a weld to a remote location and remotely viewing the performance characteristics and/or the quality characteristics via a communication network 300 (see FIG. 3).

The system 100 for tracking and analyzing welding activity comprises hardware and software components, in accordance with an embodiment of the present invention. In one embodiment, the system 100 incorporates electronic hardware. More specifically, system 100 may be constructed, at least in part, from electronic hardware 150 (see FIG. 4) of the processor based computing device 110 operable to execute programmed algorithms, also referred to herein as software 120 or a computer program product. The processor based computing device 110 may employ one or more logic processors capable of being programmed, an example of which may include one or more microprocessors. However, other types of programmable circuitry may be used without departing from the intended scope of coverage of the embodiments of the present invention. In one embodiment, the processor based computing device 110 is operatively disposed as a microcomputer in any of various configurations including but not limited to: a laptop computer, a desktop computer, a work station, a server or the like. Alternatively, mini-computers or main frame computers may serve as the platform for implementing the system 100 for tracking and analyzing welding activity. Moreover, handheld or mobile processor based computing devices may be used to execute programmable code for tracking and analyzing performance data.

Other embodiments are contemplated wherein the system 100 is incorporated into the welding system 200. More specifically, the components comprising the system 100 may be integrated into the welding power supply 210 and/or weld torch 230. For example, the processor based computing device 110 may be received internal to the housing of the welding power supply 210 and may share a common power supply with other systems located therein. Additionally, sensors 160, 165, used to sense the weld torch 230 dynamic spatial properties, may be integrated into the weld torch handle.

The system 100 may communicate with and be used in conjunction with other similarly or dissimilarly constructed systems. Input to and output from the system 100, termed I/O, may be facilitated by networking hardware and software including wireless as well as hard wired (directly connected) network interface devices. Communication to and from the system 100 may be accomplished remotely as through a network 300 (see FIG. 3), such as, for example, a wide area network (WAN) or the Internet, or through a local area network (LAN) via network hubs, repeaters, or by any means chosen with sound engineering judgment. In this manner, information may be transmitted between systems as is useful for analyzing, and/or re-constructing and displaying performance and quality data.

In one embodiment, remote communications are used to provide virtual instruction by personnel, i.e. remote or offsite users, not located at the welding site. Reconstruction of the welding process is accomplished via networking. Data representing a particular weld may be sent to another similar or dissimilar system 100 capable of displaying the weld data (see FIG. 3). It should be noted that the transmitted data is sufficiently detailed for allowing remote user(s) to analyze the welder's performance and the resultant weld quality. Data sent to a remote system 100 may be used to generate a virtual welding environment thereby recreating the welding process as viewed by offsite users as discussed later herein. Still, any way of communicating performance data to another entity remotely located from the welding site may be used without departing from the intended scope of coverage of the embodiments of the subject invention.

The processor based computing device 110 further includes support circuitry including electronic memory devices, along with other peripheral support circuitry that facilitate operation of the one or more logic processor(s), in accordance with an embodiment of the present invention. Additionally, the processor based computing device 110 may include data storage, examples of which include hard disk drives, optical storage devices and/or flash memory for the storage and retrieval of data. Still any type of support circuitry may be used with the one or more logic processors as chosen with sound engineering judgment. Accordingly, the processor based computing device 110 may be programmable and operable to execute coded instructions in a high or low level programming language. It should be noted that any form of programming or type of programming language may be used to code algorithms as executed by the system 100.

With reference now to FIGS. 1-4, the system 100 is accessible by the end user 10 via a display screen 130 operatively connected to the processor based computing device 110. Software 120 installed onto the system 100 directs the end user's 10 interaction with the system 100 by displaying instructions and/or menu options on, for example, the display screen 130 via one or more graphical user interfaces (GUI) 135. Interaction with the system 100 includes functions relating to, for example: part set up (weld joint set up), welding activity analysis, weld activity playback, real time tracking, as well as administrative activity for managing the captured data. Still other functions may be chosen as are appropriate for use with the embodiments of the present invention. System navigation screens, i.e. menu screens, may be included to assist the end user 10 in traversing through the system functions. It is noted that as the system 100 is used for training and analysis, security may be incorporated into the GUI(s) 135 that allow restricted access to various groups of end users 10. Password security, biometrics, work card arrangement or other security measures may be used to ensure that system access is given only to authorized users as determined by an administrator or administrative user. It will be appreciated that the end user 10 may be the same or a different person than that of the administrative user.

In one embodiment, the system 100 functions to capture performance data of the end user 10 for manual activity as related to the use of tools or hand held devices. In the accompanying figures, welding, and more specifically, arc welding is illustrated as performed by the end user 10 on a weldment 15 (e.g., a weld coupon). The welding activity is recorded by the system 100 in real time or near-real time for tracking and analysis purposes mentioned above by a real time tracking module 121 and an analysis module 122, respectively (see FIG. 4). By recorded it is meant that the system 10 captures data related to a particular welding process for determining the quality of the weld joint or weld joints. The types of performance data that may be captured include, but are not limited to, for example: weld joint configuration or weld joint trajectory, weld speed, welding torch pitch and roll angles, electrode distance to the center weld joint, wire feed speed, electrode trajectory, weld time, and time and date data. Other types of data may also be captured and/or entered into the system 100 including: weldment materials, electrode materials, user name, project ID number, and the like. Still, any type and quantity of information may be captured and/or entered into the system 100 as is suitable for tracking, analyzing and managing weld performance data. In this manner, detailed information about how the welding process for a particular weld joint was performed may be captured and reconstructed for review and analysis in an analysis record 124.

The data captured and entered into the system 100 is used to determine the quality of the real world weld joint. Persons of ordinary skill in the art will understand that a weld joint may be analyzed by various processes including destructive and non-destructive methods, examples of which include sawing/cutting or x-raying of the weld joint respectively. In prior art methods such as these, trained or experienced weld personnel can determine the quality of a weld performed on a weld joint. Of course, destructive testing renders the weldment unusable and thus can only be used for a sampling or a subset of welded parts. While non-destructive testing, like x-raying, do not destroy the welded article, these methods can be cumbersome to use and the equipment expensive to purchase. Moreover, some weld joints cannot be appropriately x-rayed, i.e. completely or thoroughly x-rayed. By way of contrast, system 100 captures performance data during the welding process that can be used to determine the quality of the welded joint. More specifically, system 100 is used to identify potential discontinuities and flaws within specific regions of a weld joint. The captured data may be analyzed by an experienced welder or trained professional (e.g., a trainer 123, see FIG. 4), or in an alternative by the system 100 using the analysis module 122 for identifying areas within the weld joint that may be flawed. In one example, torch position and orientation along with travel speed and other critical parameters are analyzed as a whole to predict which areas along the weld joint, if any, are deficient. It will be understood that quality is achieved during the welding process when the operator 10 keeps the weld torch 230 within acceptable operational ranges. Accordingly, the performance data may be analyzed against known good parameters for achieving weld quality for a particular weld joint configuration.

FIG. 6 illustrates an example embodiment of a graph 600, displayed on the display 130, showing tracked welding tool pitch angle 640 versus time with respect to an upper pitch angle limit 610, a lower pitch angle limit 620, and an ideal pitch angle 630. The upper and lower limits 610 and 620 define a range of acceptability between them. Different limits may be predefined for different types of users such as, for example, welding novices, welding experts, and persons at a trade show. The analysis engine 122 may provide a scoring capability, in accordance with an embodiment of the present invention, where a numeric score is provided based on how close to optimum (ideal) a user is for a particular tracked parameter, and depending on the determined level of discontinuities or defects determined to be present in the weld.

Performance data may be stored electronically in a database 140 (see FIG. 3) and managed by a database manager in a manner suitable for indexing and retrieving selected sets or subsets of data. In one embodiment, the data is retrieved and presented to an analyzing user (e.g., a trainer 123) for determining the weld quality of a particular weld joint. The data may be presented in tabular form for analysis by the analyzing user. Pictures, graphs, and or other symbol data may also be presented as is helpful to the analyzing user in determining weld quality. In an alternative embodiment, the performance data may be presented to the analyzing user in a virtual reality setting, whereby the real world welding process is simulated using real world data as captured by the system 100. An example of such a virtual reality setting is discussed in U.S. patent application Ser. No. 12/501,257 filed on Jul. 10, 2009. In this way, the weld joint and corresponding welding process may be reconstructed for review and analysis. Accordingly, the system 100 may be used to archive real data as it relates to a particular welded article. Still, it will be construed that any manner of representing captured data or reconstructing the welding process for the analyzing user may be used as is appropriate for determining weld quality.

In another embodiment, data captured and stored in the database 140 is analyzed by an analyzing module 122 (a.k.a., an analysis engine) of the system 100. The analyzing module 122 may comprise a computer program product executed by the processor based computing device 110. The computer program product may use artificial intelligence. In one particular embodiment, an expert system may be programmed with data derived from a knowledge expert and stored within an inference engine for independently analyzing and identifying flaws within the weld joint. By independently, it is meant that the analyzing module 122 functions independently from the analyzing user to determine weld quality. The expert system may be ruled-based and/or may incorporate fuzzy logic to analyze the weld joint. In this manner, areas along the weld joint may be identified as defective, or potentially defective, and marked for subsequent review by an analyzing user. Determining weld quality and/or problem areas within the weld joint may be accomplished by heuristic methods. As the system 100 analyzes welding processes of the various end users over repeated analyzing cycles, additional knowledge may be gained by the system 100 for determining weld quality.

A neural network or networks may be incorporated into the analysis engine 122 of the system 100 for analyzing data to determine weld quality, weld efficiency and/or weld flaws or problems. Neural networks may comprise software programming that simulates decision making capabilities. In one embodiment, the neural network(s) may process data captured by the system 100 making decisions based on weighted factors. It is noted that the neural network(s) may be trained to recognize problems that may arise from the weld torch position and movement, as well as other critical welding factors. Therefore, as data from the welding process is captured and stored, the system 100 may analyze the data for identifying the quality of the weld joint. Additionally, the system 100 may provide an output device 170 (see FIG. 4) that outputs indications of potential flaws in the weld such as, for example, porosity, weld overfill, and the like.

In capturing performance data, the system 100 incorporates a series of sensors, also referred to as sensor arrays 160, 165 (see FIG. 2). The sensor arrays 160, 165 include emitters and receivers positioned at various locations in proximity to the weldment 15, and more specifically, in proximity to the weld joint 16 for determining the position and orientation of the weld torch 230 in real time (or near real time). In one embodiment, the sensor arrays 160, 165 include acoustical sensor elements. It is noted that the acoustical sensor elements may use waves in the sub-sonic and/or ultra-sonic range. Alternate embodiments are contemplated that use optical sensor elements, infrared sensor elements, laser sensor elements, magnetic sensor elements, or electromagnetic (radio frequency) sensor elements. In this manner, the sensor emitter elements emit waves of energy in any of various forms that are picked up by the sensor receiver elements. To compensate for noise introduced by the welding process, the system 100 may also include bandwidth suppressors, which may be implemented in the form of software and/or electronic circuitry. The bandwidth suppressors are used to condition the sensor signals to penetrate interference caused by the welding arc. Additionally, the system 100 may further incorporate inertial sensors, which may include one or more accelerometers. In this manner, data relating to position, orientation, velocity, and acceleration may be required to ascertain the movements (i.e., motion) of the weld torch 230.

In one embodiment, part of the sensor arrays 160, 165 are received by the weld torch 230. That is to say that a portion of the sensors or sensor elements are affixed with respect to the body of the weld torch 230 (see sensor array 160 165 of FIG. 2). In other embodiments, sensors and/or sensor elements may be affixed to a portion of the article being welded (see sensor array 165 160 of FIG. 2). Still any manner of positioning and connecting the sensor elements may be chosen as is appropriate for tracking welding activity.

As an example of sensing and tracking a welding tool 230, in accordance with an embodiment of the present invention, a magnetic sensing capability may be provided. For example, the receiver sensor array 165 may be a magnetic sensor that is mounted on the welding tool 230, and the emitter sensor array 160 may take the form of a magnetic source. The magnetic source 160 may be mounted in a predefined fixed position and orientation with respect to the weldment 15. The magnetic source 160 creates a magnetic field around itself, including the space encompassing the welding tool 230 during use and establishes a 3D spatial frame of reference. The magnetic sensor 165 is provided which is capable of sensing the magnetic field produced by the magnetic source. The magnetic sensor 165 is attached to the welding tool 230 and is operatively connected to the processor based computing device 110 via, for example, a cable, or wirelessly. The magnetic sensor 165 includes an array of three induction coils orthogonally aligned along three spatial directions. The induction coils of the magnetic sensor 165 each measure the strength of the magnetic field in each of the three directions and provide that information to the real time tracking module 121 of the processor based computing device 110. As a result, the system 100 is able to know where the welding tool 230 is in space with respect to the 3D spatial frame of reference established by the magnetic field produced by the magnetic source 160. In accordance with other embodiments of the present invention, two or more magnetic sensors may be mounted on or within the welding tool 230 to provide a more accurate representation of the position and orientation of the welding tool 230, for example. Care is to be taken in establishing the magnetic 3D spatial frame of reference such that the weldment 15, the tool 230, and any other portions of the welding environment do not substantially distort the magnetic field created by the magnetic source 160. As an alternative, such distortions may be corrected for or calibrated out as part of a welding environment set up procedure. Other non-magnetic technologies (e.g., acoustic, optical, electromagnetic, inertial, etc.) may be used, as previously discussed herein, to avoid such distortions, as are well known in the art.

With reference to all of the figures, operation of the system 100 will now be described in accordance with an embodiment of the present invention. The end user 10 activates the system 100 and enters his or her user name via the user interface 135. Once authorized access has been gained, the end user 10 traverses the menu system as prompted by the computer program product 120 via the GUI 135. The system 100 instructs the end user 10 to initiate set up of the welding article 15, which includes entering information about the weldment materials and/or welding process being used. Entering such information may include, for example, selecting a language, entering a user name, selecting a weld coupon type, selecting a welding process and associated axial spray, pulse, or short arc methods, selecting a gas type and flow rate, selecting a type of stick electrode, and selecting a type of flux cored wire.

In one embodiment, the end user enters the starting and ending points of the weld joint 16. This allows the system 100, via the real time tracking module 121, to determine when to start and stop recording the tracked information. Intermediate points are subsequently entered for interpolating the weld joint trajectory as calculated by the system 100. Additionally, sensor emitters and/or receivers 160, 165 are placed proximate to the weld joint at locations suitable for gathering data in a manner consistent with that described herein. After set up is completed, system tracking is initiated and the end user 10 is prompted to begin the welding procedure. As the end user 10 completes the weld, the system 100 gathers performance data including the speed, position and orientation of the weld torch 230 for analysis by the system 100 in determining welder performance characteristics and weld quality characteristics as previously described herein.

In summary, a system and a method for tracking and analyzing welding activity is disclosed. Dynamic spatial properties of a welding tool are sensed during a welding process producing a weld. The sensed dynamic spatial properties are tracked over time and the tracked dynamic spatial properties are captured as tracked data during the welding process. The tracked data is analyzed to determine performance characteristics of a welder performing the welding process and quality characteristics of a weld produced by the welding process. The performance characteristics and the quality characteristics may be subsequently reviewed.

While the claimed subject matter of the present application has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the claimed subject matter. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the claimed subject matter without departing from its scope. Therefore, it is intended that the claimed subject matter not be limited to the particular embodiment disclosed, but that the claimed subject matter will include all embodiments falling within the scope of the appended claims.

Claims

1. A system for tracking and analyzing welding activity, said system comprising:

means for automatically sensing dynamic spatial properties of a welding tool during a welding process producing a weld;
means for automatically tracking said sensed dynamic spatial properties over time during said welding process;
means for automatically capturing said tracked dynamic spatial properties as tracked data during said welding process; and
means for automatically analyzing said tracked data to determine at least one of performance characteristics of a welder performing said welding process and quality characteristics of a weld produced by said welding process.

2. The system of claim 1 further comprising means for reviewing said performance characteristics of a welder performing said welding process.

3. The system of claim 1 further comprising means for reviewing said quality characteristics of a weld produced by said welding process.

4. The system of claim 1 further comprising means for a user to locally interact with said system.

5. The system of claim 1 further comprising means for a user to remotely interact with said system.

6. The system of claim 1 further comprising means for automatically authorizing access to a user of said system.

7. The system of claim 1 wherein said performance characteristics of a welder include at least one of a weld joint trajectory, a travel speed of said welding tool, welding tool pitch and roll angles, an electrode distance to a center weld joint, an electrode trajectory, and a weld time.

8. The system of claim 1 wherein said quality characteristics of a weld produced by said welding process include at least one of discontinuities and flaws within regions of a weld produced by said welding process.

9. A system for tracking and analyzing welding activity, said system comprising:

at least one sensor array configured to sense dynamic spatial properties of a welding tool during a welding process producing a weld;
a processor based computing device operatively interfacing to said at least one sensor array and configured to track and analyze said dynamic spatial properties of a welding tool over time during a welding process producing a weld; and
at least one user interface operatively interfacing to said processor based computing device.

10. The system of claim 9 wherein said at least one user interface includes a graphical user interface.

11. The system of claim 9 wherein said at least one user interface includes a display device.

12. The system of claim 9 further comprising a network interface configured to interface said processor based computing device to an external communication network.

13. The system of claim 9 wherein said at least one sensor array includes at least one of acoustical sensor elements, optical sensor elements, magnetic sensor elements, and electromagnetic sensor elements.

14. A method for tracking and analyzing welding activity, said method comprising:

sensing dynamic spatial properties of a welding tool during a welding process producing a weld using at least one sensor;
tracking said sensed dynamic spatial properties over time during said welding process using a real time tracking module;
capturing said tracked dynamic spatial properties as tracked data during said welding process using a computer based memory device; and
analyzing said tracked data to determine at least one of performance characteristics of a welder performing said welding process and quality characteristics of a weld produced by said welding process using a computer based analysis engine.

15. The method of claim 14 further comprising outputting said performance characteristics of a welder performing said welding process to at least one of a display device, a visualization module, and a testing module for review.

16. The method of claim 14 further comprising outputting said quality characteristics of a weld produced by said welding process to at least one of a display device, a visualization module, and a testing module for review.

17. The method of claim 14 further comprising selecting welding set up parameters for said welding process via a user interface.

18. The method of claim 14 further comprising remotely reviewing at least one of said performance characteristics of a welder performing said welding process and said quality characteristics of a weld produced by said welding process, via a communication network.

19. The method of claim 14 wherein said performance characteristics of a welder include at least one of a weld joint trajectory, a travel speed of said welding tool, welding tool pitch and roll angles, an electrode distance to a center weld joint, an electrode trajectory, and a weld time.

20. The method of claim 14 wherein said quality characteristics of a weld produced by said welding process include at least one of discontinuities and flaws within regions of a weld produced by said welding process.

21. A system for tracking welding activity, said system comprising:

an optical tracking system comprising an optical sensor and configured to track at least one of a position, a movement, and an orientation of a welding tool during a manual welding process producing a real world weld;
a processor based computing system operatively connected to said optical tracking system and configured to analyze data related to said welding process and determine an area along the real world weld that is defective or potentially defective based on said at least one of a position, a movement, and an orientation of said welding tool, said processor based computing system identifying said area for subsequent review by a user; and
a graphical display to display information related to weld quality,
wherein said processor based computing system is configured to record information related to a welder's identity and performance, and
wherein said processor based computing system is configured to record information corresponding to said at least one of a position, a movement, and an orientation of said welding tool in an analysis record.

22. A system for tracking welding activity, said system comprising:

at least one optical sensor array configured to sense spatial properties of a welding tool during a manual welding process producing a real world weld;
a computer operatively connected to said at least one sensor array to determine at least one parameter of said welding tool, said computer configured to analyze data related to said welding process and determine an area along the real world weld that is defective based on said sensed spatial properties, said computer identifying said area for subsequent review by a user; and
a user interface to display information related to weld quality,
wherein said computer is configured to record information related to a welder's identity and performance, and
wherein said computer is configured to record information corresponding to said at least one parameter in an analysis record.

23. A system for tracking welding activity, said system comprising:

an optical tracking system comprising an optical sensor and configured to track at least one of a position, a movement, and an orientation of a welding tool during a manual welding process producing a real world weld;
a computer operatively connected to said optical tracking system, said computer configured to determine at least one parameter of said welding tool; and
a graphical display to display a graph having information related to weld quality,
wherein said computer is configured to record information related to a welder's performance based on at least said at least one parameter, said computer configured to analyze data related to said welding process and determine an area along the real world weld that is potentially defective based on said at least one of a position, a movement, and an orientation of said welding tool, said computer identifying said area for subsequent review by a user,
wherein said computer records information related to at least one of weldment materials, electrode materials, user name, and project ID number, and
wherein said computer records information corresponding to said at least one parameter in an analysis record.

24. A system for tracking and analyzing welding activity, said system comprising:

a welding tool configured to create a real world weld during a manual welding operation;
an optical sensor array comprising at least one optical sensor, said optical sensor array configured to sense spatial properties of said welding tool during said welding operation;
a processor based computing device operatively connected to said sensor array and configured to receive information from said sensor array related to said sensed spatial properties, said processor based computing device configured to analyze data related to said welding process, said analyzing including determining a plurality of performance parameters for said welding operation, and where said processor based computing device determines at least one location of said real world weld which contains a defect based on said sensed spatial properties, said processor based computing device identifying said at least one location for subsequent review by a user; and
a user interface operatively connected to said processor based computing device, said user interface configured to graphically display said plurality of performance parameters and display information relating to said at least one location.

25. The system of claim 24, wherein said determination of said defect is based on heuristic methods.

26. The system of claim 24 wherein said plurality of performance parameters includes at least one of a weld speed, welding tool pitch angle, welding tool roll angle, and an electrode distance to a center weld joint.

27. The system of claim 24, wherein said spatial properties comprise at least one of a position, an orientation, and a movement of said welding tool.

28. The system of claim 24, wherein said welding tool comprises a portion of said optical sensor array.

29. The system of claim 24, wherein said processor based computing device is configured to record information related to a welder's performance.

30. The system of claim 29, wherein said information includes information corresponding to said welder's identity.

31. The system of claim 24, wherein said analyzing further includes comparing at least one of said plurality of performance parameters to known good parameters for a particular weld joint configuration.

32. The system of claim 24, wherein said determining of said defect is performed by an expert system.

33. The system of claim 32, wherein said expert system is a rule-based system.

Referenced Cited
U.S. Patent Documents
317063 May 1885 Wittenstrom
428459 May 1890 Oopfin
483428 September 1892 Goppin
1159119 November 1915 Springer
D140630 March 1945 Garibay
D142377 September 1945 Dunn
D152049 December 1948 Welch
2681969 June 1954 Burke
D174208 March 1955 Abidgaard
2728838 December 1955 Barnes
D176942 February 1956 Cross
2894086 July 1959 Rizer
3035155 May 1962 Hawk
3059519 October 1962 Stanton
3356823 December 1967 Waters
3555239 January 1971 Kerth
3621177 November 1971 McPherson et al.
3654421 April 1972 Streetman et al.
3739140 June 1973 Rotilio
3866011 February 1975 Cole et al.
3867769 February 1975 Schow et al.
3904845 September 1975 Minkiewicz et al.
3988913 November 2, 1976 Metcalfe et al.
D243459 February 22, 1977 Bliss
4024371 May 17, 1977 Drake
4041615 August 16, 1977 Whitehill
D247421 March 7, 1978 Driscoll
4124944 November 14, 1978 Blair
4132014 January 2, 1979 Schow
4237365 December 2, 1980 Lambros et al.
4280041 July 21, 1981 Kiessling et al.
4280042 July 21, 1981 Berger
4280137 July 21, 1981 Ashida et al.
4314125 February 2, 1982 Nakamura
4354087 October 12, 1982 Osterlitz
4359622 November 16, 1982 Dostoomian et al.
4375026 February 22, 1983 Kearney
4410787 October 18, 1983 Kremers
4429266 January 31, 1984 Traadt
4452589 June 5, 1984 Denison
D275292 August 28, 1984 Bouman
D277761 February 26, 1985 Korovin et al.
4525619 June 25, 1985 Ide et al.
D280329 August 27, 1985 Bouman
4611111 September 9, 1986 Baheti et al.
4616326 October 7, 1986 Meier et al.
4629860 December 16, 1986 Linbom
4677277 June 30, 1987 Cook et al.
4680014 July 14, 1987 Paton et al.
4689021 August 25, 1987 Vasiliev et al.
4707582 November 17, 1987 Beyer
4716273 December 29, 1987 Paton et al.
D297704 September 20, 1988 Bulow
4867685 September 19, 1989 Brush et al.
4877940 October 31, 1989 Bangs et al.
4897521 January 30, 1990 Burr
4907973 March 13, 1990 Hon
4931018 June 5, 1990 Herbst et al.
4973814 November 27, 1990 Kojima et al.
4998050 March 5, 1991 Nishiyama et al.
5034593 July 23, 1991 Rice et al.
5061841 October 29, 1991 Richardson
5089914 February 18, 1992 Prescott
5192845 March 9, 1993 Kirmsse et al.
5206472 April 27, 1993 Myking et al.
5266930 November 30, 1993 Ichikawa et al.
5285916 February 15, 1994 Ross
5305183 April 19, 1994 Teynor
5320538 June 14, 1994 Baum
5337611 August 16, 1994 Fleming et al.
5360156 November 1, 1994 Ishizaka et al.
5360960 November 1, 1994 Shirk
5370071 December 6, 1994 Ackermann
D359296 June 13, 1995 Witherspoon
5424634 June 13, 1995 Goldfarb et al.
5436638 July 25, 1995 Bolas et al.
5464957 November 7, 1995 Kidwell
D365583 December 26, 1995 Viken
5562843 October 8, 1996 Yasumoto
5662822 September 2, 1997 Tada
5670071 September 23, 1997 Ueyama et al.
5676503 October 14, 1997 Lang
5676867 October 14, 1997 Allen
5708253 January 13, 1998 Bloch et al.
5710405 January 20, 1998 Solomon et al.
5719369 February 17, 1998 White et al.
D392534 March 24, 1998 Degen
5728991 March 17, 1998 Takada et al.
5751258 May 12, 1998 Fergason et al.
D395296 June 16, 1998 Kaye et al.
D396238 July 21, 1998 Schmitt
5781258 July 14, 1998 Debral et al.
5823785 October 20, 1998 Matherne, Jr.
5835077 November 10, 1998 Dao et al.
5835277 November 10, 1998 Hegg
5845053 December 1, 1998 Watanabe
5877777 March 2, 1999 Colwell
5963891 October 5, 1999 Walker et al.
6008470 December 28, 1999 Zhang
6037948 March 14, 2000 Liepa
6049059 April 11, 2000 Kim
6051805 April 18, 2000 Vaidya et al.
6114645 September 5, 2000 Burgess
6155475 December 5, 2000 Ekelof et al.
6155928 December 5, 2000 Burdick
6179619 January 30, 2001 Tanaka
6230327 May 15, 2001 Briand et al.
6236013 May 22, 2001 Delzenne
6236017 May 22, 2001 Smartt et al.
6242711 June 5, 2001 Cooper
6271500 August 7, 2001 Hirayam et al.
6330938 December 18, 2001 Herve et al.
6330966 December 18, 2001 Eissfeller
6331848 December 18, 2001 Stove et al.
D456428 April 30, 2002 Aronson et al.
6373465 April 16, 2002 Jolly et al.
D456828 May 7, 2002 Aronson et al.
D461383 August 13, 2002 Blackburn
6441342 August 27, 2002 Hsu
6445964 September 3, 2002 White et al.
6492618 December 10, 2002 Flood et al.
6506997 January 14, 2003 Matsuyama
6552303 April 22, 2003 Blankenship et al.
6560029 May 6, 2003 Dobbie et al.
6563489 May 13, 2003 Latypov et al.
6568846 May 27, 2003 Cote et al.
D475726 June 10, 2003 Suga et al.
6572379 June 3, 2003 Sears et al.
6583386 June 24, 2003 Ivkovich
6621049 September 16, 2003 Suzuki
6624388 September 23, 2003 Blankenship et al.
D482171 November 11, 2003 Vui et al.
6647288 November 11, 2003 Madill et al.
6649858 November 18, 2003 Wakeman
6655645 December 2, 2003 Lu et al.
6660965 December 9, 2003 Simpson
6697701 February 24, 2004 Hillen et al.
6697770 February 24, 2004 Nagetgall
6703585 March 9, 2004 Suzuki
6708385 March 23, 2004 Lemelson
6710298 March 23, 2004 Eriksson
6710299 March 23, 2004 Blankenship et al.
6715502 April 6, 2004 Rome et al.
D490347 May 25, 2004 Meyers
6730875 May 4, 2004 Hsu
6734393 May 11, 2004 Friedl et al.
6744011 June 1, 2004 Hu et al.
6750428 June 15, 2004 Okamoto et al.
6765584 July 20, 2004 Wloka
6772802 August 10, 2004 Few
6788442 September 7, 2004 Potin et al.
6795778 September 21, 2004 Dodge et al.
6798974 September 28, 2004 Nakano et al.
6857553 February 22, 2005 Hartman et al.
6858817 February 22, 2005 Blankenship et al.
6865926 March 15, 2005 O'Brien et al.
D504449 April 26, 2005 Butchko
6920371 July 19, 2005 Hillen et al.
6940037 September 6, 2005 Kovacevic et al.
6940039 September 6, 2005 Blankepship et al.
7021937 April 4, 2006 Simpson et al.
7024342 April 4, 2006 Waite
7126078 October 24, 2006 Demers et al.
7132617 November 7, 2006 Lee et al.
7170032 January 30, 2007 Flood
7194447 March 20, 2007 Harvey et al.
7247814 July 24, 2007 Ott
D555446 November 20, 2007 Ibarrondo et al.
7315241 January 1, 2008 Daily et al.
D561973 February 12, 2008 Kinsley et al.
7353715 April 8, 2008 Myers
7363137 April 22, 2008 Brant et al.
7375304 May 20, 2008 Kainec et al.
7381923 June 3, 2008 Gordon et al.
7414595 August 19, 2008 Muffler
7465230 December 16, 2008 LeMay et al.
7478108 January 13, 2009 Townsend et al.
D587975 March 10, 2009 Aronson et al.
7516022 April 7, 2009 Lee et al.
7580821 August 25, 2009 Schirm
D602057 October 13, 2009 Osicki
7621171 November 24, 2009 O'Brien
D606102 December 15, 2009 Bender et al.
7643890 January 5, 2010 Hillen et al.
7687741 March 30, 2010 Kainec et al.
D614217 April 20, 2010 Peters et al.
D615573 May 11, 2010 Peters et al.
7817162 October 19, 2010 Bolick et al.
7853645 December 14, 2010 Brown
D631074 January 18, 2011 Peters et al.
7874921 January 25, 2011 Baszucki et al.
7970172 June 28, 2011 Hendrickson
7972129 July 5, 2011 O'Donoghue
7991587 August 2, 2011 Ihn
8069017 November 29, 2011 Hallquist
8224881 July 17, 2012 Spear et al.
8248324 August 21, 2012 Nangle
8265886 September 11, 2012 Bisiaux et al.
8274013 September 25, 2012 Wallace
8287522 October 16, 2012 Moses et al.
8316462 November 27, 2012 Becker et al.
8363048 January 29, 2013 Gering
8365603 February 5, 2013 Lesage et al.
8512043 August 20, 2013 Choquet
8569646 October 29, 2013 Daniel et al.
8657605 February 25, 2014 Wallace
8680434 March 25, 2014 Stoger
8747116 June 10, 2014 Zboray
8777629 July 15, 2014 Kreindl et al.
RE45062 August 5, 2014 Maguire, Jr.
8851896 October 7, 2014 Wallace
8860760 October 14, 2014 Chen
RE45398 March 3, 2015 Wallace
8992226 March 31, 2015 Leach
9011154 April 21, 2015 Kindig
9293056 March 22, 2016 Zboray
9293057 March 22, 2016 Zboray
9318026 April 19, 2016 Peters
9323056 April 26, 2016 Williams
9761153 September 12, 2017 Zboray et al.
9767712 September 19, 2017 Postlethwaite
9779636 October 3, 2017 Zboray et al.
9818312 November 14, 2017 Zboray et al.
9836994 December 5, 2017 Kinding et al.
9911359 March 6, 2018 Wallace
9911360 March 6, 2018 Wallace
9928755 March 27, 2018 Wallace et al.
20010045808 November 29, 2001 Hietmann et al.
20010052893 December 20, 2001 Jolly et al.
20020032553 March 14, 2002 Simpson et al.
20020046999 April 25, 2002 Veikkolainen
20020050984 May 2, 2002 Roberts
20020085843 July 4, 2002 Mann
20020175897 November 28, 2002 Pelosi
20030000931 January 2, 2003 Ueda
20030023592 January 30, 2003 Modica et al.
20030025884 February 6, 2003 Hamana et al.
20030069866 April 10, 2003 Ohno
20030075534 April 24, 2003 Okamoto et al.
20030106787 June 12, 2003 Santilli
20030111451 June 19, 2003 Blankenship et al.
20030172032 September 11, 2003 Choquet
20030186199 October 2, 2003 McCool
20030234885 December 25, 2003 Pilu
20040020907 February 5, 2004 Zauner et al.
20040035990 February 26, 2004 Ackeret
20040050824 March 18, 2004 Samler
20040088071 May 6, 2004 Kouno
20040140301 July 22, 2004 Blankenship et al.
20040181382 September 16, 2004 Hu et al.
20040217096 November 4, 2004 Lipnevicius
20050007504 January 13, 2005 Fergason
20050017152 January 27, 2005 Fergason
20050029326 February 10, 2005 Henrikson
20050046584 March 3, 2005 Breed
20050050168 March 3, 2005 Wen et al.
20050101767 May 12, 2005 Clapham et al.
20050103766 May 19, 2005 Iizuka et al.
20050103767 May 19, 2005 Kainec et al.
20050109735 May 26, 2005 Flood
20050128186 June 16, 2005 Shahoian et al.
20050133488 June 23, 2005 Blankenship et al.
20050159840 July 21, 2005 Lin et al.
20050163364 July 28, 2005 Beck et al.
20050189336 September 1, 2005 Ku
20050199602 September 15, 2005 Kaddani et al.
20050230573 October 20, 2005 Ligertwood
20050252897 November 17, 2005 Hsu
20050275913 December 15, 2005 Vesely et al.
20050275914 December 15, 2005 Vesely et al.
20060014130 January 19, 2006 Weinstein
20060076321 April 13, 2006 Maev et al.
20060136183 June 22, 2006 Choquet
20060142656 June 29, 2006 Malackowski et al.
20060154226 July 13, 2006 Maxfield
20060163227 July 27, 2006 Hillen et al.
20060163228 July 27, 2006 Daniel
20060166174 July 27, 2006 Rowe et al.
20060169682 August 3, 2006 Kainec et al.
20060173619 August 3, 2006 Brant et al.
20060189260 August 24, 2006 Sung
20060207980 September 21, 2006 Jacovetty et al.
20060213892 September 28, 2006 Ott
20060214924 September 28, 2006 Kawamoto et al.
20060226137 October 12, 2006 Huismann et al.
20060252543 November 9, 2006 Van Noland et al.
20060258447 November 16, 2006 Baszucki et al.
20070034611 February 15, 2007 Drius et al.
20070038400 February 15, 2007 Lee et al.
20070045488 March 1, 2007 Shin
20070088536 April 19, 2007 Ishikawa
20070112889 May 17, 2007 Cook et al.
20070198117 August 23, 2007 Wajihuddin
20070209586 September 13, 2007 Ebensberger et al.
20070211026 September 13, 2007 Ohta
20070221797 September 27, 2007 Thompson et al.
20070256503 November 8, 2007 Wong et al.
20070277611 December 6, 2007 Portzgen et al.
20070291035 December 20, 2007 Vesely et al.
20080021311 January 24, 2008 Goldbach
20080031774 February 7, 2008 Magnant et al.
20080038702 February 14, 2008 Choquet
20080061113 March 13, 2008 Seki et al.
20080078811 April 3, 2008 Hillen et al.
20080078812 April 3, 2008 Peters et al.
20080117203 May 22, 2008 Gering
20080120075 May 22, 2008 Wloka
20080128398 June 5, 2008 Schneider
20080135533 June 12, 2008 Ertmer et al.
20080140815 June 12, 2008 Brant et al.
20080149686 June 26, 2008 Daniel et al.
20080203075 August 28, 2008 Feldhausen et al.
20080233550 September 25, 2008 Solomon
20080303197 December 11, 2008 Paquette et al.
20080314887 December 25, 2008 Stoger
20090015585 January 15, 2009 Klusza
20090021514 January 22, 2009 Klusza
20090045183 February 19, 2009 Artelsmair et al.
20090050612 February 26, 2009 Serruys
20090057286 March 5, 2009 Ihara
20090139968 June 4, 2009 Hesse
20090152251 June 18, 2009 Dantinne et al.
20090173726 July 9, 2009 Davidson et al.
20090184098 July 23, 2009 Daniel et al.
20090200281 August 13, 2009 Hampton
20090200282 August 13, 2009 Hampton
20090231423 September 17, 2009 Becker
20090259444 October 15, 2009 Dolansky et al.
20090298024 December 3, 2009 Batzler et al.
20090325699 December 31, 2009 Delgiannidis
20100012017 January 21, 2010 Miller
20100012637 January 21, 2010 Jaeger
20100048273 February 25, 2010 Wallace et al.
20100062405 March 11, 2010 Zboray et al.
20100062406 March 11, 2010 Zboray et al.
20100096373 April 22, 2010 Hillen et al.
20100121472 May 13, 2010 Babu et al.
20100133247 June 3, 2010 Mazumder et al.
20100133250 June 3, 2010 Sardy et al.
20100176107 July 15, 2010 Bong
20100201803 August 12, 2010 Melikian
20100224610 September 9, 2010 Wallace
20100276396 November 4, 2010 Cooper et al.
20100299101 November 25, 2010 Shimada et al.
20100307249 December 9, 2010 Lesage et al.
20110006047 January 13, 2011 Penrod et al.
20110060568 March 10, 2011 Goldfine et al.
20110091846 April 21, 2011 Kreindl et al.
20110114615 May 19, 2011 Daniel et al.
20110116076 May 19, 2011 Chanty et al.
20110117527 May 19, 2011 Conrardy et al.
20110122495 May 26, 2011 Togashi
20110183304 July 28, 2011 Wallace
20110187746 August 4, 2011 Suto
20110248864 October 13, 2011 Becker et al.
20110290765 December 1, 2011 Albrecht et al.
20110316516 December 29, 2011 Schiefermuller et al.
20120122062 May 17, 2012 Yang
20120189993 July 26, 2012 Kindig
20120291172 November 22, 2012 Wills et al.
20120298640 November 29, 2012 Conrardy et al.
20130026150 January 31, 2013 Chanty et al.
20130040270 February 14, 2013 Albrecht
20130049976 February 28, 2013 Maggiore
20130075380 March 28, 2013 Albrech et al.
20130182070 July 18, 2013 Peters et al.
20130183645 July 18, 2013 Wallace et al.
20130189657 July 25, 2013 Wallace et al.
20130189658 July 25, 2013 Peters et al.
20130209976 August 15, 2013 Postlethwaite et al.
20130230832 September 5, 2013 Peters et al.
20130231980 September 5, 2013 Elgart
20130327747 December 12, 2013 Dantinne
20140017642 January 16, 2014 Postelthwaite et al.
20140038143 February 6, 2014 Daniel et al.
20140065584 March 6, 2014 Wallace et al.
20140134579 May 15, 2014 Becker
20140134580 May 15, 2014 Becker
20140220522 August 7, 2014 Peters
20140263224 September 18, 2014 Becker
20140272835 September 18, 2014 Becker
20140272836 September 18, 2014 Becker
20140272837 September 18, 2014 Becker
20140272838 September 18, 2014 Becker
20140312020 October 23, 2014 Daniel
20140315167 October 23, 2014 Kreindl et al.
20140322684 October 30, 2014 Wallace et al.
20140346158 November 27, 2014 Matthews
20150056584 February 26, 2015 Boulware
20150056585 February 26, 2015 Boulware et al.
20150056586 February 26, 2015 Penrod et al.
20160125763 May 5, 2016 Becker
20160260261 September 8, 2016 Hsu
Foreign Patent Documents
2698078 September 2011 CA
101193723 June 2008 CN
10214178 July 2008 CN
101209512 July 2008 CN
201083660 July 2008 CN
101419755 April 2009 CN
101419755 April 2009 CN
201229711 April 2009 CN
101571887 November 2009 CN
101587659 November 2009 CN
101587659 November 2009 CN
102165504 August 2011 CN
102171744 August 2011 CN
202684308 January 2013 CN
103871279 June 2014 CN
102014819 July 2014 CN
107316544 November 2017 CN
2833638 February 1980 DE
3046634 January 1984 DE
3244307 May 1984 DE
3522581 January 1987 DE
4037879 June 1991 DE
19615069 October 1997 DE
19739720 October 1998 DE
19834205 February 2000 DE
20009543 August 2001 DE
102005047204 April 2007 DE
102010038902 September 2012 DE
202012013151 February 2015 DE
108599 May 1984 EP
127299 December 1984 EP
145891 June 1985 EP
319623 June 1989 EP
0852986 July 1998 EP
1527852 May 2005 EP
1905533 April 2008 EP
2274736 May 2007 ES
2274736 March 2008 ES
1456780 March 1965 FR
2827066 January 2003 FR
2926660 July 2009 FR
1455972 November 1976 GB
1511608 May 1978 GB
2254172 September 1992 GB
2435838 September 2007 GB
2454232 June 2009 GB
478719 October 1972 JP
5098035 August 1975 JP
2224877 September 1990 JP
5329645 December 1993 JP
7047471 February 1995 JP
7232270 September 1995 JP
8132274 May 1996 JP
8150476 June 1996 JP
8505091 June 1996 JP
11104833 April 1999 JP
2000167666 June 2000 JP
2000237872 September 2000 JP
2001071140 March 2001 JP
2002278670 September 2002 JP
2002366021 December 2002 JP
2003200372 July 2003 JP
2003271048 September 2003 JP
2003326362 November 2003 JP
2006006604 January 2006 JP
2006175205 July 2006 JP
2006281270 October 2006 JP
2007290025 November 2007 JP
2009500178 January 2009 JP
2009160636 July 2009 JP
2010231792 October 2010 JP
2011528283 November 2011 JP
2012024867 February 2012 JP
2013091086 May 2013 JP
100876425 December 2008 KR
20090010693 January 2009 KR
20090010693 January 2009 KR
1020110068544 June 2011 KR
2008108601 November 2009 RU
1038963 August 1983 SU
WO9845078 October 1998 WO
0112376 February 2001 WO
001009867 February 2001 WO
WO0143910 June 2001 WO
0158400 August 2001 WO
2005102230 November 2005 WO
2006034571 April 2006 WO
WO2006034571 April 2006 WO
WO2007009131 January 2007 WO
WO2007039278 April 2007 WO
2009120921 January 2009 WO
WO2009060231 May 2009 WO
WO2009149740 December 2009 WO
WO2010000003 January 2010 WO
2010020867 February 2010 WO
2010020870 February 2010 WO
2010044982 April 2010 WO
WO2010091493 August 2010 WO
2011045654 April 2011 WO
2011058433 May 2011 WO
WO2011067447 June 2011 WO
2011097035 August 2011 WO
2011148258 December 2011 WO
2012082105 June 2012 WO
2012137060 October 2012 WO
WO2012143327 October 2012 WO
2013008235 January 2013 WO
WO2013014202 January 2013 WO
2013025672 February 2013 WO
2013061518 May 2013 WO
2013114189 August 2013 WO
2013175079 November 2013 WO
2014007830 January 2014 WO
2014019045 February 2014 WO
2014020386 February 2014 WO
2014140722 September 2014 WO
2016137578 January 2016 WO
2014140721 September 2017 WO
Other references
  • International Search Report and Written Opinion from PCT/IB09/000605 dated Feb. 12, 2010.
  • International Search Report and Written Opinion from PCT/IB10/02913 dated Apr. 19, 2011.
  • ASME Definitions, Consumables, Welding Positions, dated Mar. 19, 2001. See http://www.gowelding.com/wp/asme4.htm.
  • Abbas, M., et al.; Code_Aster; Introduction to Code_Aster; User Manual; Booket U1.0-: Introduction to Code_Aster; Document: U1.02.00; Version 7.4; Jul. 22, 2005.
  • Bjorn G. Agren; Sensor Integration for Robotic Arc Welding; 1995; vol. 5604C of Dissertations Abstracts International p. 1123; Dissertation Abs Online (Dialog® File 35): © 2012 ProQuest Info& Learning: http://dialogweb.com/cgi/dwclient?req=1331233317524; one (1) p.; printed Mar. 8, 2012.
  • Abid, et al., “Numerical simulation to study the effect of tack welds and root gap on welding deformations and residual stresses of a pipe-flange joint” by M. Abid and M. Siddique, Faculty of Mechanical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, NWFP, Pakistan. Available on-line Aug. 25, 2005.
  • “Penetration in Spot GTA Welds during Centrifugation,”D.K. Aidun and S.A. Martin; Journal of Materials Engineering and Performance vol. 7(5) Oct. 1998—597.
  • Arc+ simulator; http://www.123arc.com/en/depliant_ang.pdf; 2000, 2 pgs.
  • ARS Electronica Linz GmbH, Fronius, 2 pages, May 18, 1997.
  • Asciencetutor.com, A division of Advanced Science and Automation Corp., VWL (Virtual Welding Lab), 2 pages, 2007.
  • 16TH International Shop and Offshore Structures Congress: Aug. 20-25, 2006: Southhampton, UK, vol. 2 Specialist Committee V.3 Fabrication Technology Committee Mandate: T Borzecki, G. Bruce, Y.S. Han, M. Heinemann, A Imakita, L. Josefson, W. Nie, D. Olson, F. Roland, and Y. Takeda.
  • CS Wave, A Virtual learning tool for welding motion, 10 pages, Mar. 14, 2008.
  • Choquet, Claude; “ARC+: Today's Virtual Reality Solution for Welders” Internet Page, Jan. 1, 2008.
  • Code Aster (Software) EDF (France), Oct. 2001.
  • Cooperative Research Program, Virtual Reality Welder Training, Summary Report SR 0512, 4 pages, Jul. 2005.
  • Desroches, X.; Code-Aster, Note of use for aclculations of welding; Instruction manual U2.03 booklet: Thermomechanical; Document: U2.03.05; Oct. 1, 2003.
  • Edison Welding Institute, E-Weld Predictor, 3 pages, 2008.
  • Eduwelding+, Weld Into the Fugure; Online Welding Seminar—A virtual training environment; 123arc.com; 4 pages, 2005.
  • Eduwelding+, Training Activities with arc+ simulator; Weld Into The Future, Online Welding Simulator—A virtual training environment; 123arc.com; 6 pages, May 2008.
  • FH Joanneum, Fronius—virtual welding, 2 pages, May 12, 2008.
  • The Fabricator, Virtual Welding, 4 pages, Mar. 2008.
  • Fast, K. et al., “Virtual Training for Welding”, Mixed and Augmented Realtity, 2004, ISMAR 2004, Third IEEE and CM International Symposium on Arlington, VA, Nov. 2-5, 2004.
  • Garcia-Ellende et al., “Defect Detection in Arc-Welding Processes by Means of the Line-to-Continuum Method and Feature Selection”, www.mdpi.com/journal/sensors; Sensors 2009, 9, 7753-7770; doi; 10.3390/s91007753.
  • Juan Vicenete Rosell Gonzales, “RV-Sold: simulator virtual para la formacion de soldadores”; Deformacion Metalica, Es. vol. 34, No. 301 Jan. 1, 2008.
  • Hillis and Steele, Jr.; “Data Parallel Algorithms”, Communications of the ACM, Dec. 1986, vol. 29, No. 12, p. 1170.
  • “The influence of fluid flow phenomena on the laser beam welding process”; International Journal of Heat and Fluid Flow 23, dated 2002.
  • The Lincoln Electric Company, CheckPoint Production Monitoring brochure; four pages; http://www.lincolnelectric.com/assets/en_US/products/literature/s232.pdf; Publication S2.32; issue date Feb. 2012.
  • The Lincoln Electric Company, Production Monitoring brochure, 4 pages, May 2009.
  • Eric Linholm, John Nickolls, Stuart Oberman, and John Montrym, “Nvidia Testla: A Unifired Graphics and Computing Architecture”, IEEE Computer Society, 2008.
  • Mahrle, A., et al.; “The influence of fluid flow phenomena on the laser beam welding process” International Journal of Heat and Fluid Flow 23 (2002, No. 3, pp. 288.
  • Mavrikios D et al, A prototype virtual reality-based demonstrator for immersive and interactive simulation of welding processes, International Journal of Computer Integrated manufacturing, Taylor and Francis, Basingstoke, GB, vol. 19, No. 3, Apr. 1, 2006, pp. 294-300.
  • Mechanisms and Mechanical Devices Source Book, Chironis, Neil Sclater; McGraw Hill; 2nd Addition, 1996.
  • Miller Electric Mgf Co.; MIG Welding System features weld monitoring software; NewsRoom 2010 (Dialog® File 992); © 2011 Dialog. 2010; http://www.dialogweb.com/cgi/dwclient?reg=1331233430487; three (3) pages; printed Mar. 8, 2012.
  • NSRP ASE, Low-Cost Virtual Reality Welder Training System, 1 Page, 2008.
  • N. A. Tech., P/NA.3 Process Modeling and Optimization, 11 pages, Jun. 4, 2008.
  • Virtual Reality Welder Trainer, Sessiion 5: Joining Technologies for Naval Applications: earliest date Jul. 14, 2006 (http://weayback.archive.org) by Nancy C. Porter, Edision Welding Institute; J. Allan Cote, General Dynamics Electric Boat; Timothy D. Gifford, VRSim, and Wim Lam, FCS Controls.
  • Porter, et al., Virtual Reality Training, Paper No. 2005-P19, 14 pages, 2005.
  • Production Monitoring 2 brochure, four pages, The Lincoln Electric Company, May 2009.
  • Ratnam and Khalid: “Automatic classification of weld defects using simulated data and an MLP neutral network.” Insight vol. 49, No. 3; Mar. 2007.
  • Russel and Norvig, “Artificial Intelligence: A Modern Approach”, Prentice-Hall (Copyright 1995).
  • “Design and Implementation of a Video Sensor for Closed Loop Control of Back Bead Weld Puddle Width,” Robert Schoder, Massachusetts Institute of Technology, Dept. of Mechanical Engineering, May 27, 1983.
  • http://www.sciencedirect.com/science/article/pil/S009457650000151X.
  • Sim Welder, retrieved on Apr. 12, 2010 from: http://www.simwelder.com.
  • SIMFOR / CESOL, “RV-Sold” Welding Simulator, Technical and Functional Features, 20 page, no. date available.
  • Training in a virtual environment gives welding students a leg up, retrieved on Apr. 12, 2010 from: http://www.thefabricator.com/article/arcwelding/virtually-welding.
  • Wade, “Human uses of ultrasound: ancient and modern”, Ultrasonics vol. 38, dated 2000.
  • Wang et al., “Numerical Analysis of Metal Tranfser in Gas Metal Arc Welding,” G. Wang, P.G. Huang, and Y.M. Zhang. Departements of Mechanical and Electrical Engineering. University of Kentucky, Dec. 10, 2001.
  • Wang et al., Study on welder training by means of haptic guidance and virtual reality for arc welding, 2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006 ISBN-10: 1424405718, p. 954-958.
  • White et al., Virtual welder training, 2009 IEEE Virtual Reality Conference, p. 303, 2009.
  • Edison Welding Institue, Inc. And Realweld Systems, Inc. -v- Lincoln Global, Inc.; Complaint for Declaratory Judgement including Exhibits; Civil Action No. 2:12-cv-1040.
  • Edison Welding Institue, Inc. and Realweld Systems, Inc. -v- Lincoln Global, Inc.; Stipulated Extension of Time to Answer . . . Civil Action No. 2:12-cv-1040.
  • Edison Welding Institue, Inc. and Realweld Systems, Inc. -v- Lincoln Global, Inc.; Corporate Disclosure Statement; Civil Action No. 2:12-cv-1040.
  • Edison Welding Institue, Inc. and Realweld Systems, Inc. -v- Lincoln Global, Inc.; Notice of Appearance of Counsel; Civil Action No. 2:12-cv-1040.
  • Edison Welding Institue, Inc. and Realweld Systems, Inc. -v- Lincoln Global,lnc.; Unopposed Motion to Dismiss w/o Prejudice including Exhibits; Civil Action No. 2:12-cv-1040.
  • Edison Welding Institue, Inc. and Realweld Systems, Inc. -v- Lincoln Global, Inc.; Order Granting Motion; Civil Action No. 2:12-cv-1040.
  • Edison Welding Institute, Inc.; Docket; Civil Action No. 2:12-cv-1040.
  • Bender Shipbuilding and Repair Co. Virtual Welding-A Low Cost Virtual Reality Welding Training System. Proposal submitted pursuant to MSRP Advanced Shipbuilding Enterprise Research Announcement, Jan. 23, 2008. 28 pages, See also, http://www.nsrp.org/6-Presentations/WD/020409 Virtual Welding Wilbur.pdf;.
  • Porter, Nancy; Cote, Allan; Gifford, Timothy; and Lam, Wim. “Virtual Reality Welder Training.” The American Welding Society Fabtech International/ AWS Welding Show, Session 5. 29 pages; allegedly Chicago 2005;.
  • Tschirner, Petra; Hillers, Bernd; and Graser, Axel “A Concept for the Application of Augmented Reality in Manual Gas Metal Arc Welding.” Proceedings of the International Symposium on Mixed and Augmented Reality; 2 pages; 2002;.
  • Penrod, Matt. “New Welder Training Tools.” EWI PowerPoint presentation; 16 pages allegedly 2008;.
  • Echtler, Florian; Sturm, Fabian; Kindermann, Kay; Klinker, Gudrun; Stilla, Joachim; Trilk, Jorn; and Najafi, Hesam. “The Intelligent Welding Gun: Augmented Reality for Experimental Vehicle Construction.” Virtual and Augmented Reality Applications in Manufacturing. Eds. Ong, S.K. And Nee, A.Y.C. Springer Verlag. 27 pages. 2003.
  • Fite-Georgel, Pierre. “Is there a Reality in Industrial Augmented Reality?” 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR). 10 pages, allegedly 2011.
  • Aiteanu, Dorian; and Graser, Axel. “Generation and Rendering of a Virtual Welding Seam in an Augmented Reality Training Environment.” Proceedings of the Sixth IASTED International Conference on Visualization, Imaging and Image Processing, Aug. 28-30, 2006, 8 pages, allegedly Palma de Mallorca, Spain. Ed. J.J. Villaneuva. ACTA Press.
  • Hillers, B.; Graser, A. “Real time Arc-Welding Video Observation System.” 62nd International Conference of IIW, Jul. 12-17,2009, 5 pages, allegedly Singapore 2009.
  • Hillers, B.; Graser, A. “Direct welding arc observation without harsh flicker,” 8 pages, allegedly Fabtech International and AWS welding show, 2007.
  • Advance Program of American Welding Society Programs and Events. Nov. 11-14, 2007. 31 pages. Chicago.
  • Terebes: examples from http://www.terebes.uni-bremen.de.; 6 pages.
  • Sandor, Christian; Gudrun Klinker. “Paarti: Development of an Intelligent Welding Gun for BMW.” PIA2003, 7 pages, Tokyo. 2003.
  • Arvika Forum Vorstellung Projekt PAARI. BMW Group Virtual Reality Center. 4 pages. Nuernberg. 2003.
  • Sandor, Christian; Klinker, Gudrun. “Lessons Learned in Designing Ubiquitous Augmented Reality User Interfaces.” 21 pages, allegedly from Emerging Technologies of Augmented Reality: Interfaces Eds. Haller, M.; Billinghurst, M.; Thomas, B. Idea Group Inc. 2006.
  • Impact Welding: examples from current and archived website, trade shows, etc. See, e.g., http://www.impactwelding.com. 53 pages.
  • http://www.nsrp.org/6-Presentations/WDVirtual_Welder. pdf (Virtual Reality Welder Training, Project No. S1051, Navy ManTech Program, Project Review for ShipTech 2005); 22 pages. Biloxi, MS.
  • https://app.aws.org/w/r/www/wj/2005/03/WJ_2005_03.pdf (AWS Welding Journal, Mar. 2005 (see, e.g., p. 54)).; 114 pages.
  • https://app.aws.org/conferences/defense/live index.html (AWS Welding in the Defense Industry conference schedule, 2004); 12 pages.
  • https://app.aws.org/wj/2004/04/052/njc (AWS Virtual Reality Program to Train Welders for Shipbuilding, workshop information, 2004); 7 pages.
  • http://citeseerx.ist.psu.edu/viewdoc/download; jsessionid= E5 B275EI72A9E2 D E2803B9 A5BCA3 E8F8?doi=I 0.1.1.134.8879&rep=rep1&type=pdf (Virtual Reality Welder Training, Cooperative Research Program Summary report SR0512, Jul. 2005); 4 pages.
  • https://app.aws.org/wj/2007 /11/WJ200711.pdf (AWS Welding Journal, Nov. 2007); 240 pages.
  • American Welding Society, “Vision for Welding Industry”; 41 pages.
  • Energetics, Inc. “Welding Technology Roadmap”, Sep. 2000, 38 pages.
  • Aiteanu, Dorian; and Graser, Axel. “Computer-Aided Manual Welding Using an Augmented Reality Supervisor” Sheet Metal Welding Conference XII, Livonia, MI, May 9-12, 2006, 14 pages.
  • Hillers, Bernd; Aiteanu, Dorin and Graser, Axel “Augmented Reality—Helmet for the Manual Welding Process” Institute of Automation, University of Bremen, Germany; 21 pages.
  • Aiteanu, Dorin, Hillers, Bernd and Graser, Axel “A Step Forward in Manual Welding: Demonstration of Augmented Reality Helmet” Institute of Automation, University of Bremen, Germany, Proceedings of the Second IEEE and ACM International Symposium on Mixed and Augmented Reality; 2003; 2 pages.
  • ArcSentry Weld Quality Monitoring System; Native American Technologies allegedly 2002, 5 pages.
  • P/NA.3 Process Modelling and Optimization; Native American Technologies, allegedly 2002, 5 pages.
  • B. Hillers, D. Aitenau, P. Tschimer, M. Park, A. Graser, B. Balazs, L. Schmidt, “Terebes: Welding Helmet with AR Capabilities”, Institute of Automatic University Bremen; Institute of Industrial Engineering and Ergonomics, 10 pages, allegedly 2004.
  • “Sheet Metal Welding Conference XII”, American Welding Society Detroit Section, May 2006, 11 pages.
  • Kenneth Fast, Timothy Gifford, Robert Yancey, “Virtual Training for Welding”, Proceedings of the Third IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2004); 2 pages.
  • Claude Choquet, “ARC+®: Today's Virtual Reality Solution for Welders” estimated Jan. 1, 2008, 6 pages.
  • ARC+ Welding Simulation presentation; 25 pages.
  • Chuansong Wu, “Microcomputer-based welder training simulator” Computers in Industry 20, 1992, 5 pages.
  • P. Tschirner et al., “Virtual and Augmented Reality for Quality Improvement of Manual Welds” National Institute of Standards and Technology, Jan. 2002, Publication 973, 24 pages.
  • Matt Phar, GPU Gems 2 Programming Techniques for High-Performance Graphics and General-Purpose Computation 2005, 12 pages.
  • Y. Wang et al., “Impingement of Filler Droplets and Weld Pool During Gas Metal Arc Welding Process” International Journal of Heat and Mass Transfer, Sep. 1999, 14 pages.
  • Larry Jeffus, “Welding Principles and Applications” Sixth Edition, 2008, 10 pages.
  • R.J. Renwick et al., “Experimental Investigation of GTA Weld Pool Oscillations” Welding Research—Supplement to the Welding Journal, Feb. 1983, 7 pages.
  • Dorin Aiteanu et al., “Generation and Rendering of a Virtual Welding Seam in an Augmented Reality Training Environment” Proceedings of the Sixth IASTED International Conference, Aug. 2006, 8 pages.
  • VRSim Inc. “About Us—History” www.vrsim.net/history, 2016, 1 page.
  • VRSim Powering Virtual Reality, www.lincolnelectric.com/en-us/equipment/training-equipment/Pages/powered-by-vrsim.aspx, 2016, 1 page.
  • ARC+—Archived Press Release from WayBack Machine from Jan. 31, 2008-Apr. 22, 2013, Page, https://web.archive.org/web/20121006041803/http://www.123certification.com/en/article_press/index.htm, Jan. 21, 2016, 3 pages.
  • Aidun, Daryush “Influence of Simulated High-g on the Weld Size of Al—Li Alloy” Elevator Sciece Ltd.; Jan. 2001; 4 pages.
  • ARC Simulation & Certification, Weld Into the Future, 4 pages, Est. Jan. 2005.
  • International Search Report for PCT/IB2015/000777, dated Dec. 15, 2016; 11 pages.
  • International Search Report for PCT/IB2015/000814 dated Dec. 15, 2016; 9 pages.
  • Exhibit B from Declaration of Morgan Lincoln in Lincoln Electric Co. et al. v. Seabery Soluciones, S.L. et al., Case No. 1:15-cv-01575-DCN, dated Dec. 20, 2016, 5 pages.
  • Bryan E. Feldman, James F. O'Brien, Bryan M. Klingner, and Tolga G. Goktekin. Fluids in deforming meshes. In ACM Siggraph/Eurographics Symposium on Computer Animation 2005, Jul. 2005.
  • Adam W. Bargteil, Tolga G. Goktekin, James F. O'Brien, and John A. Strain. A semi-lagrangian contouring method for fluid simulation. ACM Transactions on Graphics, 25(1), Jan. 2006.
  • Adam W. Bargteil, Funshing Sin, Jonathan E. Michaels, Tolga G. Goktekin, and James F. O'Brien. A texture synthesis method for liquid animations. In Proceedings of the ACM Siggraph/Eurographics Symposium on Computer Animation, Sep. 2006.
  • Bryan M. Klingner, Bryan E. Feldman, Nuttapong Chentanez, and James F. O'Brien. Fluid animation with dynamic meshes. In Proceedings of ACM Siggraph 2006, pp. 820-825, Aug. 2006.
  • Nuttapong Chentanez, Tolga G. Goktekin, Bryan E. Feldman, and James F. O'Brien. Simultaneous coupling of fluids and deformable bodies. In ACM Siggraph/Eurographics Symposium on Computer Animation, pp. 83-89, Aug. 2006.
  • Nuttapong Chentanez, Bryan E. Feldman, François Labelle, James F. O'Brien, and Jonathan R. Shewchuk. Liquid simulation on lattice-based tetrahedral meshes. In ACM Siggraph/Eurographics Symposium on Computer Animation 2007, pp. 219-228, Aug. 2007.
  • Pascal Clausen, Martin Wicke, Jonathan R. Shewchuk, and James F. O'Brien. Simulating liquids and solid-liquid interactions with lagrangian meshes. ACM Transactions on Graphics, 32(2):17:1-15, Apr. 2013. Presented at Siggraph 2013.
  • Kass, M., and Miller, G., “Rapid, Stable Fluid Dynamics for Computer Graphics,” Proceedings of Siggraph '90, in Computer Graphics, vol. 24, No. 4, pp. 49-57, Sep. 1990.
  • Nathan Holmberg and Burkhard C. Wünsche Efficient modeling and rendering of turbulent water over natural terrain. In Proceedings of the 2nd international conference on Computer graphics and interactive techniques in Australasia and South East Asia (Graphite '04), Jun. 2004.
  • James F. O'Brien and Jessica K Hodgins. “Dynamic Simulation of Splashing Fluids”. In Proceedings of Computer Animation 95, pp. 198-205, Apr. 1995.
  • Nils Thurey, Matthias Müller-Fischer, Simon Schirm, and Markus Gross. Real-time BreakingWaves for Shallow Water Simulations. In Proceedings of the 15th Pacific Conference on Computer Graphics and Applications (PG '07). Oct. 2007.
  • Nick Foster, Ronald Fedkiw, Practical animation of liquids, Proceedings of the 28th annual conference on Computer graphics and interactive techniques, p. 23-30, Aug. 2001.
  • N. Rasmussen, D. Enright, D. Nguyen, S. Marino, N. Sumner, W. Geiger, S. Hoon, R. Fedkiw, Directable photorealistic liquids, Proceedings of the 2004 ACM Siggraph/Eurographics symposium on Computer animation, Aug. 27-29, 2004, Grenoble, France.
  • Bryan E Feldman, James F. O'Brien, and Okan Arikan. “Animating Suspended Particle Explosions”. In Proceedings of ACM Siggraph 2003, pp. 708-715, Aug. 2003.
  • Tolga G. Goktekin, Adam W. Bargteil, and James F. O'Brien. “A Method for Animating Viscoelastic Fluids”. ACM Transactions on Graphics (Proc. of ACM Siggraph 2004), 23(3):463-468, Aug. 2004.
  • Geoffrey Irving, Eran Guendelman, Frank Losasso, Ronald Fedkiw, Efficient simulation of large bodies of water by coupling two and three dimensional techniques, ACM Siggraph 2006 Papers, Jul. 30-Aug. 3, 2006, Boston, Massachusetts.
  • Bart Adams, Mark Pauly, Richard Keiser, Leonidas J. Guibas, Adaptively sampled particle fluids, ACM Siggraph 2007 papers, Aug. 5-9, 2007, San Diego, California.
  • Matthias Müller, David Charypar, Markus Gross, Particle-based fluid simulation for interactive applications, Proceedings of the 2003 ACM Siggraph/Eurographics symposium on Computer animation, Jul. 26-27, 2003, San Diego, California.
  • Premoze, S., Tasdizen, T., Bigler, J., Lefohn, A. E., and Whitaker, R. T. Particle-based simulation of fluids. Comput. Graph. Forum 22, 3, 401-410 Sep. 2003.
  • Office Action from U.S. Appl. No. 14/526,914 dated Feb. 3, 2017.
  • International Preliminary Report from PCT/IB2015/001084 dated Jan. 26, 2017.
  • Sun Yaoming; Application of Micro Computer in Robotic Technologies; Science and Technology Literature Press; Catalogue of New Books of Science and Technology; Sep. 1987, pp. 145-150—CN and English.
  • Kenneth Fast; Virtual Welding—A Low Cost Virtual Reality Welder system training system phase II; NSRP ASE Technology Investment Agreement; Feb. 29, 2012; pp. 1-54.
  • The Lincoln Electric Company, CheckPoint Operator's Manual, 188 pages, issue date Aug. 2015.
  • Nick Foster, Dimitri Metaxas, Realistic animation of liquids, Graphical Models and Image Processing, v.58 n.5, p. 471-483, Sep. 1996.
  • International Search Report for PCT/IB2014/001796, dated Mar. 24, 3016; 8 pages.
  • International Search Report for PCT/IB2015/000161, dated Aug. 25, 2016; 9 pages.
  • Petition for Inter Partes Review of U.S. Pat. No. 8,747,116; IPR 2016-01568; Aug. 9, 2016; 75 pages.
  • Decision Termination Proceeding of U.S. Pat. No. 8,747,116; IPR 2016-01568; Nov. 15, 2016; 4 pages.
  • Decision Trial Denied IPR Proceeding of U.S. Pat. No. 8,747,116; IPR 2016-00749; Sep. 21, 2016; 21 pages.
  • Decision Denying Request for Rehearing of U.S. Pat. No. RE45398; IPR 2016-00840; Nov. 17, 2016; 10 pages.
  • Decision Trial Denied IPR Proceeding of U.S. Pat. No. 9,293,056; IPR 2016-00904; Nov. 3, 2016; 15 pages.
  • Decision Trial Denied IPR Proceeding of U.S. Pat. No. 9,293,057; IPR 2016-00905; Nov. 3, 2016; 21 pages.
  • Porter, Nancy C.; “Virtual Reality Welder Training,” Journal of Ship Production, vol. 22, No. 3, Aug. 2006, pp. 126-138.
  • William Hoff, Khoi Nguyen, “Computer Vision Based Registration Techniques for Augmented Reality ”, Colorado School of Mines, Division of Engineering, Proceedings of Intellectual Robots and Computer Vision XV, pp. 538-548; SPIE vol. 2904, Nov. 18-22, 1996, Boston MA.
  • Final Written Decision dated Oct. 2, 2017, Case IPR 2016-00840, Patent RE45,398, Seabery North America Inc. (Petitioner) vs. Lincoln Global, Inc. (Patent Owner), pp. 1-65.
  • Catalina, Stefanescu, Sen, and Kaukler, “Interaction of Porosity with a Planar Solid/Liquid Interface” (“Catalina”), Metallurgical and Materials Transactions, vol. 35A, May 2004, pp. 1525-1538.
  • Swantec corporate web page downloaded Apr. 19, 2016. http://www.swantec.com/technology/numerical-simulation/.
  • Complaint for Patent Infringement in Lincoln Electric Co. et al. v. Seabery Soluciones, S.L. et al., Case No. 1:15-cv-01575-DCN, docket No. 1, filed Aug. 10, 2015, in the U.S. District Court for the Northern District of Ohio; 81 pages.
  • Amended Answer to Complaint with Exhibit A for Patent Infringement filed by Seabery North America Inc. In Lincoln Electric Co. et al. v. Seabery Soluciones, S.L. et al., Case No. 1:15-cv-01575-DCN, docket No. 44, filed Mar. 1, 2016, in the U.S. District Court for the Northern District of Ohio; 19 pages.
  • Amended Answer to Complaint with Exhibit A for Patent Infringement filed by Seabery Soluciones Sl in Lincoln Electric Co. et al. v. Seabery Soluciones, S.L. et al., Case No. 1:15-cv-01575-DCN, docket No. 45, filed Mar. 1, 2016, in the U.S. District Court for the Northern District of Ohio; 19 pages.
  • Reply to Amended Answer to Complaint for Patent Infringement filed by Lincoln Electric Company; Lincoln Global, Inc. In Lincoln Electric Co. et al. v. Seabery Soluciones, S.L. et al., Case No. 1:15-cv-01575-DCN; docket No. 46, filed Mar. 22, 2016; 5 pages.
  • Answer for Patent Infringement filed by Lincoln Electric Company, Lincoln Global, Inc. in Lincoln Electric Co. et al. v. Seabery Soluciones, S.L. et al., Case No. 1:15-cv-01575-DCN; docket No. 47, filed Mar. 22, 2016; 5 pages.
  • Petition for Inter Partes Review of U.S. Pat. No. 8,747,116; IPR 2016-00749; Apr. 7, 2016; 70 pages.
  • Petition for Inter Partes Review of U.S. Pat. No. RE45,398; IPR 2016-00840; Apr. 18, 2016; 71 pages.
  • Petition for Inter Partes Review of U.S. Pat. No. 9,293,056; IPR 2016-00904; May 9, 2016; 91 pages.
  • Petition for Inter Partes Review of U.S. Pat. No. 9,293,057; IPR 2016-00905; May 9, 2016; 87 pages.
  • Declaration of Dr. Michael Zyda, May 3, 2016, exhibit to IPR 2016-00749.
  • Declaration of Edward Bohnart, Apr. 27, 2016, exhibit to IPR 2016-00749.
  • Declaration of Dr. Michael Zyda, May 3, 2016, exhibit to IPR 2016-00905; 72 pages.
  • Declaration of Edward Bohnart, Apr. 27, 2016, exhibit to IPR 2016-00905; 23 pages.
  • Declaration of Dr. Michael Zyda, May 3, 2016, exhibit to IPR 2016-00904; 76 pages.
  • Declaration of Edward Bohnart, Apr. 27, 2016, exhibit to IPR 2016-00904; 22 pages.
  • Declaration of AxelGraeser, Apr. 17, 2016, exhibit to IPR 2016-00840; 88 pages.
  • SIMFOR / CESOL, “RV-SOLD” Welding Simulator, Technical and Functional Features, 20 pages, estimated Jan. 2010.
  • Teeravarunyou et al, “Computer Based Welding Training System,” International Journal of Industrial Engineering (2009) 16(2): 116-125.
  • Antonelli et al, “A Semi-Automated Welding Station Exploiting Human-Robot Interaction,” Advanced Manufacturing Systems and Technology (2011) pp. 249-260.
  • Praxair Technology Inc, “The RealWeld Trainer System: Real Weld Training Under Real Conditions” Brochure (2013) 2 pages.
  • United States Provisional Patent Application for “System for Characterizing Manual Welding Operations on Pipe and Other Curved Structures,” Prov. U.S. Appl. No. 62/055,724, filed Sep. 26, 2014, 35 pages.
  • Lincoln Global, Inc., “VRTEX 360: Virtual Reality Arc Welding Trainer” Brochure (2015) 4 pages.
  • J.Y. (Yosh) Mantinband, Hillel Goldenberg, Llan Kleinberger, Paul Kleinberger, Autosteroscopic, field-sequential display with full freedom of movement OR Let the display were the shutter-glasses, 3ality (Israel) Ltd., 8 pages, 2002.
  • Kobayashi, Ishigame, and Kato, “Simulator of Manual Metal Arc Welding with Haptic Display” (“Kobayashi 2001”), Proc. of the 11th International Conf. on Artificial Reality and Telexistence (ICAT), Dec. 5-7, 2001, pp. 175-178, Tokyo, Japan.
  • Wahi, Maxwell, and Reaugh, “Finite-Difference Simulation of a Multi-Pass Pipe Weld” (“Wahi”), vol. L, paper 3/1, International Conference on Structural Mechanics in Reactor Technology, San Francisco, CA, Aug. 15-19, 1977.
  • Nuhan Onew Technology Co Ltd, “ONEW-360 Welding Training Simulator” http://en.onewtech.com/_d276479751.htm as accessed on Jul. 10, 2015, 12 pages.
  • The Lincoln Electric Company, “VRTEX Virtual Reality Arc Welding Trainer,” http://www.lincolnelectric.com/en-us/equipment/training-equipment/Pages/vrtex.aspx as accessed on Jul. 10, 2015, 3 pages.
  • Miller Electric Mfg Co, “LiveArc: Welding Performance Management System” Owner's Manual, (Jul. 2014) 64 pages.
  • Miller Electric Mfg Co, “LiveArc Welding Performance Management System” Brochure, (Dec. 2014) 4 pages.
  • Kobayashi, Ishigame, and Kato, “Skill Training System of Manual Arc Welding by Means of Face-Shield-Like HMD and Virtual Electrode” (“Kobayashi 2003”), Entertainment Computing, vol. 112 of the International Federation for Information Processing (IFIP), Springer Science + Business Media, New York, copyright 2003, pp. 389-396.
  • G.E. Moore, “No exponential is forever: but ‘Forever’ can be delayed!,” IEEE International Solid-State Circuits Conference, 2003. 19 pages.
  • “High Performance Computer Architectures_ A Historical Perspective,” downloaded May 5, 2016. http://homepages.inf.ed.ac.uk/cgi/mi/comparch. pl?Paru/perf.html,Paru/perf-f.html,Paru/menu-76.html; 3 pages.
  • Andreas Grahn, “Interactive Simulation of Contrast Fluid using Smoothed Particle Hydrodynamics,” Jan. 1, 2008, Master's Thesis in Computing Science, Umeå University, Department of Computing Science, Umeå, Sweden; 69 pages.
  • Marcus Vesterlund, “Simulation and Rendering of a Viscous Fluid using Smoothed Particle Hydrodynamics,” Dec. 3, 2004, Master's Thesis in Computing Science, Umeå University, Department of Computing Science, Umeå, Sweden; 46 pages.
  • M. Müller, et al., “Point Based Animation of Elastic, Plastic and Melting Objects,” Eurographics/ACM Siggraph Symposium on Computer Animation (2004); 11 pages.
  • Andrew Nealen, “Point-Based Animation of Elastic, Plastic, and Melting Objects,” CG topics, Feb. 2005; 2 pages.
  • D. Tonnesen, Modeling Liquids and Solids using Thermal Particles, Proceedings of Graphics Interface'91, pp. 255-262, Calgary, Alberta, 1991.
  • “CUDA Programming Guide Version 1.1,” Nov. 29, 2007. 143 pages.
  • Webster's II new college dictionary, 3rd ed., Houghton Mifflin Co., copyright 2005, Boston, MA, p. 1271, definition of “wake.” 3 pages.
  • Da Dalto L, et al. “CS Wave: Learning welding motion in a virtual environment” Published in Proceedings of the IIW International Conference, Jul. 10-11, 2008; 19 pages.
  • CS Wave-Manual, “Virtual Welding Workbench User Manual 3.0” 2007; 25 pages.
  • Choquet, Claude. “ARC+®: Today's Virtual Reality Solution for Welders”, Published in Proceedings of the IIW Internatioal Conference; Jul. 10-11, 2008; 19 pages.
  • Welding Handbook, Welding Science & Technology, American Welding Society, Ninth Ed., Copyright 2001. Appendix A “Terms and Definitions” 54 pages.
  • Virtual Welding: A Low Cost Virtual Reality Welder Training System, NSRP RA 07-01—BRP Oral Review Meeting in Charleston, SC at ATI, Mar. 2008; 6 pages.
  • Dorin Aiteanu “Virtual and Augmented Reality Supervisor for a New Welding Helmet” Dissertation Nov. 15, 2005; 154 pages.
  • “The Evolution of Computer Graphics,” Tony Tamasi, NVIDIA, 2008; 36 pages.
  • ViziTech USA, retrieved on Mar. 27, 2014 from http://vizitechusa.com/, 2 pages.
  • Guu and Rokhlin, Technique for Simultaneous Real-Time Measurements of Weld Pool Surface Geometry and Arc Force, 10 pages, Dec. 1992.
  • S.B. Chen, L. Wu, Q. L. Wang and Y. C. Liu, Self-Learning Fuzzy Neural Networks and Computer Vision for Control of Pulsed GTAW, 9 pages, dated May 1997.
  • Patrick Rodjito, Position tracking and motion prediction using Fuzzy Logic, 81 pages, 2006, Colby College.
  • D'Huart, Deat, and Lium; Virtual Environment for Training, 6th International Conference, ITS 20002, 6 pages, Jun. 2002.
  • Konstantinos Nasios (Bsc), Improving Chemical Plant Safety Training Using Virtual Reality, Thesis submitted to the University of Nottingham for the Degree of Doctor of Philosophy, 313 pages, Dec. 2001.
  • ANSI/A WS D 10.11 MID 10. 11 :2007 Guide for Root Pass Welding of Pipe without Backing Edition: 3rd American Welding Society / Oct. 13, 2006/36 pages ISBN: 0871716445, 6 pages.
  • M. Jonsson, L. Karlsson, and L-E Lindgren, Simulation of Tack Welding Procedures in Butt Joint Welding of Plate Welding Research Supplement, Oct. 1985, 7 pages.
  • Isaac Brana Veiga, Simulation of a Work Cell in the IGRIP Program, dated 2006, 50 pages.
  • Balijepalli, A. and Kesavadas, Haptic Interfaces for Virtual Environment and Teleoperator Systems, Haptics 2003, 7-., Department of Mechanical & Aerospace Engineering, State University of New York at Buffalo, NY.
  • Johannes Hirche, Alexander Ehlert, Stefan Guthe, Michael Doggett, Hardware Accelerated Per-Pixel Displacement Mapping, 8 pages.
  • Yao et al., ‘Development of a Robot System for Pipe Welding’. 2010 International Conference on Measuring Technology and Mechatronics Automation. Retrieved from the Internet: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5460347&tag=1; pp. 1109-1112, 4 pages.
  • Steve Mann, Raymond Chun Bing Lo, Kalin Ovtcharov, Shixiang Gu, David Dai, Calvin Ngan, Tao Al, Realtime HDR (High Dynamic Range) Video for Eyetap Wearable Computers, FPGA-Based Seeing Aids, and Glasseyes (Eyetaps), 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1-6, 6 pages, Apr. 29, 2012.
  • Kyt Dotson, Augmented Reality Welding Helmet Prototypes How Awsome the Technology Can Get, Sep. 26, 2012, Retrieved from the Internet: URL:http://siliconangle.com/blog/2012/09/26/augmented-reality-welding-helmet-prototypes-how-awesome-the-technology-can-get/, 1 page, retrieved on Sep. 26, 2014.
  • Terrence O'Brien, “Google's Project Glass gets some more details”, Jun. 27, 2012 (Jun. 27, 2012), Retrieved from the Internet: http://www.engadget.com/2012/06/27/googles-project-glass-gets-some-more-details/, 1 page, retrieved on Sep. 26, 2014.
  • William T. Reeves, “Particles Systems—A Technique for Modeling a Class of Fuzzy Objects”, Computer Graphics 17:3 pp. 359-3761983.
  • Fletcher Yoder Opinion re RE45398 and U.S. Appl. No. 14/589,317; including appendices; Sep. 9, 2015; 1700 pages.
  • Screen Shot of CS Wave Exercise 135.FWPG Root Pass Level 1 https://web.archive.org/web/20081128081858/http:/wave_c-sfr/images/english/snap_evolution2.jpg; 1 page.
  • Screen Shot of CS Wave Control Centre V3.0.0 https://web.archive.org/web/20081128081915/http:/wave.c-s.fr/images/english/snap_evolution4.jpg; 1 page.
  • Screen Shot of CS Wave Control Centre V3.0.0 https://web.archive.org/web/20081128081817/http:/wave.c-s.fr/images/english/snap_evolution6.jpg; 1 page.
  • Da Dalto L, et al. “CS Wave a Virtual learning tool for the welding motion,” Mar. 14, 2008; 10 pages.
  • Nordruch, Stefan, et al. “Visual Online Monitoring of PGMAW Without a Lighting Unit”, Jan. 2005; 14 pages.
  • ChemWeb.com—Journal of Materials Engineering (printed Sep. 26, 2012) (01928041).
  • Heat and mass transfer in gas metal arc welding. Part 1: the arc by J. Hu, and Hi Tsai found in ScienceDirect, International Journal of Heat and Mass transfer 50 (2007) 833-846 Available on Line on Oct. 24, 2006 http://web.mst/edu/˜tsai/publications/Hu-IJHMT-2007-1-60.pdf.
  • Texture Mapping by. Ian Graham, Carnegie Mellon University Class 15-462 Computer graphics, Lecture 10 dated Feb. 13, 2003.
  • Nancy C. Porter, J. Allan Cote, Timothy D. Gifford, and Wim Lam, Virtual Reality Welder Training, dated Jul. 14, 2006.
  • European Examination Report for application No. 17001820.4, 4 pp., dated May 16, 2019.
  • Yizhong Wang, Younghua Chen, Zhongliang Nan, Yong Hu, Study on Welder Training by Means of Haptic Guidance and Virtual Reality for Arc Welding, 2006 IEEE International Conference on Robotics and Biomimetrics, pp. 954-958, ROBIO 2006 ISBN-10:1424405718, Dec. 17-20, 2006, Kunming, China.
  • Nancy C. Porter, J. Allan Cote, Timothy D. Gifford, Wim Lan, Virtual Reality Welder Training, Paper No. 2005-P19, 2005, pp. 1-14.
  • Asciencetutor.Com, A Division of Advanced Science and Automation Corp., VWL (Virtual Welding Lab), 2007, 2 pages.
  • Edison Welding Institute, E-Weld Predictor, 3 pages, 2008, Columbus, OH.
  • Tim Heston, Virtually welding, The Fabricator, Mar. 2008, 4 pages. FMA Communications Inc., Rockford, IL, www.thefabricator.com. NSRP ASE, Low-Cost Virtual Reality Welder Training System, 2008, 1 page.
  • Steven White, Mores Prachyabrued, Dhruva Baghi, Amit Aglawe, Dirk Reiners, Christoph Borst, Terry Chambers, Virtual Welder Trainer, IEEE Virtual Reality 2009, p. 303.
  • Nancy Porter, J. Allan Cote, Timothy Gifford, Virtual Reality Welder Training, CRP Cooperative Research Program, Summary Report SR 0512, Jul. 2005, 4 pages.
  • Weld Into the Future, Eduwelding+, Training Activities with arc+simulator, 2005, 4 pages.
  • Claude Choquet, ARC+: Today's Virtual Reality Solution for Welders, 123 Certification In.,Montreal, Quebec, CA, May 2008, 6 pages.
  • Laurent Da Dalto, Dominique Steib, Daniel Mellet-d'Huart, Olivier Balet, CS WAVE A Virtual learning tool for the welding motion, http://www,c-s.fr, Mar. 14, 2008, 10 pages.
  • CS WAVE, The Virtual Welding Trainer, 6 pages, 2007.
  • Fronius—virtual welding, www.fh-joanneum.at/ca/cn/yly/?lan=en, 2 pages, May 12, 2008.
  • Fronius, ARS Electronica, 2 pages, May 18, 1997.
  • P/NA.3 Process Modelling and Optimization, www.natech-inc.com/pna3/index.html, 11 pages, Jun. 4, 2008.
  • “RV-Sold” Welding Simulator Technical and Functional Features, SIMFOR, pp. 1-20, date unknown.
  • Juan Vicente Rosell, RV-Sold: Simulador virtual para la formacion de soldadores, Deformacion Metalica, Es. vol. 34, No. 301, 14 pages, Jan. 1, 2008.
  • Kenneth Fast, Timothy Gifford, Robert Yancy, Virtual Training for Welding, 3rd IEEE and ACM International symposium on Mixed and Augmented Reality (ISMAR 2004), 2 pages, 2004.
  • D. Mavrikios, V. Karabatsou, D. Fragos, G. Chryssolouris, A proto-type virtual reality-0cased demonstrator for immersive and interactive simulation of welding processes, International Journal of Computer Integrated Manufacturing, 294-301, 2006.
  • PCT/IB2009/00605 International Search Report.
  • PCT/IB2009/00605 Written Opinion.
  • U.S. Appl. No. 29/399,980, filed Jul. 10, 2009, issued May 11, 2010 as D615,573.
  • U.S. Appl. No. 29/339,979, filed Jul. 10, 2009, issued Apr. 20, 2010 as D614,217.
  • U.S. Appl. No. 29/339,978, filed Jul. 10, 2009.
  • U.S. Appl. No. 12/504,870, filed Jul. 17, 2009 claiming priority to U.S. Appl. No. 61/090,794.
  • U.S. Appl. No. 12/501,263, filed Jul. 10, 2009 claiming priority to U.S. Appl. No. 61/090,794.
  • U.S. Appl. No. 12/501,257, filed Jul. 10, 2009 claiming priority to U.S. Appl. No. 61/090,764.
Patent History
Patent number: RE47918
Type: Grant
Filed: Jan 5, 2015
Date of Patent: Mar 31, 2020
Assignee: Lincoln Global, Inc. (Santa Fe Springs, CA)
Inventor: Matthew Wayne Wallace (South Windsor, CT)
Primary Examiner: William C Doerrler
Application Number: 14/589,317
Classifications
Current U.S. Class: Controlled In Response To Current, Voltage, Or Temperature (219/110)
International Classification: B23K 9/06 (20060101); B23K 9/095 (20060101);