Abstract: Systems and methods for distributed weld monitoring using jobs and job sessions are described. In some examples, a distributed monitoring system comprises a central monitoring station in communication with a user device and a local monitoring station. A user may use the user device to enter weld monitoring data that is subsequently received by the central monitoring station and stored in a central data repository. The central data repository may associate the weld monitoring data with welding data received from a welding device, as well as with a job session that is, in turn, associated with a job.

Abstract: Systems and methods for distributed weld monitoring using jobs and job sessions are described. In some examples, a distributed monitoring system comprises a central monitoring station in communication with a user device and a local monitoring station. A user may use the user device to enter weld monitoring data that is subsequently received by the central monitoring station and stored in a central data repository. The central data repository may associate the weld monitoring data with welding data received from a welding device, as well as with a job session that is, in turn, associated with a job.

Abstract: Systems and methods for distributed weld monitoring using jobs and job sessions are described. In some examples, a distributed monitoring system comprises a central monitoring station in communication with a user device and a local monitoring station. A user may use the user device to enter weld monitoring data that is subsequently received by the central monitoring station and stored in a central data repository. The central data repository may associate the weld monitoring data with welding data received from a welding device, as well as with a job session that is, in turn, associated with a job.

Abstract: Systems and methods for welding are described. The welding system can include, for example, a welding power source, a welding torch, and a computer. The computer and the welding torch can be operatively coupled to the power source. A first weld is performed and its signature is saved by the computer. It is considered a high quality weld and is selected as a weld reference. A second weld is performed and its signature is saved by the computer. The computer then computes a single weld confidence result for the second weld based on a comparison between the signature data of the second weld and the signature data of the reference weld. A weld fault condition is triggered based on the single weld confidence result which causes the welding system to stop or to modify the welding operation, and/or which causes the welding system to send out communications relating to the triggering of the weld fault condition.

Abstract: Systems and methods for labeling non-welding time periods using machine learning techniques are described. In some examples, a weld monitoring system may collect various data from sensors and/or welding equipment in a welding area over a time period. The data may evaluated to divide the time period into welding time periods and non-welding time periods. The weld monitoring system may use one or more machine learning models and/or techniques in combination with the collected data to determine what non-welding activities took place during the non-welding time periods. In some examples, the machine learning models may be continuously trained, updated, and/or improved using feedback from operators and/or other individuals, data from ongoing welding and/or non-welding activities, as well as data from other weld monitoring systems and/or machine learning models.

Abstract: Systems and methods for part quality confidence are described. In some examples a part quality confidence system may receive inputs (e.g., via sensor measurements, operator input(s), etc.) related to one or more stages of a part assembly process. The inputs may be representative of certain feature characteristics of the part and/or one or more of the assembly stages. A computational engine of the system may determine one or more quality characteristics of the part based on the feature characteristics, and assign a quality metric to the part (and/or part assembly process) based on the quality characteristics. In some examples, a quality rating may be assigned to the part based on the quality metric, so as to provide an even simpler abstraction.

Abstract: Systems and methods for welding are described. The welding system can include, for example, a power source, a computer, and a welding torch. The computer and the welding torch can be operatively coupled to the power source. The power source controls a wire feed and one of a current or a voltage to the welding torch. When the welding torch is performing pulsed welding, the computer is configured to receive a weld signature. The computer is configured to synthesize features from the weld signature and to analyze the features for each pulse of the weld signature to determine whether particular limits have been exceeded or met. If particular limits are exceed or met, a weld fault condition is triggered which causes the welding system to stop or to modify the pulsed welding operation, and/or which causes the welding system to send out communications relating to the triggering of the weld fault condition.

Abstract: Systems and methods for labeling non-welding time periods using machine learning techniques are described. In some examples, a weld monitoring system may collect various data from sensors and/or welding equipment in a welding area over a time period. The data may evaluated to divide the time period into welding time periods and non-welding time periods. The weld monitoring system may use one or more machine learning models and/or techniques in combination with the collected data to determine what non-welding activities took place during the non-welding time periods. In some examples, the machine learning models may be continuously trained, updated, and/or improved using feedback from operators and/or other individuals, data from ongoing welding and/or non-welding activities, as well as data from other weld monitoring systems and/or machine learning models.

Abstract: Systems and methods for using drones in dispersed welding applications are disclosed. In some examples, drones may be used in large and/or dispersed welding environments to quickly navigate the large distances and/or reach areas that might be more difficult for a person to reach. In some examples, the drones may use one or more attached devices to locate, identify, and/or collect information from welding equipment, welding workpieces, and/or welds within a (e.g., large and/or dispersed) welding environment.

Abstract: Systems and methods for weld monitoring systems with unknown downtime disabling are described. In some examples, a local monitoring station may perform activity tracking as part of a larger weld monitoring system. A welding device may send welding data to the local monitoring system, which may be used to determine a current activity. A user may also manually input an activity to use as the current activity. If the local monitoring station is unable to determine a current activity from the welding data or user input, then the local monitoring station determines that an unknown downtime has occurred. If the local monitoring station cannot determine a reason for the unknown downtime, the welding device may be disabled until the user provides a reason for the unknown downtime.

Abstract: Systems and methods for distributed weld monitoring using jobs and job sessions are described. In some examples, a distributed monitoring system comprises a central monitoring station in communication with a user device and a local monitoring station. A user may use the user device to enter weld monitoring data that is subsequently received by the central monitoring station and stored in a central data repository. The central data repository may associate the weld monitoring data with welding data received from a welding device, as well as with a job session that is, in turn, associated with a job.

Abstract: Systems and methods for part quality confidence are described. In some examples a part quality confidence system may receive inputs (e.g., via sensor measurements, operator input(s), etc.) related to one or more stages of a part assembly process. The inputs may be representative of certain feature characteristics of the part and/or one or more of the assembly stages. A computational engine of the system may determine one or more quality characteristics of the part based on the feature characteristics, and assign a quality metric to the part (and/or part assembly process) based on the quality characteristics. In some examples, a quality rating may be assigned to the part based on the quality metric, so as to provide an even simpler abstraction.

Abstract: Systems and methods for welding are described. The welding system can include, for example, a welding power source, a welding torch, and a computer. The computer and the welding torch can be operatively coupled to the power source. A first weld is performed and its signature is saved by the computer. It is considered a high quality weld and is selected as a weld reference. A second weld is performed and its signature is saved by the computer. The computer then computes a single weld confidence result for the second weld based on a comparison between the signature data of the second weld and the signature data of the reference weld. A weld fault condition is triggered based on the single weld confidence result which causes the welding system to stop or to modify the welding operation, and/or which causes the welding system to send out communications relating to the triggering of the weld fault condition.

Abstract: Systems and methods for welding are described. The welding system can include, for example, a welding power source, a welding torch, and a computer. The computer and the welding torch can be operatively coupled to the power source. A first weld is performed and its signature is saved by the computer. It is considered a high quality weld and is selected as a weld reference. A second weld is performed and its signature is saved by the computer. The computer then computes a single weld confidence result for the second weld based on a comparison between the signature data of the second weld and the signature data of the reference weld. A weld fault condition is triggered based on the single weld confidence result which causes the welding system to stop or to modify the welding operation, and/or which causes the welding system to send out communications relating to the triggering of the weld fault condition.

Abstract: Systems and/or methods for welding-type production cell monitoring are disclosed. Some examples of the present disclosure relate to a welding-type monitoring system configured to track parts, production, and/or operations within a welding-type production cell. In some examples, the monitoring system comprises a computing system, one or more tags attached to one or more first items, and/or one or more tag readers attached to one or more second items. In some examples, the one or more tags store data relating to the first items, and the one or more tag readers are configured to read the data using a close proximity communication protocol when in range of the one or more tags. After the tag reader reads the data from the tag, the tag data may be communicated to the computing system, which may, in turn process the data to determine whether one or more workflow events are associated with the data. If so, the computing system may execute an event script corresponding to the workflow event.

Abstract: Systems and methods for welding are described. The welding system can include, for example, a power source, a computer, and a welding torch. The computer and the welding torch can be operatively coupled to the power source. The power source controls a wire feed and one of a current or a voltage to the welding torch. When the welding torch is performing pulsed welding, the computer is configured to receive a weld signature. The computer is configured to synthesize features from the weld signature and to analyze the features for each pulse of the weld signature to determine whether particular limits have been exceeded or met. If particular limits are exceed or met, a weld fault condition is triggered which causes the welding system to stop or to modify the pulsed welding operation, and/or which causes the welding system to send out communications relating to the triggering of the weld fault condition.

Abstract: Systems and methods for welding are described. The welding system can include, for example, a welding power source, a welding torch, and a computer. The computer and the welding torch can be operatively coupled to the power source. A first weld is performed and its signature is saved by the computer. It is considered a high quality weld and is selected as a weld reference. A second weld is performed and its signature is saved by the computer. The computer then computes a single weld confidence result for the second weld based on a comparison between the signature data of the second weld and the signature data of the reference weld. A weld fault condition is triggered based on the single weld confidence result which causes the welding system to stop or to modify the welding operation, and/or which causes the welding system to send out communications relating to the triggering of the weld fault condition.

Abstract: The present invention provides a weld monitoring system and method that monitors and automatically coordinates information on the quality of each weld in a workpiece having one or more welds. In particular, each weld in the workpiece is automatically analyzed at the time it is being made using weld sensors such as those that measure current, wire feed, voltage, and gas flow to produce information on the quality of the weld. Using this information, the welds are sorted, displayed, and logged with workpiece and weld number information which is provided to the operator in real-time and stored in a computer for access at a later time for quality control or other purposes. Therefore, the system and method enables welds of a quality less than a pre-determined quality for the weld to be identified in real-time and information concerning any particular weld to be accessed at a later time.