Abstract: This invention teaches a quality assurance system for additive manufacturing. This invention teaches a multi-sensor, real-time quality system including sensors, affiliated hardware, and data processing algorithms that are Lagrangian-Eulerian with respect to the reference frames of its associated input measurements. The quality system for Additive Manufacturing is capable of measuring true in-process state variables associated with an additive manufacturing process, i.e. those in-process variables that define a feasible process space within which the process is deemed nominal. The in-process state variables can also be correlated to the part structure or microstructure and can then be useful in identifying particular locations within the part likely to include defects.

Abstract: This invention teaches a quality assurance system for additive manufacturing. This invention teaches a multi-sensor, real-time quality system including sensors, affiliated hardware, and data processing algorithms that are Lagrangian-Eulerian with respect to the reference frames of its associated input measurements. The quality system for Additive Manufacturing is capable of measuring true in-process state variables associated with an additive manufacturing process, i.e. those in-process variables that define a feasible process space within which the process is deemed nominal. The in-process state variables can also be correlated to the part structure or microstructure and can then be useful in identifying particular locations within the part likely to include defects.

Abstract: This disclosure describes various system and methods for monitoring photons emitted by a heat source of an additive manufacturing device. Sensor data recorded while monitoring the photons can be used to predict metallurgical, mechanical and geometrical properties of a part produced during an additive manufacturing operation. In some embodiments, a test pattern can be used to calibrate an additive manufacturing device.

Abstract: This invention teaches a multi-sensor quality inference system for additive manufacturing. This invention still further teaches a quality system that is capable of discerning and addressing three quality issues: i) process anomalies, or extreme unpredictable events uncorrelated to process inputs; ii) process variations, or difference between desired process parameters and actual operating conditions; and iii) material structure and properties, or the quality of the resultant material created by the Additive Manufacturing process. This invention further teaches experimental observations of the Additive Manufacturing process made only in a Lagrangian frame of reference. This invention even further teaches the use of the gathered sensor data to evaluate and control additive manufacturing operations in real time.

Abstract: This invention teaches a quality assurance system for additive manufacturing. This invention teaches a multi-sensor, real-time quality system including sensors, affiliated hardware, and data processing algorithms that are Lagrangian-Eulerian with respect to the reference frames of its associated input measurements. The quality system for Additive Manufacturing is capable of measuring true in-process state variables associated with an additive manufacturing process, i.e. those in-process variables that define a feasible process space within which the process is deemed nominal. The in-process state variables can also be correlated to the part structure or microstructure and can then be useful in identifying particular locations within the part likely to include defects.

Abstract: The present invention provides a feature extraction system that extracts geometrical features of a part using in-process data acquired during an additive manufacturing process. The geometric features are extracted by applying a number of image processing operations to images taken of a powder bed during the additive manufacturing process. In this way, both internal and external geometries of the part can be characterized. In some embodiments, geometric feature extraction can be used in conjunction with other part characterizing operations, such as for example, thermal characterization processes.

Abstract: A method of sensing and controlling an arc welding process employs a high equency rate of sampling of electrical signals from the welding circuit. The sampled signals are operated upon by predetermined processes to determine electrical resistance, shielding gas quality, and short circuit frequency. The process measurements are compared to a predetermined set of tolerance levels and evaluated using a window technique that updates the evaluation of the data samples at the sampling rate.

Abstract: A method and apparatus for preventing catastrophic melting of the contact be in continuously-fed consumable electrode arc welding due to wire feed interruptions by monitoring the movement of the wire electrode with a high frequency sensor such as all optical encoder having a resolution on the order of <0.1 second and switching off the power to the welding apparatus if the wire electrode speed falls below a predetermined threshold.

Abstract: An gas metal arc welding (GMAW) process is monitored by disposing a light intensity sensor in the vicinity of the arc, such that the output signal from the sensor is responsive to the intensity of the arc. The arc light intensity is analyzed to determine the arc length, and to determine of the mode of metal transfer and whether the welding process is proceeding as intended.