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 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 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 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 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 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.