Abstract: Determining a quality score for a part manufactured by an additive manufacturing machine based on build parameters and sensor data without the need for extensive physical testing of the part. Sensor data is received from the additive manufacturing machine during manufacture of the part using a first set of build parameters. The first set of build parameters is received. A first algorithm is applied to the first set of build parameters and the received sensor data to generate a quality score. The first algorithm is trained by receiving a reference derived from physical measurements performed on at least one reference part built using a reference set of build parameters. The quality score is output via the communication interface of the device.

Abstract: A method of calibrating an additive manufacturing machine includes obtaining a model for the additive manufacturing machine, obtaining a baseline sensor data set for a particular additive manufacturing machine, creating a machine-specific nominal fingerprint for the particular additive manufacturing machine with controllable variation for one or more process inputs, producing on the particular additive manufacturing machine a test-page based object, obtaining a current sensor data set of the test-page based object on the particular additive manufacturing machine, estimating a scaling factor or a bias for each of the one or more process inputs from the current data set, and updating a calibration file for the particular additive machine if the estimated scaling error or bias are greater than a respective predetermined tolerance. A system for implementing the method and a non-transitory computer-readable medium are also disclosed.

Abstract: Determining a quality score for a part manufactured by an additive manufacturing machine based on build parameters and sensor data without the need for extensive physical testing of the part. Sensor data is received from the additive manufacturing machine during manufacture of the part using a first set of build parameters. The first set of build parameters is received. A first algorithm is applied to the first set of build parameters and the received sensor data to generate a quality score. The first algorithm is trained by receiving a reference derived from physical measurements performed on at least one reference part built using a reference set of build parameters. The quality score is output via the communication interface of the device.

Abstract: Providing updated build parameters to an additive manufacturing machine to improve quality of a part manufactured by the machine. Sensor data is received from the additive manufacturing machine during manufacture of the part using a first set of build parameters. The first set of build parameters is received. An evaluation parameter is determined based on the first set of build parameters and the received sensor data. Thermal data is generated based on a thermal model of the part derived from the first set of build parameters. A first algorithm is applied to the received sensor data, the determined evaluation parameter, and the generated thermal data to produce a second set of build parameters, the first algorithm being trained to improve the evaluation parameter. The second set of build parameters is output to the additive manufacturing machine to produce a second part.

Abstract: A method of calibrating an additive manufacturing machine includes obtaining a model for the additive manufacturing machine, obtaining a baseline sensor data set for a particular additive manufacturing machine, creating a machine-specific nominal fingerprint for the particular additive manufacturing machine with controllable variation for one or more process inputs, producing on the particular additive manufacturing machine a test-page based object, obtaining a current sensor data set of the test-page based object on the particular additive manufacturing machine, estimating a scaling factor or a bias for each of the one or more process inputs from the current data set, and updating a calibration file for the particular additive machine if the estimated scaling error or bias are greater than a respective predetermined tolerance. A system for implementing the method and a non-transitory computer-readable medium are also disclosed.

Abstract: A method includes applying thermal and/or strain modeling to the CAD representation of an object. In addition, scan path data is generated based at least in part on a result of the thermal and/or strain modeling. A build file comprising the scan path data is generated. The build file comprises instructions that configure an additive manufacturing tool to generate the object according to the scan path data.

Abstract: A method includes applying thermal and/or strain modeling to the CAD representation of an object. In addition, scan path data is generated based at least in part on a result of the thermal and/or strain modeling. A build file comprising the scan path data is generated. The build file comprises instructions that configure an additive manufacturing tool to generate the object according to the scan path data.