Abstract: Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method includes receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.

Abstract: Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.

Abstract: A system monitoring an additive manufacturing (AM) machine recoat operation includes an automatic defect recognition subsystem having a predictive model catalog each applicable to a product and to one recoat error indication having a domain dependent feature, the predicative models representative of a recoat error indication appearance at a pixel level of an image captured during recoat operations. The system includes an online monitoring subsystem having an image classifier unit that classifies recoat error indications at the pixel level based on predictive models selected on their metadata, a virtual depiction unit that creates a virtual depiction of an ongoing AM build from successive captured image, and a processor unit to monitor the build for recoat error indications, classify a detected indication, and provide a determination regarding the severity of the detected indication on the ongoing build. A method and a non-transitory computer-readable medium are also disclosed.

Abstract: Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.

Abstract: Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.

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 system monitoring an additive manufacturing (AM) machine recoat operation includes an automatic defect recognition subsystem having a predictive model catalog each applicable to a product and to one recoat error indication having a domain dependent feature, the predicative models representative of a recoat error indication appearance at a pixel level of an image captured during recoat operations. The system includes an online monitoring subsystem having an image classifier unit that classifies recoat error indications at the pixel level based on predictive models selected on their metadata, a virtual depiction unit that creates a virtual depiction of an ongoing AM build from successive captured image, and a processor unit to monitor the build for recoat error indications, classify a detected indication, and provide a determination regarding the severity of the detected indication on the ongoing build. A method and a non-transitory computer-readable medium are also disclosed.

Abstract: Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.

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: Some embodiments facilitate creation of an industrial asset item via an additive manufacturing process. A laser source may receive a laser power command signal PC and generate a laser beam output in accordance with PC. A first sensor may measure a power PD of a laser beam delivered for the additive manufacturing process. A second sensor may measure a power PO associated with the laser beam output from the laser source, wherein at least a portion of an optic train is located between the first and second sensors. A monitoring apparatus, coupled to the first and second sensors, may monitor PC, PO, and PD to facilitate creation of the industrial asset item. Responsive to the monitoring, the system may control at least one aspect of the additive manufacturing process, automatically generate an advisory indication, automatically localize a detected problem in the system, automatically predict a future performance of the system, etc.