Laser powder bed fusion additive manufacturing anomaly detection

An additive manufacturing system comprising:

• a powder depositing system for depositing a layer of additive material onto a powder bed of an additive manufacturing machine;
• a first energy source for selectively directing a first energy beam onto a first focal point on the layer of additive material;
• an electromagnetic energy sensor configured to generate a time-series signal comprising a plurality of samples at a sampling rate greater than or equal to a first sampling rate, the plurality of samples correspond to electromagnetic emissions from an interaction between the first energy source and powder at the first focal point on the layer of additive material;
• and
• a controller operably coupled to the electromagnetic energy sensor, the controller being configured to:
• obtain, from the electromagnetic energy sensor, the time-series signal;
• decompose the time-series signal into at least a high-frequency signal variation component;
• compute one or more spatiotemporal statistical values from the high-frequency signal variation component of the time-series signal;
• identify, with a machine learning model, one or more perturbations, from the one or more spatiotemporal statistical values, that exceeds a predetermined threshold corresponding to the one or more spatiotemporal statistical values;
• and
• generate an alert in response to identifying the one or more perturbations.

A apparatus comprising:

• a controller that includes one or more processors and one or more memories coupled with the one or more processors, the controller configured to cause the apparatus to:
• obtain, from an electromagnetic energy sensor, a time-series signal comprising a plurality of samples, wherein the samples correspond to electromagnetic emissions from an interaction between a first energy source and powder at a first focal point on a layer of additive material and the samples are generated at a sampling rate greater than or equal to a first sampling rate;
• decompose the time-series signal into at least a high-frequency signal variation component;
• compute one or more spatiotemporal statistical values from the high-frequency signal variation component of the time-series signal;
• identify one or more perturbations, from the one or more spatiotemporal statistical values, that exceeds a predetermined threshold corresponding to the one or more spatiotemporal statistical values;
• and
• generate an alert in response to identifying the one or more perturbations.