In-situ prediction of part quality in additive manufacturing using sensor and simulation fusion

A computer-implemented method, comprising:

• acquiring, by at least one computing device, in-situ sensor data from a plurality of sensors during fabrication of a part by an additive manufacturing process;
• extracting, by the at least one computing device, sensor-derived features from the in-situ sensor data on a layer-by-layer basis;
• predicting, by the at least one computing device, thermal history quantifiers for the part using a physics-based thermal model;
• spatially aligning, by the at least one computing device, the sensor-derived features and the thermal history quantifiers on a layer-by-layer basis;
• inputting, by the at least one computing device, the spatially aligned sensor-derived features and the thermal history quantifiers to a machine learning model;
• and
• predicting, by the at least one computing device, at least one part quality characteristic based on an output of the machine learning model.

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to at least:

• receive in-situ sensor data acquired from a plurality of sensors during fabrication of a part by an additive manufacturing process;
• extract sensor-derived features from the in-situ sensor data;
• predict thermal history quantifiers for the part using a physics-based thermal model;
• combine the sensor-derived features and the thermal history quantifiers as inputs to a hierarchical machine learning model, the hierarchical machine learning model comprising a first echelon that classifies a presence or absence of a fabrication defect and a second echelon that predicts a microstructure characteristic for portions of the part classified as absence of the fabrication defect;
• and
• output the predicted microstructure characteristic.