System and method for prediction binder jet distortion and variability using machine learning

A system for predicting distortion of a part during sintering in an additive process comprising:

• a computing device comprising a processor and a non-transitory processor readable medium storing instructions that when executed by the processor cause the computing device to:
• receive a computerized representation of a complex geometric part;
• discretize the computerized representation of the complex geometric part into a plurality of elements;
• process the plurality of elements of the computerized representation of the complex geometric part with a machine-learning model, the machine-learning model is configured to predict a distorted geometry of the complex geometric part in response to a sintering process, wherein the machine-learning model is trained to predict distortion of a set of primitive geometric coupons represented by image data fed into the machine-learning model during training, the set of primitive geometric coupons having fewer geometries than the complex geometric part, the complex geometric part comprises a plurality of geometries corresponding to geometries associated with the set of primitive geometric coupons;
• and
• generate a computerized representation of the predicted distorted geometry of the complex geometric part.

A computer program product for predicting distortion of a part during sintering in an additive process, the computer program product comprising machine-readable instructions stored on a non-transitory computer readable memory, which when executed by a computing device, causes the computing device to carry out steps comprising:

• receiving a computerized representation of a complex geometric part;
• discretizing the computerized representation of the complex geometric part into a plurality of elements;
• processing the plurality of elements of the computerized representation of the complex geometric part with a machine-learning model, the machine-learning model configured to predict a distorted geometry of the complex geometric part in response to a sintering process, wherein the machine-learning model is trained to predict distortion of a set of primitive geometric coupons represented by image data fed into the machine-learning model during training, the set of primitive geometric coupons having fewer geometries than the complex geometric part, the complex geometric part comprises a plurality of geometries corresponding to geometries associated with the set of primitive geometric coupons;
• and
• generating a computerized representation of the predicted distorted geometry of the complex geometric part.