Physics-guided thermal history control and microstructure prediction for laser powder bed fusion additive manufacturing

A computer-implemented method, comprising:

• receiving, by at least one computing device, a digital representation of a part geometry for a part to be fabricated by a laser powder bed fusion (LPBF) process;
• predicting, by the at least one computing device, a thermal history of the part under nominal processing conditions using a thermal model, wherein the thermal model discretizes the part geometry into a plurality of nodes connected by edges to form a network graph, constructs a discrete Laplacian matrix from the network graph, and computes eigenvalues and eigenvectors of the Laplacian matrix;
• determining, by the at least one computing device, a target thermal history for the part based on a control strategy;
• for each of a plurality of layers of the part, iteratively simulating, by the at least one computing device, the thermal history of the part at a plurality of candidate processing parameter levels over a prediction horizon of future layers using the thermal model, and selecting a candidate processing parameter level that minimizes a deviation between the simulated thermal history and the target thermal history over the prediction horizon;
• and
• outputting, by the at least one computing device, a layer-by-layer process parameter plan specifying a processing parameter for each of the plurality of layers of the part based on the selected candidate processing parameter levels.

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 a digital representation of a part geometry for a part to be fabricated by a laser powder bed fusion (LPBF) process;
• predict a thermal history of the part under nominal processing conditions using a thermal model, wherein the thermal model discretizes the part geometry into a plurality of nodes connected by edges to form a network graph, constructs a discrete Laplacian matrix from the network graph, and computes eigenvalues and eigenvectors of the Laplacian matrix;
• determine a target thermal history for the part based on a control strategy;
• for each of a plurality of layers of the part, iteratively simulate the thermal history of the part at a plurality of candidate processing parameter levels over a prediction horizon of future layers using the thermal model, and select a candidate processing parameter level that minimizes a deviation between the simulated thermal history and the target thermal history over the prediction horizon;
• and
• output a layer-by-layer process parameter plan specifying a processing parameter for each of the plurality of layers of the part based on the selected candidate processing parameter levels.