Methods and apparatus for sensor-assisted part development in additive manufacturing using a machine learning model

A method for voxelized reference generation, comprising:

• translating at least one user-defined material property selection into a desired process observable, the desired process observable including a meltpool property;
• performing voxel-based autozoning of an input part geometry, the input part geometry based on a computer-generated design;
• outputting a voxelized reference map for the input part geometry based on the desired process observable and the voxel-based autozoning;
• and
• training a machine learning model to predict the desired process observable based on input from one or more sensors of a three-dimensional printer;
• and
• updating, based on the machine learning model, at least one of a layer-to-layer or a build-to-build algorithm for energy density based on one or more identified desired process observable errors.

A non-transitory computer readable storage medium comprising instructions that, when executed, cause a processor to at least:

• translate at least one user-defined material property selection into a desired process observable, the desired process observable including a meltpool property;
• perform voxel-based autozoning of an input part geometry, the input part geometry based on a computer-generated design;
• output a voxelized reference map for the input part geometry based on the desired process observable and the voxel-based autozoning;
• and
• train a machine learning model to predict the desired process observable based on input from one or more sensors of a three-dimensional printer;
• and
• update, based on the machine learning model, at least one of a layer-to-layer or a build-to-build algorithm for energy density based on one or more identified desired process observable errors.