POWDER DEGRADATION PREDICTIONS
Examples of methods are described. In some examples, a method includes determining a quantification of a spatial neighborhood of a voxel of a build volume. In some examples, the method includes predicting, using a machine learning model, a manufacturing powder degradation based on the quantification and a position of the voxel.
1 . A method, comprising:
determining a quantification of a spatial neighborhood of a voxel of a build volume; and
predicting, using a machine learning model, a manufacturing powder degradation based on the quantification and a position of the voxel.
2 . The method of claim 1 , wherein determining the quantification comprises performing a convolution of the spatial neighborhood of the voxel.
3 . The method of claim 2 , wherein the convolution is a gaussian convolution.
4 . The method of claim 2 , wherein the convolution is performed at a first length scale to produce the quantification.
5 . The method of claim 4 , further comprising:
performing a second convolution at a second length scale to produce a second quantification; and
performing a third convolution at a third length scale to produce a third quantification, where the first length scale, the second length scale, and the third length scale are unequal.
6 . The method of claim 5 , wherein predicting the manufacturing powder degradation is further based on the second quantification and the third quantification.
7 . The method of claim 1 , wherein the position comprises an location, a location, and a location, and wherein predicting the manufacturing powder degradation is further based on a build height.
8 . The method of claim 1 , wherein predicting the manufacturing powder degradation comprises predicting a stress based on the quantification and the position.
9 . The method of claim 8 , wherein predicting the manufacturing powder degradation comprises determining a powder quality metric based on the stress.
10 . An apparatus, comprising:
a memory; and
a processor coupled to the memory, wherein the processor is to:
determine a voxel representing a portion of a build of manufacturing powder;
performing a convolution based on neighboring voxels of the voxel to produce a quantification; and
determine a powder quality metric based on the quantification.
11 . The apparatus of claim 10 , wherein the convolution is performed at a first length scale, and wherein the processor is to perform a second convolution at a second length scale that is different from the first length scale to produce a second quantification.
12 . The apparatus of claim 11 , wherein the processor is to determine the powder quality metric based on the quantification and the second quantification.
13 . A non-transitory tangible computer-readable medium comprising instructions when executed cause a processor of an electronic device to:
voxelize a manufacturing build to produce voxels;
determine, for a first voxel of the voxels, a quantification based on a length scale; and
predict, using a machine learning model, manufacturing powder degradation based on the quantification, a position, and a build height.
14 . The non-transitory tangible computer-readable medium of claim 13 , wherein the instructions when executed cause the processor of the electronic device to perform a gaussian convolution at the length scale to determine the quantification for the first voxel, wherein the length scale indicates a spherical neighborhood of the voxels around the first voxel.
15 . The non-transitory tangible computer-readable medium of claim 13 , wherein the manufacturing powder degradation is a voxel stress of the first voxel.