OBJECT SINTERING PREDICTIONS
Examples of methods are described herein. In some examples, a method includes determining a graph representation of a three-dimensional (3D) object based on a voxel representation of the 3D object. In some examples, the graph representation includes nodes corresponding to voxels of the voxel representation and edges associated with the nodes. In some examples, the method includes predicting, using a machine learning model, a sintering state of the 3D object based on the graph representation.
1 . A method, comprising:
determining a graph representation of a three-dimensional (3D) object based on a voxel representation of the 3D object, wherein the graph representation comprises nodes corresponding to voxels of the voxel representation and edges associated with the nodes; and
predicting, using a machine learning model, a sintering state of the 3D object based on the graph representation.
2 . The method of claim 1 , wherein the graph representation further comprises a global factor.
3 . The method of claim 1 , wherein determining the graph representation comprises filtering voxel vertices to produce the nodes.
4 . The method of claim 1 , wherein determining the graph representation comprises determining a node attribute value for each of the nodes.
5 . The method of claim 4 , wherein the node attribute value indicates node mobility.
6 . The method of claim 1 , wherein determining the graph representation comprises determining the edges based on a threshold distance.
7 . The method of claim 1 , wherein determining the graph representation comprises determining an edge attribute value for each of the edges.
8 . The method of claim 1 , further comprising:
updating the graph representation over a time increment of a sintering procedure; and
predicting, using the machine learning model, a second sintering state of the 3D object based on the updated graph representation.
9 . The method of claim 1 , further comprising voxelizing a 3D object model to produce the voxel representation of the 3D object.
10 . An apparatus, comprising:
a memory;
a processor in electronic communication with the memory, wherein the processor is to:
simulate sintering of voxels to produce an initial simulated sintering state;
determine a graph based on the initial simulated sintering state, wherein the graph comprises nodes and edges; and
predict a subsequent sintering state based on the graph.
11 . The apparatus of claim 10 , wherein each of the nodes comprises an attribute value indicating a node type.
12 . The apparatus of claim 10 , wherein the processor is to predict the subsequent sintering state using a machine learning model trained with an anchoring loss.
13 . A non-transitory tangible computer-readable medium comprising instructions when executed cause a processor of an electronic device to:
generate, based on voxels representing a three-dimensional (3D) object model, a plurality of nodes of a first graph corresponding to a first time;
determine a plurality of node attributes for the plurality of nodes;
generate a plurality of edges of the first graph;
determine a plurality of edge attributes for the plurality of edges; and
predict, using a graph neural network, a second graph based on the first graph, wherein the second graph indicates a sintering state corresponding to a second time.
14 . The non-transitory tangible computer-readable medium of claim 13 , wherein the graph neural network is trained based on a deformation loss and a stress loss.
15 . The non-transitory tangible computer-readable medium of claim 14 , wherein the graph neural network is trained based on an anchoring loss.