TEMPERATURE PROFILE DEFORMATION PREDICTIONS
Examples of methods are described herein. In some examples, a method includes determining a graph representation of a three-dimensional (3D) object. In some examples, the graph representation includes nodes and edges associated with the nodes. In some examples, each node includes a temperature profile attribute. In some examples, the method includes predicting, using a machine learning model, a deformation of the 3D object based on the graph representation.
1 . A method, comprising:
determining a graph representation of a three-dimensional (3D) object, wherein the graph representation comprises nodes and edges associated with the nodes, wherein each node comprises a temperature profile attribute; and
predicting, using a machine learning model, a deformation of the 3D object based on the graph representation.
2 . The method of claim 1 , wherein the temperature profile attribute is a global factor.
3 . The method of claim 1 , wherein the temperature profile attribute comprises a vector of temperatures.
4 . The method of claim 3 , wherein the vector of temperatures comprises temperature change rates.
5 . The method of claim 3 , wherein the vector of temperatures comprises a stage duration.
6 . The method of claim 1 , further comprising encoding, using a second machine learning model, a vector of temperatures to produce the temperature profile attribute.
7 . The method of claim 6 , wherein the second machine learning model is a multilayer perceptron model.
8 . The method of claim 6 , wherein the second machine learning model is a recurrent neural network (RNN) model.
9 . The method of claim 8 , wherein the RNN model comprises a long short-term memory (LSTM) layer or a gated recurrent unit (GRU) layer.
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 deformation;
determine a graph based on the initial simulated deformation, wherein the graph comprises nodes and edges, wherein each node comprises a temperature profile attribute; and
predict a subsequent deformation based on the graph.
11 . The apparatus of claim 10 , wherein the processor is to encode a vector of temperatures to produce the temperature profile attribute.
12 . The apparatus of claim 11 , wherein the processor is to predict the subsequent deformation using a machine learning model trained with a second machine learning model to encode the vector of temperatures.
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;
encode, using a machine learning model, a vector of temperatures to produce a temperature profile attribute;
append the temperature profile attribute to the plurality of nodes of the first graph; and
predict, using a graph neural network, a second graph based on the first graph.
14 . The non-transitory tangible computer-readable medium of claim 13 , wherein the machine learning model is a multilayer perceptron model or a recurrent neural network model.
15 . The non-transitory tangible computer-readable medium of claim 13 , wherein the vector of temperatures comprises temperature change rates.