MACHINE LEARNING FEATURE FEED RATES FOR 3D PRINTING
Systems for and methods of providing a feed rate for three-dimensional printing a part are presented. The disclosed techniques include: obtaining computer readable toolpath instructions for the part, where the toolpath instructions specify a nominal feed rate for a toolpath segment and spatial toolpath data of the toolpath segment; providing an input including the spatial toolpath data to a trained machine learning system, where the trained machine learning system has been trained using training data including: training spatial toolpath data, training closed loop gain data, and training feed rate data; obtaining a revised feed rate for the toolpath segment different from the nominal feed rate for the toolpath segment, where the revised feed rate is output from the trained machine learning system; and providing revised computer readable toolpath instructions, where the revised machine learning toolpath instructions include the revised feed rate.
1 . A method of providing a feed rate for three-dimensional printing a part, the method comprising:
obtaining computer readable toolpath instructions for the part, wherein the toolpath instructions specify a nominal feed rate for a toolpath segment and spatial toolpath data of the toolpath segment;
providing an input comprising the spatial toolpath data to a trained machine learning system, wherein the trained machine learning system has been trained using training data comprising: training spatial toolpath data, training closed loop gain data, and training feed rate data;
obtaining a revised feed rate for the toolpath segment different from the nominal feed rate for the toolpath segment, wherein the revised feed rate is output from the trained machine learning system; and
providing revised computer readable toolpath instructions, wherein the revised machine learning toolpath instructions comprise the revised feed rate.
2 . The method of claim 1 , further comprising printing the part using the revised computer readable toolpath instructions.
3 . The method of claim 1 , wherein the input further comprises a predetermined closed loop gain value.
4 . The method of claim 1 , wherein the training data comprises data that is specific to the part, the method further comprising training the trained machine learning system using the training data.
5 . The method of claim 1 , wherein the toolpath instructions specify an anisotropic region of the part that comprises the toolpath segment.
6 . The method of claim 5 , wherein the anisotropic region comprises at least one of: an overhang, an outer perimeter, or a thin feature.
7 . The method of claim 1 , wherein the spatial toolpath data for the toolpath segment comprises at least one of: a distance from an edge of the part, a distance from a corner of the part, or a rounding radius of the toolpath segment.
8 . The method of claim 1 , wherein the training data further comprises training overhang angle data.
9 . The method of claim 1 , wherein the training feed rate data comprises an induced feed rate variation.
10 . The method of claim 9 , wherein the induced feed rate variation comprises a periodic induced feed rate variation along an edge of a feature.
11 . A system for providing a feed rate for three-dimensional printing a part, the system comprising an electronic processor and a persistent memory storing non-transitory computer readable instructions that, when executed by the electronic processor, configure the electronic processor to perform actions comprising:
obtaining computer readable toolpath instructions for the part, wherein the toolpath instructions specify a nominal feed rate for a toolpath segment and spatial toolpath data of the toolpath segment;
providing an input comprising the spatial toolpath data to a trained machine learning system, wherein the trained machine learning system has been trained using training data comprising: training spatial toolpath data, training closed loop gain data, and training feed rate data;
obtaining a revised feed rate for the toolpath segment different from the nominal feed rate for the toolpath segment, wherein the revised feed rate is output from the trained machine learning system; and
providing revised computer readable toolpath instructions, wherein the revised machine learning toolpath instructions comprise the revised feed rate.
12 . The system of claim 11 , wherein the actions further comprise printing the part using the revised computer readable toolpath instructions.
13 . The system of claim 11 , wherein the input further comprises a predetermined closed loop gain value.
14 . The system of claim 11 , wherein the training data comprises data that is specific to the part, the actions further comprising training the trained machine learning system using the training data.
15 . The system of claim 11 , wherein the toolpath instructions specify an anisotropic region of the part that comprises the toolpath segment.
16 . The system of claim 15 , wherein the anisotropic region comprises at least one of: an overhang, an outer perimeter, or a thin feature.
17 . The system of claim 11 , wherein the spatial toolpath data for the toolpath segment comprises at least one of: a distance from an edge of the part, a distance from a corner of the part, or a rounding radius of the toolpath segment.
18 . The system of claim 11 , wherein the training data further comprises training overhang angle data.
19 . The system of claim 11 , wherein the training feed rate data comprises an induced feed rate variation.
20 . The system of claim 19 , wherein the induced feed rate variation comprises a periodic induced feed rate variation along an edge of a feature.