Methods and apparatus for sensor-assisted part development in additive manufacturing

Patent metadata

Field	Value
Publication number	US-12585241-B2
Application number	US-202217840386-A
Country	US
Kind code	B2
Filing date	Jun 14, 2022
Priority date	Jun 14, 2022
Publication date	Mar 24, 2026
Grant date	Mar 24, 2026

Abstract

Methods and apparatus for sensor-based part development are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to identify a reference process observable of a computer-generated part, receive input from at least one sensor during three-dimensional printing to identify an estimated process observable using feature extraction, and adjust at least one three-dimensional printing process parameter to reduce an error identified from a mismatch between the estimated process observable and the reference process observable.

First claim

What is claimed is:

1 . An apparatus, comprising:
at least one memory; instructions in the apparatus; and processor circuitry to execute the instructions to:
identify a reference process observable of a computer-generated part based on a voxelized reference map, the voxelized reference map corresponding to a mapping of voxels representative of three-dimensional (3D) units of an input image, the voxelized reference map determined based on a drill down model identifying a vertical distance from a voxel to a printing powder of a three-dimensional printer;
receive an image input from at least one sensor during three-dimensional printing;
perform feature extraction from the image input using a machine learning model to identify an estimated process observable, the machine learning model trained based on data from one or more three-dimensional printer sensors; and
adjust at least one three-dimensional printing process parameter of the three-dimensional printer to reduce an error identified from a mismatch between the estimated process observable and the reference process observable.

2 . The apparatus of claim 1 , wherein the processor circuitry is to identify the reference process observable based on a material property or a geometric feature of the computer-generated part.

3 . The apparatus of claim 1 , wherein the reference process observable or the estimated process observable is at least one of a meltpool width, a meltpool depth, a meltpool height, a temperature profile, or a cooling rate.

4 . The apparatus of claim 1 , wherein, when the reference process observable is a meltpool characteristic, the processor circuitry is to extract meltpool features, in real-time or offline, using the at least one sensor.

5 . The apparatus of claim 1 , wherein the processor circuitry is to adjust the at least one three-dimensional printing process parameter based on at least one of a real-time feedback implementation, a layer-to-layer feedback implementation, or a build-to-build feedback implementation.

6 . The apparatus of claim 5 , wherein the processor circuitry is to obtain (1) first estimated process observable information from a previous build using the build-to-build feedback implementation and (2) second estimated process observable information from a previous layer of a same build using the layer-to-layer feedback implementation.

7 . The apparatus of claim 1 , wherein the at least one three- dimensional printing process parameter includes at least one of a power, a speed, a focus, a beam shape, or an energy density.

8 . The apparatus of claim 1 , wherein the at least one sensor includes an on-axis sensor or an off-axis sensor.

9 . A method, comprising:
identifying a reference process observable of a computer-generated part based on a voxelized reference map, the voxelized reference map corresponding to a mapping of voxels representative of three-dimensional (3D) units of an input image, the voxelized reference map determined based on a drill down model identifying a vertical distance from a voxel to a printing powder of a three-dimensional printer; receiving an image input from at least one sensor during three-dimensional printing; performing feature extraction from the image input using a machine learning model to identify an estimated process observable, the machine learning model trained based on data from one or more three-dimensional printer sensors; and adjusting at least one three-dimensional printing process parameter of the three-dimensional printer to reduce an error identified from a mismatch between the estimated process observable and the reference process observable.

10 . The method of claim 9 , further including identifying the reference process observable based on a material property or a geometric feature of the computer-generated part.

11 . The method of claim 9 , wherein the reference process observable or the estimated process observable is at least one of a meltpool width, a meltpool depth, a meltpool height, a temperature profile, or a cooling rate.

12 . The method of claim 9 , further including, when the reference process observable is a meltpool characteristic, extracting meltpool features, in real-time or offline, using the at least one sensor.

13 . The method of claim 9 , further including adjusting the at least one three-dimensional printing process parameter based on at least one of a real-time feedback implementation, a layer-to-layer feedback implementation, or a build-to-build feedback implementation.

14 . The method of claim 13 , further including (1) obtaining first estimated process observable information from a previous build using the build-to-build feedback implementation and (2) obtaining second estimated process observable information from a previous layer of a same build using the layer-to-layer feedback implementation.

15 . The method of claim 9 , wherein the at least one three-dimensional printing process parameter includes at least one of a power, a speed, a focus, a beam shape, or an energy density.

16 . The method of claim 9 , wherein the at least one sensor includes an on-axis sensor or an off-axis sensor.

17 . A non-transitory computer readable storage medium comprising instructions that, when executed, cause a processor to at least:
identify a reference process observable based on a computer-generated part based on a voxelized reference map, the voxelized reference map corresponding to a mapping of voxels representative of three-dimensional (3D) units of an input image, the voxelized reference map determined based on a drill down model identifying a vertical distance from a voxel to a printing powder of a three-dimensional printer; receive an image input from at least one sensor during three-dimensional printing; perform feature extraction from the image input using a machine learning model to identify an estimated process observable, the machine learning model trained based on data from one or more three-dimensional printer sensors; and adjust at least one three-dimensional printing process parameter of the three-dimensional printer to reduce an error identified from a mismatch between the estimated process observable and the reference process observable.

18 . The non-transitory computer readable storage medium of claim 17 , wherein the processor is to identify the reference process observable based on a material property or a geometric feature of the computer-generated part.

19 . The non-transitory computer readable storage medium of claim 17 , wherein the processor is to adjust the at least one three-dimensional printing process parameter based on at least one of a real-time feedback implementation, a layer-to-layer feedback implementation, or a build-to-build feedback implementation.

20 . The non-transitory computer readable storage medium of claim 19 , wherein the processor is to obtain (1) first estimated process observable information from a previous build using the build-to-build feedback implementation and (2) second estimated process observable information from a previous layer of a same build using the layer-to-layer feedback implementation.

Assignees

• Gen Electric

Inventors

• Roychowdhury Subhrajit
• Iyer Naresh S
• Cho Sanghee
• Blom Rogier Sebastiaan
• Brunell Brent
• Ping Xiaohu
• Aramanekoppa Sharath

Classifications

• G05B2219/49023

  3-D printing, layer of powder, add drops of binder in layer, new powder · CPC title

• G06N3/08

  Learning methods · CPC title

• B33Y30/00

  Apparatus for additive manufacturing; Details thereof or accessories therefor · CPC title

• B33Y10/00

  Processes of additive manufacturing · CPC title

• B29C64/153

  using layers of powder being selectively joined, e.g. by selective laser sintering or melting · CPC title