In-situ monitoring system assisted material and parameter development for additive manufacturing

Patent metadata

Field	Value
Publication number	US-11472115-B2
Application number	US-201916360180-A
Country	US
Kind code	B2
Filing date	Mar 21, 2019
Priority date	Mar 21, 2019
Publication date	Oct 18, 2022
Grant date	Oct 18, 2022

Abstract

According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a parameter development module comprising a processor, a defined geometry for one or more parts, wherein the parts are manufactured with an additive manufacturing machine, and wherein a stack is formed from one or more parts; fabricating the one or more parts with the additive manufacturing machine based on a first parameter set; collecting in-situ monitoring data from one or more in-situ monitoring systems of the additive manufacturing machine for one or more parts; determining whether each stack should receive an additional part based on an analysis of the collected in-situ monitoring data; and fabricating each additional part based on the determination the stack should receive the additional part. Numerous other aspects are provided.

First claim

The invention claimed is:

1. A method comprising:
receiving, via a communication interface of a parameter development module comprising a processor, a defined geometry for a plurality of parts, each part to be formed of a plurality of layers, wherein the parts are manufactured with an additive manufacturing machine, and wherein the parts are to be manufactured in stacks, each stack including at least two parts, the plurality of parts arranged as a plurality of stacks;
initiating fabrication of the plurality of parts in the plurality of stacks with the additive manufacturing machine based on a first parameter set;
collecting in-situ monitoring data from one or more in-situ monitoring systems of the additive manufacturing machine for the plurality of parts during the fabrication;
determining whether each stack should receive an additional part to be manufactured based on an analysis of the collected in-situ monitoring data, the determination whether each stack should receive the additional part based on at least one of the collected in-situ monitoring data or a predictive material model; and
fabricating each additional part based on the determination that the respective stack should receive the additional part.

2. The method of claim 1 , wherein the collected in-situ monitoring data includes at least one of laser-material and laser-environment interactions.

3. The method of claim 1 , wherein the collected in-situ monitoring data includes a melt-pool response.

4. The method of claim 1 , wherein it is determined whether each stack should receive the additional part when the collected in-situ monitoring data falls one of inside or outside of a predetermined value or range of values.

5. The method of claim 1 , wherein the first parameter set is based on the predictive material model.

6. The method of claim 5 , wherein fabrication of the predictive material model further comprises:
executing at least one of a bead-on-powder and bead-on-plate process with an initial parameter set to output one or more melt-pool tracks;
collecting in-situ monitoring data from the one or more melt-pool tracks;
executing an in-situ analysis of the one or more melt-pool tracks with one or more in-situ monitoring systems of the additive manufacturing machine based on the collected in-situ monitoring data; and
generating at least one predictive material model based on at least one of the in-situ monitoring data analysis and one or more tests or measurements performed on the one or more melt-pool tracks.

7. The method of claim 1 , further comprising:
generating a library of response surfaces by mapping the analysis of the in-situ monitoring data to one or more tests, measurements or results.

8. The method of claim 1 , wherein the predictive material model is at least one of a machine learning model and a physics-based model.

9. The method of claim 1 , wherein the first parameter set includes at least one of a laser power, a laser speed, a laser beam diameter, and hatch-spacing.

10. A system comprising:
a parameter development module;
a parameter development processor; and
a memory storing program instructions, the parameter development processor and the parameter development module operative with the program instructions to perform functions as follows:
receive, via a communication interface of the parameter development module, a defined geometry for a plurality of parts, each part to be formed of a plurality of layers, wherein the parts are manufactured with an additive manufacturing machine, and wherein the parts are to be manufactured in stacks, each stack including at least two parts, the plurality of parts arranged as a plurality of stacks;
initiating fabrication of the plurality of parts in the plurality of stacks with the additive manufacturing machine based on a first parameter set;
collect in-situ monitoring data from one or more in-situ monitoring systems of the additive manufacturing machine for the plurality of parts during the fabrication;
determine whether each stack should receive an additional part to be manufactured based on an analysis of the collected in-situ monitoring data, the determining whether each stack should receive the additional part based on at least one of the collected in-situ monitoring data or a predictive material model; and
fabricate each additional part based on the determination that the respective stack should receive the additional part.

11. The system of claim 10 , wherein the collected in-situ monitoring data includes at least one of laser-material, laser-environment interactions, and a melt-pool response.

12. The system of claim 10 , wherein it is determined whether each stack should receive the additional part when the collected in-situ monitoring data falls one of inside or outside of a predetermined value or range of values.

13. The system of claim 10 , wherein the first parameter set is based on the predictive material model.

14. The system of claim 13 , wherein fabrication of the predictive material model further comprises program instructions to perform functions as follows:
execute at least one of a bead-on-powder and bead-on-plate process with an initial parameter set to output one or more melt-pool tracks;
collect in-situ monitoring data from the one or more melt-pool tracks;
execute an in-situ analysis of the one or more melt-pool tracks with one or more in-situ monitoring systems of the additive manufacturing machine based on the collected in-situ monitoring data; and
generate at least one predictive material model based on at least one of the in-situ monitoring analysis and one or more tests or measurements performed on the one or more melt-pool tracks.

15. A non-transitory computer-readable medium storing instructions that, when executed by a computer processor, cause the computer processor to perform a method comprising:
receiving, via a communication interface of a parameter development module comprising a processor, a defined geometry for a plurality of parts, each part to be formed of a plurality of layers, wherein the parts are manufactured with an additive manufacturing machine, and wherein the parts are to be manufactured in stacks, each stack including at least two parts, the plurality of parts arranged as a plurality of stacks;
initiating fabrication of the plurality of parts in the plurality of stacks with the additive manufacturing machine based on a first parameter set;
collecting in-situ monitoring data from one or more in-situ monitoring systems of the additive manufacturing machine for the plurality of parts during the fabrication;
determining whether each stack should receive an additional part to be manufactured based on an analysis of the collected in-situ monitoring data, the determining whether each stack should receive the additional part based on at least one of the collected in-situ monitoring data or a predictive material model; and
fabricating each additional part based on the determination that the respective stack should receive the additional part.

16. The medium of claim 15 , wherein it is determined whether each stack should receive the additional part when the collected in-situ monitoring data falls one of inside or outside of a predetermined value or range of values.

17. The method of claim 1 , wherein the predictive material model is at least one of a physics-based model or a machine learning-based model.

18. The method of claim 1 , wherein the plurality of parts and the plurality of stacks include a first part and a second part in a first stack, and wherein the second

Assignees

• Gen Electric

Inventors

• Gupta Vipul Kumar
• Chennimalai Kumar Natarajan
• Vinciquerra Anthony Joseph
• Dial Laura Cerully
• Dheeradhada Voramon Supatarawanich
• Hanlon Timothy
• Salasoo Lembit
• Ping Xiaohu
• Roychowdhury Subhrajit
• Gambone Justin John

Classifications

• B33Y50/00 Primary

  Data acquisition or data processing for additive manufacturing · CPC title

• B29C64/153 Primary

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

• B22F12/90

  Means for process control, e.g. cameras or sensors · CPC title

• B22F10/85

  for controlling or regulating additive manufacturing processes · CPC title

• B22F10/31

  Calibration of process steps or apparatus settings, e.g. before or during manufacturing · CPC title