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

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 part is made from a different feature type than the first part in the first stack.

19. The system of claim 10 , 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 part is made from a different feature type than the first part in the first stack.

20. The medium of claim 15 , 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 part is made from a different feature type than the first part in the first stack.