Uncertainty quantification or predictive defect model for multi-laser powder bed fusion additive manufacturing

Patent metadata

Field	Value
Publication number	US-12485489-B2
Application number	US-202318201787-A
Country	US
Kind code	B2
Filing date	May 25, 2023
Priority date	May 25, 2023
Publication date	Dec 2, 2025
Grant date	Dec 2, 2025

Abstract

A process for uncertainty quantification for a predictive defect model for multi-laser additive manufacturing of a part including executing computational fluid dynamics modeling of a gas flow in an additive manufacturing machine manufacturing chamber; assigning a spatter particle size, velocity and direction relative to a melt pool on a powder bed disposed on a build plate within the manufacturing chamber; executing computational fluid dynamics post processing for spatter particle tracking; predicting a spatter particle landing pattern; feeding the spatter particle landing pattern prediction into a defect model; producing a layer thickness map, the layer thickness map configured to demonstrate a location of locally thicker layers on the part; and predicting defect location and density to accumulate lack-of-fusion risk as a function of part placement, orientation, and scan strategy.

First claim

What is claimed is:

1 . A system comprising a computer readable storage device readable by the system, tangibly embodying a program having a set of instructions executable by the system to perform the following steps for predicting defects in powder bed fusion additive manufacturing process for a part, the set of instructions comprising:
an instruction to execute computational fluid dynamics modeling of a gas flow in an additive manufacturing machine manufacturing chamber; an instruction to assign a spatter particle size, velocity and direction relative to a melt pool on a powder bed disposed on a build plate within the manufacturing chamber; an instruction to execute computational fluid dynamics post processing for spatter particle tracking; an instruction to predict a spatter particle landing pattern; an instruction to feed the spatter particle landing pattern prediction into a defect model; an instruction to produce a layer thickness map, the layer thickness map configured to demonstrate a location of locally thicker layers on the part; and an instruction to predict defect location and density to accumulate lack-of-fusion risk as a function of part placement, orientation, and scan strategy.

2 . The system for additive manufacturing according to claim 1 , wherein the computational fluid dynamics modeling of the gas flow predicts a flow field inside the chamber.

3 . The system for additive manufacturing according to claim 1 , wherein the spatter particle includes a vector having velocity and direction influenced by the gas flow and laser/melt pool/powder bed dynamics.

4 . The system for additive manufacturing according to claim 1 , wherein the tracking of the spatter particle includes tracking the spatter particle within the chamber as the spatter particle travels into an un-melted powder of the particle bed.

5 . The system for additive manufacturing according to claim 1 , wherein the gas flow influences the spatter particle and a plume formed within the chamber, wherein the gas flow entrains the spatter particle and influences a trajectory of the spatter particle.

6 . The system for additive manufacturing according to claim 1 , wherein an accumulation of spatter particles are formed into a representative spatter particle landing pattern.

7 . The system for additive manufacturing according to claim 1 , further comprising:
an instruction to integrate spatter risk by controlling at least one laser to move the melt pool/spatter pattern to a location that reduces formation of defects.

8 . The system for additive manufacturing according to claim 1 , further comprising:
an instruction to include a representation of spatter accumulation by local thickness variation in the defect model.

9 . The system for additive manufacturing according to claim 1 , further comprising:
an instruction to provide local variation zones to the defect model through boundary polygons for each layer.

10 . The system for additive manufacturing according to claim 1 , further comprising:
an instruction to include a nominal additive manufacturing build parameter as an input to the defect model.

11 . The system for additive manufacturing according to claim 1 , wherein a local increase of layer thickness is responsive to a lack of fusion in the powder bed.

12 . The system for additive manufacturing according to claim 11 , wherein the local increase of layer thickness is responsive to at least one of a spatter particle landing on the powder bed and a damaged recoater blade.

13 . The system for additive manufacturing according to claim 1 , wherein the spatter particle landing pattern is configured representative of various scan angle directions relative to the gas flow, wherein the scan angle is selected from the group consisting of 0 degrees, −15 degrees, −30 degrees, −45 degrees, and −60 degrees.

14 . A process for uncertainty quantification for a predictive defect model for multi-laser additive manufacturing of a part comprising:
executing computational fluid dynamics modeling of a gas flow in an additive manufacturing machine manufacturing chamber; assigning a spatter particle size, velocity and direction relative to a melt pool on a powder bed disposed on a build plate within the manufacturing chamber; executing computational fluid dynamics post processing for spatter particle tracking; predicting a spatter particle landing pattern; feeding the spatter particle landing pattern prediction into a defect model; producing a layer thickness map, the layer thickness map configured to demonstrate a location of locally thicker layers on the part; and predicting defect location and density to accumulate lack-of-fusion risk as a function of part placement, orientation, and scan strategy.

15 . The process of claim 14 , further comprising:
integrating a spatter risk by controlling at least one laser to move the melt pool/spatter pattern to a location that reduces formation of defects.

16 . The process of claim 14 , further comprising:
including a representation of spatter accumulation by local thickness variation in the defect model.

17 . The process of claim 14 , further comprising:
providing local variation zones into the defect model through boundary polygons for each layer.

18 . The process of claim 14 , further comprising:
including a nominal additive manufacturing build parameter as an input to the defect model.

19 . The process of claim 14 , wherein the gas flow influences the spatter particle and a plume formed within the chamber, wherein the gas flow entrains the spatter particle and influence a trajectory of the spatter particle.

20 . The process of claim 14 , wherein a local increase of layer thickness is responsive to a lack of fusion in the powder bed; wherein the local increase of layer thickness is responsive to at least one of a spatter particle landing on the powder bed and a damaged recoater blade.

Assignees

• Raytheon Tech Corp
• Rtx Corp

Inventors

• Anahid Masoud
• Lynch Matthew E
• Macdonald Malcolm P
• Acharya Ranadip
• Fisher Brian A

Classifications

• B22F10/28 Primary

  Powder bed fusion, e.g. selective laser melting [SLM] or electron beam melting [EBM] · CPC title

• B22F12/58

  for changing the material composition, e.g. by mixing · CPC title

• B22F10/37

  of powder bed aspects, e.g. density · CPC title

• B33Y50/02

  for controlling or regulating additive manufacturing processes · CPC title

• Y02P10/25

  Process efficiency · CPC title