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