Quality metric for predictive defect model for multi-laser powder bed fusion additive manufacturing
A process for an external optimization framework utilizing a defect model for multi-laser additive manufacturing of a part including determining a scalar metric for the part; employing the scalar metric in the defect model; providing at least one output from the defect model to the external optimization framework; and optimizing the powder bed fusion additive manufacturing process for the part with the external optimization framework.
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 determine a scalar metric for the part;
an instruction to employ the scalar metric in a defect model;
an instruction to provide at least one output from the defect model to an external optimization framework;
an instruction to optimize the powder bed fusion additive manufacturing process for the part with the external optimization framework; and
an instruction to determine the scalar metric used to interface with the external optimization framework, wherein the scalar metric comprises the ratio of a number of defected nodes to total nodes used in the defect model.
2 . The system for additive manufacturing according to claim 1 , wherein the scalar metric describes the overall quality of the part fabricated by powder bed fusion additive manufacturing with respect to predicted defects.
3 . The system for additive manufacturing according to claim 1 , wherein the scalar metric is configured as a single number employed as an objective or as a constraint in the optimization of the powder bed fusion additive manufacturing process for the part.
4 . The system for additive manufacturing according to claim 1 , further comprising:
an instruction to discretize the part in space in preparation for determining the scalar metric.
5 . The system for additive manufacturing according to claim 1 , further comprising:
an instruction to discretize the part by a set of nodes used for a finite difference computation.
6 . The system for additive manufacturing according to claim 1 , further comprising:
an instruction to compute the scalar metric from discretizations, such as finite volume or finite element.
7 . The system for additive manufacturing according to claim 1 , further comprising:
an instruction to compute the scalar metric as a sum of a total number of nodes predicted to contain defects divided by a total number of nodes in the part.
8 . The system for additive manufacturing according to claim 1 , further comprising:
an instruction to introduce a location criticality weighting coefficient configured to discourage defects in critical regions of a part.
9 . The system for additive manufacturing according to claim 8 , wherein the location criticality weighting coefficient is proportional to stress.
10 . A process for an external optimization framework utilizing a defect model for multi-laser additive manufacturing of a part comprising:
determining a scalar metric for the part;
employing the scalar metric in the defect model;
providing at least one output from the defect model to the external optimization framework;
optimizing a powder bed fusion additive manufacturing process for the part with the external optimization framework; and
determining the scalar metric used to interface with the external optimization framework, wherein the scalar metric comprises the ratio of a number of defected nodes to total nodes used in the defect model.
11 . The process of claim 10 , wherein the scalar metric describes the overall quality of the part fabricated by the powder bed fusion additive manufacturing process for the part with the external optimization framework with respect to predicted defects.
12 . The process of claim 10 , further comprising:
configuring the scalar metric as a single number employed as an objective or as a constraint in the optimization of the powder bed fusion additive manufacturing process for the part with the external optimization framework.
13 . The process of claim 10 , further comprising:
discretizing the part in space in preparation for determining the scalar metric.
14 . The process of claim 10 , further comprising:
discretizing the part by a set of nodes used for a finite difference computation.
15 . The process of claim 10 , further comprising:
computing the scalar metric as a sum of a total number of nodes predicted to contain defects divided by a total number of nodes in the part.
16 . The process of claim 10 , further comprising:
introducing a location criticality weighting coefficient configured to discourage defects in critical regions of a part, wherein the location criticality weighting coefficient is proportional to stress.
17 . 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 determine a scalar metric for the part;
an instruction to employ the scalar metric in a defect model;
an instruction to provide at least one output from the defect model to an external optimization framework;
an instruction to optimize the powder bed fusion additive manufacturing process for the part with the external optimization framework; and
an instruction to determine the defect quality metric as a ratio of a sum of fractional defect density at each defected node to total nodes used in the defect model.
18 . A process for an external optimization framework utilizing a defect model for multi-laser additive manufacturing of a part comprising:
determining a scalar metric for the part;
employing the scalar metric in the defect model;
providing at least one output from the defect model to the external optimization framework;
optimizing a powder bed fusion additive manufacturing process for the part with the external optimization framework; and
determining the defect quality metric as a ratio of a sum of fractional defect density at each defected node to total nodes used in the defect model.