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EP-4468193-B1 - QUALITY METRIC FOR PREDICTIVE DEFECT MODEL FOR MULTI-LASER POWDER BED FUSION ADDITIVE MANUFACTURING

EP4468193B1EP 4468193 B1EP4468193 B1EP 4468193B1EP-4468193-B1

Inventors

  • ANAHID, MASOUD
  • Lynch, Matthew E.
  • SAKTI, ABDELILAH

Dates

Publication Date
20260506
Application Date
20240315

Claims (13)

  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 a multi-laser powder bed fusion additive manufacturing process for a blade or vane (48), the set of instructions comprising: an instruction to determine a scalar metric (38) for the blade or vane (16), wherein the scalar metric (38) describes the overall quality of the blade or vane (16) fabricated by powder bed fusion additive manufacturing with respect to predicted defects (42); an instruction to introduce into the scalar metric a location criticality weighting coefficient configured to discourage defects (42) in critical regions of the blade or vane (16), wherein the criticality weighting coefficient is higher proximate a root portion (50) than at a tip portion (54) of the blade or vane (48); an instruction to employ the scalar metric (38) in a defect model (32); an instruction to provide at least one output (39) from the defect model (32) to an external optimization framework (34); and an instruction to optimize the powder bed fusion additive manufacturing process for the blade or vane (16) with the external optimization framework (34).
  2. The system for additive manufacturing according to claim 1, wherein the scalar metric (38) 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 blade or vane (16).
  3. The system for additive manufacturing according to claim 1 or 2, further comprising: an instruction to discretize the blade or vane (16) in space in preparation for determining the scalar metric (38); and/or an instruction to discretize the blade or vane (16) by a set of nodes (44) used for a finite difference computation; and/or an instruction to compute the scalar metric (38) from discretizations, such as finite volume or finite element.
  4. The system for additive manufacturing according to any of claims 1 to 3, further comprising: an instruction to compute the scalar metric (38) as a sum of a total number of nodes (46) predicted to contain defects (42) divided by a total number of nodes (44) in the blade or vane (16).
  5. The system for additive manufacturing according to any of claims 1 to 4, further comprising: an instruction to determine the scalar metric (38) used to interface with the external optimization framework (34), wherein the scalar metric (38) comprises the ratio of a number of defected nodes (46) to total nodes (44) used in the defect model (32).
  6. The system for additive manufacturing according to any of claims 1 to 5, further comprising: an instruction to determine the defect quality metric (38) as a ratio of a sum of fractional defect density at each defected node (46) to total nodes (44) used in the defect model (32).
  7. The system for additive manufacturing according to claim 6, wherein the location criticality weighting coefficient is proportional to stress.
  8. A process for an external optimization framework (34) utilizing a defect model (32) for multi-laser powder bed fusion additive manufacturing of a blade or vane (48) comprising: determining a scalar metric (38) for the blade or vane (16) wherein the scalar metric (38) describes the overall quality of the blade or vane (16) fabricated by powder bed fusion additive manufacturing with respect to predicted defects (42); introducing into the scalar metric a location criticality weighting coefficient configured to discourage defects (42) in critical regions of the blade or vane (16), wherein the criticality weighting coefficient is higher proximate a root portion (50) than at a tip portion (54) of the blade or vane (48); employing the scalar metric (38) in the defect model (32); providing at least one output (39) from the defect model (32) to the external optimization framework (34); and optimizing the powder bed fusion additive manufacturing process for the blade or vane (16) with the external optimization framework (34).
  9. The process of claim 8 , further comprising: configuring the scalar metric (38) 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 blade or vane (16).
  10. The process of any of claims 8 or 9, further comprising: discretizing the blade or vane (16) in space in preparation for determining the scalar metric (38); and/or discretizing the blade or vane (16) by a set of nodes (44) used for a finite difference computation.
  11. The process of any of claims 8 to 10, further comprising: computing the scalar metric (38) as a sum of a total number of nodes (46) predicted to contain defects (42) divided by a total number of nodes (44) in the blade or vane (16); and/or determining the scalar metric (38) used to interface with the external optimization framework (34), wherein the scalar metric (38) comprises the ratio of a number of defected nodes (46) to total nodes (44) used in the defect model (32).
  12. The process of any of claims 8 to 11, further comprising: determining the defect quality metric (38) as a ratio of a sum of fractional defect density at each defected node (46) to total nodes (44) used in the defect model (32).
  13. The process of any of claims 8 to 12, wherein the location criticality weighting coefficient is proportional to stress.

Description

The present disclosure relates generally to additive manufacturing, and more specifically to a process for determining a scalar metric that describes the overall quality of a part fabricated by additive manufacturing operations. Additive manufacturing is a process that is utilized to create components by applying sequential material layers, with each layer being applied to the previous material layer. As a result of the iterative, trial and error, construction process, multiple different parameters affect whether an end product created using the additive manufacturing process includes flaws or is within acceptable tolerances of a given part. Typically, components created using an additive manufacturing process are designed iteratively, by adjusting one or more parameters each iteration and examining the results to determine if the results have the required quality. Multi-laser additive manufacturing (AM) technology is a promising process to increase allowable part size and rate of production. However, multiple lasers in additive systems could add further complications and challenges to material quality. There is no known tool to predict defect formation and dependency to process parameters for multi-laser applications. It is known how to predict defect type, density and location at the part level under a single laser operation. An example can be the teaching in US patent 10,252,512. The scientific publication by Zhou (Accelerating extrusion-based additive manufacturing optimization processes with surrogate-based multi-fidelity models, Int J Adv Manuf Technol 103, 4071-4083 (2019)), reports a study, in which a low-fidelity numerical simulation predictive model and a high-fidelity experimental model were combined to iteratively optimize the additive manufacturing process. Although the proposed method was initially targeted for extrusion-based additive manufacturing processes, it was also verified with various practical additive manufacturing optimization problems. It was demonstrated that the proposed optimization algorithm outperformed traditional optimization algorithms by reducing the optimization cost. The scientific publication by Brock Partee (Selective Laser Sintering Process Optimization for Layered Manufacturing of CAPA 6501 Polycaprolactone Bone Tissue Engineering Scaffolds, J. Manuf. Sci. Eng. May 2006, 128(2): 531-540) reports on the factorial design of experimental procedures that allows to achieve optimal selective laser sintering (SLS) processing parameters for polycaprolactone, and the subsequent characterization of scaffolds built by SLS using the optimal parameters. What is not well known is the determination of a quality metric associated with predicted defects in parts. Multi-laser additive manufacturing, owing to multiple lasers is capable of producing different types of defects such as lack of fusion and keyhole porosity. As the number of lasers acting simultaneously increases, the likelihood of multi-laser interaction goes up. What is needed is a process for determining a quality metric associated with types of defects in components produced by multi-laser powder bed fusion additive manufacturing (PBFAM). The invention is described in the enclosed set of claims. In accordance with the present disclosure, there is provided 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; and an instruction to optimize the powder bed fusion additive manufacturing process for the part with the external optimization framework. Particular embodiments may include at least one of, or a plurality of, the following optional features. These features may be included separately from each other or in combination with each other, unless specified otherwise. A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the scalar metric describes the overall quality of the part fabricated by powder bed fusion additive manufacturing with respect to predicted defects. A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include 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. A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the system for additive manufacturing further comprising an instruction to discretize the part in space in preparation for determining the scalar m