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Island scanning pattern optimization for residual deformation mitigation in laser powder bed fusion via sequential inherent strain method and sensitivity analysis

Journal Article · · Additive Manufacturing
 [1];  [2];  [3];  [4];  [4];  [2];  [4]
  1. University of Pittsburgh, PA (United States); OSTI
  2. University of Texas at El Paso, TX (United States)
  3. Waseda University, Tokyo (Japan)
  4. University of Pittsburgh, PA (United States)

Laser powder bed fusion (L-PBF) has emerged as one of the mainstream additive manufacturing approaches for fabricating metal parts with complex geometries and intricate internal structures. However, large deformation associated with rapid heating and cooling can lead to build failure and requires post-processing which may increase manufacturing cost and prolong the production period. Here in this work, an island scanning pattern design method is proposed to optimize the scanning direction of each island in order to reduce part deformation after cutting off the build platform. The objective of this optimization is to minimize the upward bending of the part after sectioning, which allows the part deformation to satisfy the tolerance requirement or reduce the post heat treatment time. Inherent strain method is employed in the sequential finite element analysis consisting of layer-by-layer activations and sectioning for fast residual distortion prediction. Full sequential sensitivity analysis for the formulated optimization is provided to update the island scanning directions. To show the feasibility and effectiveness of the proposed method, the scanning patterns of a block structure and a connecting rod were designed and parts were fabricated using an open architecture L-PBF machine. The fabrication experiments demonstrated that the residual deformation of both parts fabricated by optimized scanning pattern can be reduced by over 50% compared to the initial scanning patterns, which demonstrate the effectiveness of the proposed method.

Research Organization:
University of Pittsburgh, PA (United States)
Sponsoring Organization:
USDOE Office of Nuclear Energy (NE); Japan Society for the Promotion of Science (JSPS); University of Texas El Paso
Grant/Contract Number:
NE0008994
OSTI ID:
1976794
Journal Information:
Additive Manufacturing, Journal Name: Additive Manufacturing Journal Issue: C Vol. 46; ISSN 2214-8604
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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