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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

Authors :
Qian Chen
Akihiro Takezawa
Ryan B. Wicker
Hunter Taylor
Xuan Liang
Albert C. To
Xavier Jimenez
Source :
Additive Manufacturing. 46:102116
Publication Year :
2021
Publisher :
Elsevier BV, 2021.

Abstract

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. 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.

Details

ISSN :
22148604
Volume :
46
Database :
OpenAIRE
Journal :
Additive Manufacturing
Accession number :
edsair.doi...........876c307fc1694d37d2c8320be577c1e1
Full Text :
https://doi.org/10.1016/j.addma.2021.102116