Support Structure Topology Optimization Considering the Residual Distortion for Laser Powder Bed Fusion Metal Additive Manufacturing

Author:

Xu Shuzhi1,Liu Jikai2,Sun Yiding1,Li Xinming1,Ma Yongsheng3

Affiliation:

1. University of Alberta

2. Shandong University

3. Southern University of Science and Technology

Abstract

Abstract This paper proposes a support structure topology optimization method for laser powder bed fusion processed metal parts, which could effectively avoid part failures caused by over-distortions. Multiple additive manufacturing issues are considered and overcome with the proposed method, including the self-support issue, minimum length scale control, and support structure easy removal. Specifically, a finite element analysis model based on the inherent strain method is proposed to simulate the complex mechanical behavior in the additive manufacturing process. Then, according to the layer-by-layer inherent strain-based fast simulation model, the gravity compliance and residual distortion minimization topology optimization problem incorporating the self-support constraint, mass fraction constraint, minimum length scale control, and support easy-removal constraint is formulated. Accordingly, the critical sensitivity information is derived through the adjoin analysis. Finally, the proposed method is applied to several 2D and 3D benchmark examples to demonstrate the effectiveness on residual distortion control. The influences of different optimization strategies, weighting parameters, and minimum length scale limits are comparatively explored. A comprehensive discussion is presented at the end to summarize the numerical phenomena.

Publisher

Research Square Platform LLC

Reference68 articles.

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3. Brackett, D., I. Ashcroft, and R. Hague. "Topology optimization for additive manufacturing." Proceedings of the solid freeform fabrication symposium, Austin, TX. Vol. 1. 2011.

4. Paulino. "Bridging topology optimization and additive manufacturing;Zegard Tomás;Structural and Multidisciplinary Optimization,2016

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