Topology optimization considering the distortion in additive manufacturing

TL;DR

This paper introduces a topology optimization method that incorporates distortion prediction in additive manufacturing, aiming to improve the accuracy and mechanical properties of 3D printed metal parts.

Contribution

It develops a computationally efficient analytical model for laser powder bed fusion and integrates it into a topology optimization framework considering distortion effects.

Findings

Validated with two-dimensional design examples

Demonstrated reduction in residual stress and distortion

Showed improved dimensional accuracy in optimized designs

Abstract

Additive manufacturing is a free-form manufacturing technique in which parts are built in a layer-by-layer manner. Laser powder bed fusion is one of the popular techniques used to fabricate metal parts. However, it induces residual stress and distortion during fabrication that adversely affects the mechanical properties and dimensional accuracy of the manufactured parts. Therefore, predicting and avoiding the residual stress and distortion are critical issues. In this study, we propose a topology optimization method that accounts for the distortion. First, we propose a computationally inexpensive analytical model for additive manufacturing that uses laser powder bed fusion and formulated an optimization problem. Next, we approximate the topological derivative of the objective function using an adjoint variable method that is then utilized to update the level set function via a time evolutionary reaction-diffusion equation. Finally, the validity and effectiveness of the proposed optimization method was established using two-dimensional design examples.