Data-driven distortion compensation for laser powder bed fusion process using Gaussian process regression and inherent strain method

Abstract

The repeated melting and solidification cycles in the laser powder bed fusion (L-PBF) process lead to significant thermal gradients, resulting in notable distortion in the as-built part. Distortion compensation methods, which pre-deform the part design so the as-built shape aligns with the target, have been widely adopted to mitigate this issue. This research introduces a data-driven distortion compensation framework for the L-PBF process. It employs an experimentally-calibrated inherent strain method to generate a dataset and utilizes Gaussian process regression to create the compensated geometry. Experimental validation shows that the proposed method can reduce the maximum distortion by up to 82.5% for a lattice structure and 77.8% for a canonical part. Furthermore, the compensation results reveal that (1) the lumped layer thickness in finite element models has little impact on simulated distortion reduction but can notably affect the experimental reduction; (2) discrepancies between simulated and experimental compensation performance are largely attributed to the curvy surfaces with sharp transitions in trial and compensated shapes, a result of pre-deforming the design; (3) the number of trial geometries considerably affects the effectiveness of compensation, while the number of deformation states does not have a statistically significant impact.

Keywords

• Laser powder bed fusion
• Distortion compensation
• Inherent strain method
• Gaussian process regression