Fabrication Sequence Optimization for Minimizing Distortion in Multi-Axis Additive Manufacturing

TL;DR

This paper introduces a computational framework that optimizes fabrication sequences in multi-axis additive manufacturing to significantly reduce component distortion caused by thermal effects.

Contribution

It presents a novel gradient-based optimization method for fabrication sequences that are not limited to planar layers, improving accuracy in metal additive manufacturing.

Findings

Optimized curved layers reduce distortion by orders of magnitude.

The framework effectively predicts distortion using a computationally efficient model.

Sequence optimization leads to substantial improvements in structural accuracy.

Abstract

Additive manufacturing of metal parts involves phase transformations and high temperature gradients which lead to uneven thermal expansion and contraction, and, consequently, distortion of the fabricated components. The distortion has a great influence on the structural performance and dimensional accuracy, e.g., for assembly. It is therefore of critical importance to model, predict and, ultimately, reduce distortion. In this paper, we present a computational framework for fabrication sequence optimization to minimize distortion in multi-axis additive manufacturing (e.g., robotic wire arc additive manufacturing), in which the fabrication sequence is not limited to planar layers only. We encode the fabrication sequence by a continuous pseudo-time field, and optimize it using gradient-based numerical optimization. To demonstrate this framework, we adopt a computationally tractable yet reasonably accurate model to mimic the material shrinkage in metal additive manufacturing and thus to predict the distortion of the fabricated components. Numerical studies show that optimized curved layers can reduce distortion by orders of magnitude as compared to their planar counterparts.