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.

翻译：金属零件的增材制造涉及相变和高温梯度，这会导致不均匀的热膨胀与收缩，进而引发制造部件的变形。变形对结构性能和尺寸精度（例如装配）有重大影响。因此，建模、预测并最终减少变形至关重要。本文提出了一种面向制造序列优化的计算框架，旨在最小化多轴增材制造（例如机器人丝材电弧增材制造）中的变形。在该框架中，制造序列不局限于平面层。我们将制造序列编码为连续的伪时间场，并采用基于梯度的数值优化方法对其进行优化。为演示该框架，我们采用了一种计算简便且精度合理的模型来模拟金属增材制造中的材料收缩，从而预测制造部件的变形。数值研究表明，与平面层相比，优化后的曲面层可将变形降低数个数量级。