ABSTRACT

Heat accumulation is a critical problem in continuous multi-layer laser aided additive manufacturing (LAAM) process, resulting in inhomogeneous mechanical properties and non-uniformity in the deposited height which can deteriorate the deposition process. This work presents a new integrated finite element (FE) simulation and machine learning approach to select a multi-layer laser infill toolpath planning strategy for fabricating quadrilateral parts to minimise localised heat accumulation during the deposition process. After one layer deposition simulation, the approach employs a Temperature-Pattern Recurrent Neural Networks (TP-RNN) model to predict the temperature field after the next layer deposition for each of the candidate infill toolpaths, and a process parameters inspired thermal field evaluation method to select the best candidate toolpath. The approach would significantly improve the computational efficiency of the laser infill toolpath planning, which was validated by improving the flatness of the 20-layer cube deposition samples with two dimensions (20 mm × 20 mm and 30 mm × 30 mm).

摘要

在连续的多层激光辅助增材制造（LAAM）过程中，热量累积是一个关键问题，导致机械性能不均匀以及沉积高度不均匀，这可能会使沉积过程恶化。这项工作提出了一种新的集成有限元（FE）模拟和机器学习方法，以选择多层激光填充工具路径规划策略来制造四边形零件，以最大程度地减少沉积过程中的局部热量积聚。经过一层沉积模拟后，该方法采用温度模式递归神经网络（TP-RNN）模型来预测每个候选填充工具路径在下一层沉积后的温度场，并采用工艺参数启发性的热场评估方法选择最佳的候选刀具路径。