激光粉末床增材制造温度预测的长短期记忆神经网络
Long Short-Term Memory Neural Network for Temperature Prediction in Laser Powder Bed Additive Manufacturing
摘要：
在激光粉末床熔合（L-PBF）的背景下，已知最终制造产品的性质高度依赖于制造板上的温度分布及其梯度。本文提出了一种利用神经网络预测印刷过程中温度梯度分布的新方法。这是通过采用由优化的打印协议模拟产生的热图来实现的，并且热图用于根据长短期记忆架构来训练专门定制的递归神经网络。这样做的目的是避免在印刷过程中可能在整个印版上出现极端和不均匀的温度分布。为了训练神经网络，我们采用了一个精心设计的模拟和无监督学习框架。为了保持最小化的平均热梯度在整个板，成本函数被引入作为核心标准，这是启发和优化，考虑著名的旅行商问题（TSP）。随着时间的推移，由TSP控制的无监督打印过程产生了保持最小化平均热梯度的温度热图的历史。总而言之，我们提出了一种智能打印工具，它可以控制L-PBF的大量打印过程组件，即最佳喷嘴轨迹部署以及用于控制打印质量的在线温度预测。
英文摘要：
In context of laser powder bed fusion (L-PBF), it is known that the properties of the final fabricated product highly depend on the temperature distribution and its gradient over the manufacturing plate. In this paper, we propose a novel means to predict the temperature gradient distributions during the printing process by making use of neural networks. This is realized by employing heat maps produced by an optimized printing protocol simulation and used for training a specifically tailored recurrent neural network in terms of a long short-term memory architecture. The aim of this is to avoid extreme and inhomogeneous temperature distribution that may occur across the plate in the course of the printing process. In order to train the neural network, we adopt a well-engineered simulation and unsupervised learning framework. To maintain a minimized average thermal gradient across the plate, a cost function is introduced as the core criteria, which is inspired and optimized by considering the well-known traveling salesman problem (TSP). As time evolves the unsupervised printing process governed by TSP produces a history of temperature heat maps that maintain minimized average thermal gradient. All in one, we propose an intelligent printing tool that provides control over the substantial printing process components for L-PBF, i.e.\ optimal nozzle trajectory deployment as well as online temperature prediction for controlling printing quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
