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.

翻译：在激光粉末床熔融（L-PBF）背景下，已知最终制造产品的性能高度依赖于制造板上的温度分布及其梯度。本文提出了一种新颖的方法，利用神经网络预测打印过程中的温度梯度分布。具体实现是通过采用优化打印协议仿真生成的热图，并训练一种基于长短期记忆架构的特制循环神经网络。其目的是避免打印过程中可能在板上出现的极端和不均匀温度分布。为训练该神经网络，我们采用了经过精心设计的仿真和无监督学习框架。为保持板上的最小平均热梯度，引入了一个以旅行商问题（TSP）为灵感并优化设计的成本函数作为核心准则。随着时间推移，由TSP控制的无监督打印过程会产生一系列维持最小平均热梯度的温度热图历史记录。总体而言，我们提出了一种智能打印工具，可实现对L-PBF关键打印过程组件的控制，即最优喷嘴轨迹部署以及用于控制打印质量的在线温度预测。