Long Short-Term Memory Neural Network for Temperature Prediction in Laser Powder Bed Additive Manufacturing

TL;DR

This paper introduces a neural network-based method to predict temperature gradients in laser powder bed fusion, aiming to optimize printing quality by controlling temperature distribution during manufacturing.

Contribution

It presents a novel LSTM neural network trained on heat maps and a TSP-inspired cost function to predict and control temperature gradients in additive manufacturing.

Findings

Effective temperature gradient prediction during printing

Reduction of inhomogeneous temperature distribution

Enhanced control over printing process components

Abstract

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.