Physics-Informed Machine Learning for Accurate Prediction of Temperature and Melt Pool Dimension in Metal Additive Manufacturing

Abstract

The temperature distribution and melt pool size have a great influence on the microstructure and mechanical behavior of metal additive manufacturing process. The numerical method can give relatively accurate results but is time-consuming and, therefore, unsuitable for in-process prediction. Owing to its remarkable capabilities, machine learning methods have been applied to predict melt pool size and temperature distribution. However, the success of traditional data-driven machine learning methods is highly dependent on the amount and quality of the training data, which is not always convenient to access. This article proposes a physics-informed machine learning (PIML) method, which integrates data and physics laws in the training parts, overcoming the problems of low speed and data availability. An artificial neural network constrained by the heat transfer equation and a small amount of labeled data is developed to predict the melt pool size and temperature distribution. Besides, the locally adaptive activation function is utilized to improve the prediction performance. The result shows that the developed PIML model can accurately predict the temperature and melt pool dimension under different scanning speeds with a small amount of labeled data, which shows significant potential in practical application