Gaussian process classification of melt pool motion for laser powder bed fusion process monitoring

Abstract

Laser powder bed fusion (L-PBF) is a metal additive manufacturing (AM) process with great potential in producing high performance metal components. Due to lack of stability and repeatability of the building process, its wide application in industry is limited. The process monitoring and control are import to ensure product quality. The size and shape of the melt pool are continuously changing during the L-PBF process, which may lead to the generation of defects. To represent the melt pool variations more accurately, a new motion feature is extracted and a classification model is constructed to identify the melting state. Firstly, a 36-dimensional motion feature is obtained by contour unwrapping with respect to the melt pool centroid. Subsequently, a sample dataset of melt pool image including four categories of melting states is established. Finally, a Gaussian process classification (GPC) model is constructed to identify the melting state based on motion feature. To verify the performance of GPC, it is also given that the recognition results based on support vector machine (SVM) model, multilayer perceptron (MLP) and long short-term memory (LSTM) neural network. The research results show that under the advantages of automatically optimizing hyperparameters and providing probability distribution information of melting state, the GPC model can still achieve a better recognition result. The overall recognition rate reaches 87.1%, and the melting state can be better identified. A novel in-situ monitoring idea is provided for the L-PBF in this research.