Laser Cladding Quality Monitoring Using Coaxial Image Based on Machine Learning

Abstract

The processing quality of laser cladding is a topic of interest to laser machine manufacturers. The management of various experimental data and process quality of the laser machine can effectively guide the customer to better adjust the processing parameters. This study finds that the processing quality of laser cladding is related to the signal of the coaxial image. Therefore, this study uses a machine learning method to establish a model of coaxial image and laser processing quality. The study does not merely implement a single machine learning method but also compares various machine learning algorithms. Convolutional neural networks and autoencoders are implemented as algorithms for the feature extraction phase. Linear regression, random forest, support vector machine, and SoftMax neural networks are implemented as algorithms for classification. The receiver operating characteristic curve and the accuracy rate are the result indicators of this paper. The experimental results show that there is indeed a correlation between the laser processing quality and the coaxial image, and the algorithm in this study can effectively supervise the processing quality of laser cladding.