Machining process monitoring with vibration signal based manifold learning

Abstract

Machining process monitoring based on vibration sensing is a growing demand in smart manufacturing. However, in real factories, massive process conditions which include thousands of shapes of workpieces and thousands of combinations of cutting parameters, such as spindle speed, feed rate and cutting depth, are designed and used in manufacturing. Manifold learning is able to extract essential and distinct features from the vibration signal and helps to monitor and recognize different process conditions. In this paper, the dataset, including slight and huge variation of cutting parameters and workpiece shapes, are collected for analysis. Different manifold learning algorithms are utilized and compared to mine the essential features and reduce the interference of non-sensitive features. The generalization ability of different manifold learning algorithms are discussed to fit the various process conditions. Convolutional neural networks are employed to evaluate the monitoring accuracy. The experimental results show that the features obtained by the manifold learning distinguish vibration signals of different cutting parameters in low dimensional space and give a protentional way to construct effective monitoring systems. The generalization ability to different workpieces and cutting parameters and its limitation are discussed.