Real-time quality monitoring and diagnosis for manufacturing process profiles based on deep belief networks

Abstract

A large number of real-time quality data are collected through various sensors in the manufacturing process. However, most process data are high-dimension, nonlinear and high-correlated, so that it is difficult to model the process profiles, which restricts the application of conventional statistical process control technique. Motivated by the powerful ability of deep belief network (DBN) to extract the essential features of input data, this paper develops a real-time quality monitoring and diagnosis scheme for manufacturing process profiles based on DBN. The profiles collected from a manufacturing process are mapped into quality spectra. A novel DBN recognition model for quality spectra is established in the off-line learning phase, which can be applied to monitor and diagnose the process profiles in the on-line phase. The effectiveness of DBN recognition model for manufacturing process profiles is demonstrated by simulation experiment, and a real injection molding process example is applied to analyze the performance. The results show that the proposed DBN model outperforms alternative methods.