Quality spectra fluctuation modeling for manufacturing process based on deep transfer learning

It is difficult to characterize and monitor the quality fluctuation caused by multi-correlation parameters in manufacturing process. Motivated by the powerful ability of digital images to characterize process states, this paper presents a quality spectra fluctuation modeling method based on deep transfer learning. Firstly, through the multi-parameter correlation of spectra pixels, the quality spectra is constructed to characterize quality fluctuation. Then, a deep residual network transfer learning model is used to identify the types of quality fluctuation. Finally, the effectiveness analysis of proposed model is demonstrated by the Tennessee Eastman process.