Open AccessQuality spectra fluctuation modeling for manufacturing process based on deep transfer learning
Author(s) -
Sheng Hu,
Zhe Li,
Shoujing Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1983/1/012101
Subject(s) - quality (philosophy) , process (computing) , residual , computer science , artificial intelligence , spectral line , transfer of learning , deep learning , pixel , biological system , pattern recognition (psychology) , machine learning , algorithm , physics , quantum mechanics , astronomy , biology , operating system
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.