A CONTINUAL LEARNING MODEL FOR COATINGS HARDNESS PREDICTION BASED ON ARTIFICIAL NEURAL NETWORK WITH ELASTIC WEIGHT CONSOLIDATION

Da Lei, Qianzhi Wang, Fei Zhou, Jizhou Kong and Zhifeng Zhou
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Da Lei: College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
Qianzhi Wang: ��National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
Fei Zhou: College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China†National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
Jizhou Kong: College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
Zhifeng Zhou: ��Hong Kong Branch of National Precious Metals Material Engineering Research Center (NPMM), City University of Hong Kong, Hong Kong, P. R. China

Surface Review and Letters (SRL), 2023, vol. 30, issue 06, 1-12

Abstract: In order to continuously update the prediction model based on the ever-expanding data set solely, this study established a continual learning model, i.e. the elastic weight consolidation (EWC)-based artificial neural network (ANN) model to predict the hardness of Ni–Cu–CrBN coating that could be used in tribology field. The results showed that after being trained by the ever-expanding dataset, the determination coefficient R2 of the normal ANN model on old data decreased to 0.8421 while that of the EWC-based ANN model was still 0.9836. It was indicated that the EWC-based ANN model presented good performance on both new and old data after being trained by the ever-expanding dataset solely, which saved time and was more in line with practical application.

Keywords: Continual learning; hardness prediction; artificial neural network; elastic weight consolidation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218625X23500361

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