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

被引：1

作者：

Lei, Da ^{[1
]}

Wang, Qianzhi ^{[2
]}

Zhou, Fei ^{[1
,2
]}

Kong, Jizhou ^{[1
]}

Zhou, Zhifeng ^{[3
]}

机构：

[1] Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing 210016, Peoples R China

[2] Nanjing Univ Aeronaut & Astronaut, Natl Key Lab Sci & Technol Helicopter Transmiss, Nanjing 210016, Peoples R China

[3] City Univ Hong Kong, Natl Precious Met Mat Engn Res Ctr NPMM, Hong Kong Branch, =, Hong Kong, Peoples R China

来源：

SURFACE REVIEW AND LETTERS | 2023年 / 30卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Continual learning; hardness prediction; artificial neural network; elastic weight consolidation; OPTIMIZATION;

D O I：

10.1142/S0218625X23500361

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

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.

引用

页数：12

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