Process parameter effects estimation and surface quality prediction for selective laser melting empowered by Bayes optimized soft attention mechanism-enhanced transfer learning

Zhu，Jianjian1,2,3; Su，Zhongqing2,3; Wang，Qingqing2,4; Hao，Runze5; Lan，Zifeng6; Chan，Frankie Siu fai7; Li，Jiaqiang8; Wong，Sidney Wing fai7

2024-04-01

10.1016/j.compind.2023.104066

Computers in Industry   影响因子和分区

0166-3615

Additive Manufacturing (AM), particularly Selective Laser Melting (SLM), has revolutionized the industrial manufacturing sector owing to its remarkable design flexibility and precision. However, it is well known that slight changes in SLM process parameters may highly affect the surface quality of the as-built product. In this paper, we investigate the influence of SLM printing parameters (laser power, laser scanning speed, layer thickness, and hatch distance) on surface quality and develop a predictive model for surface quality based on the given printing parameters. The developed model is constructed by a Bayesian Optimization and soft Attention mechanism-enhanced Transfer learning (BOAT) framework with superior domain adaptability and generalization capability. Through experimental validation, the effectiveness of the BOAT approach in estimating printing parameters and correlating them with surface quality has been verified. The comprehensive methodology, experimental configurations, prediction results, and ensuing discussions are all presented. This study contributes to providing valuable insights and practical implications for improving the competitiveness and impact of SLM in advanced manufacturing by accurately predicting surface quality with specified printing parameters.

Additive manufacturing Attention mechanism Bayesian optimization Selective laser melting Transfer learning

SCI ; EI

英语

其他

COMPUTER SCIENCE

2-s2.0-85182016291

Scopus

1.College of Aviation Engineering,Civil Aviation Flight University of China,Guanghan,618311,China
2.Department of Mechanical Engineering,The Hong Kong Polytechnic University,Kowloon,Hong Kong
3.The Hong Kong Polytechnic University Shenzhen Research Institute,Shenzhen,518057,China
4.School of System Design and Intelligent Manufacturing,Southern University of Science and Technology,Shenzhen,518055,China
5.Hefei Zhongke Chongming Technology Co.,Ltd,Hefei,230009,China
6.School of Engineering,The University of Tokyo,Tokyo,113-8654,Japan
7.Industrial Center,The Hong Kong Polytechnic University,Kowloon,Hong Kong
8.School of Mechanical and Electric Engineering,Soochow University,Suzhou,215021,China

Zhu，Jianjian,Su，Zhongqing,Wang，Qingqing,et al. Process parameter effects estimation and surface quality prediction for selective laser melting empowered by Bayes optimized soft attention mechanism-enhanced transfer learning[J]. Computers in Industry,2024,156.

Zhu，Jianjian.,Su，Zhongqing.,Wang，Qingqing.,Hao，Runze.,Lan，Zifeng.,...&Wong，Sidney Wing fai.(2024).Process parameter effects estimation and surface quality prediction for selective laser melting empowered by Bayes optimized soft attention mechanism-enhanced transfer learning.Computers in Industry,156.

Zhu，Jianjian,et al."Process parameter effects estimation and surface quality prediction for selective laser melting empowered by Bayes optimized soft attention mechanism-enhanced transfer learning".Computers in Industry 156(2024).