Tool life prediction of dicing saw based on PSO-BP neural network-Reference-Cited by-同舟云学术

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Tool life prediction of dicing saw based on PSO-BP neural network

Published:2022-11-19 Issue:11-12 Volume:123 Page:4399-4412
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Shi Jun,Zhang Yanyan,Sun Yahui,Cao Weifeng,Zhou Lintao

Funder

Major Science and Technology Special Projects in Henan Province

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00170-022-10466-y.pdf

Reference26 articles.

1. Lin JW, Cheng MH (2014) Investigation of chipping and wear of silicon wafer dicing. J Manuf Process 16(3):373–378

2. Jeon S, Zolfaghari A, Lee CB (2018) Dicing wheel wear monitoring technique utilizing edge diffraction effect. Measurement 121:139–143

3. Tobon-Mejia DA, Medjaher K, Zerhouni N, Zerhouni N (2012) A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models. IEEE Trans Reliab 61(2):491–503

4. Si XS, Wang WB, Hu CH, Zhou DH (2011) Remaining useful life estimation — a review on the statistical data driven approaches. Eur J Oper Res 213(1):1–14

5. Bezazi A, Pierce SG, Worden K (2007) Fatigue life prediction of sandwich composite materials under flexural tests using a Bayesian trained artificial neural network. Int J Fatigue 29(4):738–747

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