A new method for tool wear monitoring based on small sample size-Reference-Cited by-同舟云学术

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A new method for tool wear monitoring based on small sample size

Published:2023-11-08 Issue:9-10 Volume:129 Page:4541-4551
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Qin Bo,Liu Kuo,Song Lei,Qiao Shi,Jiang Yeming,Niu Mengmeng,Wang Yongqing

Funder

Key Program of National Natural Science Foundation of China

National Major Science and Technology Projects of China

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/s00170-023-12414-w.pdf

Reference28 articles.

1. Hou YF, Zhang DH, Wu BH, Luo M ( 2015) Milling force modeling of worn tool and tool flank wear recognition in end milling. IEEE/ASME Trans Mechatron 20(3):1024–1035. https://ieeexplore.ieee.org/document/6965588

2. Dutta S, Pal SK, Mukhopadhyay S, Sen R (2013) Application of digital image processing in tool condition monitoring: a review. CIRP J Manuf Sci Technol 6(3):212–232. https://doi.org/10.1016/j.cirpj.2013.02.005

3. Banda T, Farid AA, Li C, Jauw VL, Lim CS (2022) Application of machine vision for tool condition monitoring and tool performance optimization–a review. Int J Adv Manuf Technol 121(11–12):7057–7086. https://doi.org/10.1007/s00170-022-09696-x

4. Zhang TJ, Zhang CR, Wang YJ, Zou XF, Hu TL (2021) A vision-based fusion method for defect detection of milling cutter spiral cutting edge. Measurement 177:109248. https://doi.org/10.1016/j.measurement.2021.109248

5. Gouarir A, Martínez-Arellano G, Terrazas G, Benardos P, Ratchev S (2018) In-process tool wear prediction system based on machine learning techniques and force analysis. Procedia CIRP 77:501–504. https://doi.org/10.1016/j.procir.2018.08.253

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