Tool wear model based on least squares support vector machines and Kalman filter-Reference-Cited by-同舟云学术

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Tool wear model based on least squares support vector machines and Kalman filter

Published:2014-01-20 Issue:1-2 Volume:8 Page:101-109
ISSN:0944-6524
Container-title:Production Engineering
language:en
Short-container-title:Prod. Eng. Res. Devel.

Author:

Zhang Haiyan,Zhang Chen,Zhang Jilin,Zhou Laishui

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering

Link

http://link.springer.com/content/pdf/10.1007/s11740-014-0527-1.pdf

Reference20 articles.

1. Chen JC, Chen JC (2005) An artificial-neural-networks-based in-process tool wear prediction system in milling operations. Int J Adv Manuf Technol 25(5–6):427–434

2. Wong T, Kim W, Kwon P (2004) Experimental support for a model-based prediction of tool wear. Wear 257(7–8):790–798

3. Zhang C, Liu XH, Fang JW et al (2011) A new tool wear estimation method based on shape mapping in the milling process. Int J Adv Manuf Technol 53(1–4):121–130

4. Rao D, Krishna PV, Srikant RR (2008) Surface model and tool-wear prediction model for solid lubricant-assisted turning. Proc Inst Mech Eng J J Eng 222(5):657–665

5. Jiaa CL, Dornfeld DA (1998) A self-organizing approach to the prediction and detection of tool wear. ISA Trans 37(4):239–255

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2. Machine learning for monitoring hobbing tool health in CNC hobbing machine;Frontiers in Materials;2024-04-12

3. Tool wear prediction model based on wear influence factor;The International Journal of Advanced Manufacturing Technology;2023-10-09

4. Tool Condition Monitoring Methods Applicable in the Metalworking Process;Archives of Computational Methods in Engineering;2023-07-22

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