Condition monitoring and life prediction of the turning tool based on extreme learning machine and transfer learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-021-05716-1.pdf
Reference33 articles.
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2. Yin Z, Huang C, Yuan J et al (2015) Cutting performance and life prediction of an Al2O3/TiC micro–nano-composite ceramic tool when machining austenitic stainless steel. Ceram Int 41(5):7059–7065
3. Karandikar JM, Abbas AE, Schmitz TL (2014) Tool life prediction using Bayesian updating. Part 1: milling tool life model using a discrete grid method. Precis Eng 38(1):9–17
4. Benkedjouh T, Medjaher K, Zerhouni N et al (2015) Health assessment and life prediction of cutting tools based on support vector regression. J Intell Manuf 26(2):213–223
5. Krolczyk GM, Nieslony P, Legutko S (2015) Determination of tool life and research wear during duplex stainless steel turning. Arch Civ Mech Eng 15(2):347–354
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