Application of 1-D Convolutional Neural Network for Cutting Tool Condition Monitoring: A Classification Approach
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-5868-7_40
Reference26 articles.
1. Patange AD, Jegadeeshwaran R (2021) Review on tool condition classification in milling: a machine learning approach. Mater Today Proc 46(2):1106–1115
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3. Tambake NR, Deshmukh BB, Patange AD (2021) Data driven cutting tool fault diagnosis system using machine learning approach: a review. J Phys Conf Ser IOP Publishing 1969(012049)
4. Khade HS, Patange AD, Pardeshi SS, Jegadeeshwaran R (2021) Design of bagged tree ensemble for carbide coated inserts fault diagnosis. Mater Today Proc 46(2):1283–1289
5. Khairnar A, Patange A, Pardeshi S, Jegadeeshwaran R (2021) Supervision of carbide tool condition by training of vibration-based statistical model using boosted trees ensemble. Int J Performability Eng 17(2):229–240
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1. Multi-scale one-dimensional convolution tool wear monitoring based on multi-model fusion learning skills;Journal of Manufacturing Systems;2023-10
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