Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models
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DOI: 10.1007/s10845-017-1317-3
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Cited by:
- Soheyl Khalilpourazari & Saman Khalilpourazary & Aybike Özyüksel Çiftçioğlu & Gerhard-Wilhelm Weber, 2021. "Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1621-1647, August.
- Dragan Rodić & Milenko Sekulić & Marin Gostimirović & Vladimir Pucovsky & Davorin Kramar, 2021. "Fuzzy logic and sub-clustering approaches to predict main cutting force in high-pressure jet assisted turning," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 21-36, January.
- Danil Yu Pimenov & Andres Bustillo & Szymon Wojciechowski & Vishal S. Sharma & Munish K. Gupta & Mustafa Kuntoğlu, 2023. "Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2079-2121, June.
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Keywords
Cobalt chromium alloys; Orthogonal cutting; Forces in cutting; Gaussian process; Tool life optimisation;All these keywords.
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