Prediction of Forces Components During the Turning Process of Stellite 6 Material Based on Artificial Neural Networks
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-27146-6_43
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4. Sarıkaya M, Güllü A (2015) Multi-response optimization of minimum quantity lubrication parameters using Taguchi-based grey relational analysis in turning of difficult-to-cut alloy Haynes 25. J Clean Prod 91:347–357
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Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Multi-response Optimization of Turning Parameters with TOPSIS Method;Lecture Notes in Mechanical Engineering;2022-08-20
2. Modelling of cutting forces and surface roughness evolutions during straight turning of Stellite 6 material based on response surface methodology, artificial neural networks and support vector machine approaches;J MECH ENG SCI;2021
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