Funder
University of Nottingham (GB)
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Reference38 articles.
1. Agustina, B., Rubio, E. M., & Sebastián, M. A. (2014). Surface roughness model based on force sensors for the prediction of the tool wear. Sensors, 14, 6393–6408.
2. Arvajeh, T., & Ismail, F. (2006). Machining stability in high speed drilling—Part 2: Time domain simulation of a bending-torsional model and experimental validations. International Journal of Machine Tools & Manufacture, 46, 1573–1581.
3. Chen, X., & Rowe, W. B. (1999). Modelling surface roughness improvement in grinding. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture, 213(1), 93–96.
4. DeHart, A., & Murphy, D. (2004). Machine shopping: How to become a better-informed machine tool consumer, American Machinist. http://americanmachinist.com/machining-cutting/machine-shopping .
5. Furness, R. (1992). Supervisory Control of the Drilling Process, Ph.D. dissertation. Department of Mechanical Engineering and Applied Mechanics, University of Michigan.
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