An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
http://link.springer.com/content/pdf/10.1007/s10845-014-0907-6.pdf
Reference27 articles.
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3. Chang, H., Kim, J., Kim, I., Jang, D. Y., & Han, D. C. (2007). In-process surface roughness prediction using displacement signals from spindle motion. International Journal of Machine Tools and Manufacture, 47(6), 1021–1026.
4. Chen, J. C., & Lou, M. S. (2000). Fuzzy-nets based approach to using an accelerometer for an in-process surface roughness prediction system in milling operation. International Journal of Computer Integrated Manufacturing, 13(4), 358–368.
5. Chen, K. Y., Lim, C. P., & Lai, W. K. (2005). Application of a neural fuzzy system with rule extraction to fault detection and diagnosis. Journal of Intelligent Manufacturing, 16(6), 679–691.
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