An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations

PoTsang B. Huang ()
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PoTsang B. Huang: Chung-Yuan Christian University

Journal of Intelligent Manufacturing, 2016, vol. 27, issue 3, No 15, 689-700

Abstract: Abstract In this research, a new intelligent neural-fuzzy in-process surface roughness monitoring (INF-SRM) system for an end milling operation was developed. The success of the INF-SRM system depends on an accurate decision-making algorithm, which can analyze the input factors and then generate an accurate output. A new neural-fuzzy model was proposed and implemented as decision-making algorithm for the INF-SRM system. The objective of the new model is to achieve higher accuracy for surface roughness prediction and solve the disadvantages of both neural networks and fuzzy logic. The neural-assisted method was implemented to generate the fuzzy IF-THEN rules for the model. To evaluate the performance of the new neural-fuzzy model, a neural networks model was applied to develop another surface roughness monitoring system for comparison. A statistical method was finally employed to analyze the accuracy between these systems.

Keywords: Intelligent neural-fuzzy model; In-process surface roughness monitoring; End milling operations; Neural networks; Fuzzy logic (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10845-014-0907-6

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