Research Article Open Access

Modification Approach of Fuzzy Logic Model for Predicting of Cutting Force When Machining Nickel Based Hastelloy C-276

Basim A. Khidhir1, Bashir Mohamed1 and Mahmoud A.A. Younis1
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American Journal of Engineering and Applied Sciences
Volume 3 No. 1, 2010, 207-213

DOI: https://doi.org/10.3844/ajeassp.2010.207.213

Submitted On: 8 May 2009 Published On: 31 March 2010

How to Cite: Khidhir, B. A., Mohamed, B. & Younis, M. A. (2010). Modification Approach of Fuzzy Logic Model for Predicting of Cutting Force When Machining Nickel Based Hastelloy C-276. American Journal of Engineering and Applied Sciences, 3(1), 207-213. https://doi.org/10.3844/ajeassp.2010.207.213

Abstract

Problem statement: Most Nickel based Hastelloy C-276 is a difficult-to-machine material because of its low thermal diffusive property and high strength at high temperature. Machinability consideration of nickel based Hastelloy C-276 in turning operations has been carried out using ceramic inserts under dry conditions. Approach: This study described a modification approach applied to a fuzzy logic based model for predicting cutting force where the machining parameters for cutting speed ranges, feed rate, depth of cut and approach angle are not overlapping. For this study, data were selected depending on the design of experiments. Response surface methodology was applied to predict the cutting force and to examine the fuzzy logic based model. Results: The modification approach fuzzy logic based model produced the cutting force data providing good correlation with response surface data. In this situation the cutting force data were superimposed and results were adjusted according to their own ranges. Conclusion: A review of literatures on optimization techniques revealed that there were, in particular, successful industrial applications of design of experiment-based approaches for optimal settings of process variables.

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Keywords

  • Cutting speed
  • surface response methodology
  • first order (linear + interaction)
  • fuzzy logic