Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm)

Kumar Abhishek, V. Rakesh Kumar, Saurav Datta () and Siba Sankar Mahapatra
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Kumar Abhishek: National Institute of Technology
V. Rakesh Kumar: National Institute of Technology
Saurav Datta: National Institute of Technology
Siba Sankar Mahapatra: National Institute of Technology

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 8, No 1, 1769-1785

Abstract: Abstract The present paper focuses on machining (turning) aspects of CFRP (epoxy) composites by using single point HSS cutting tool. The optimal setting i.e. the most favourable combination of process parameters (such as spindle speed, feed rate, depth of cut and fibre orientation angle) has been derived in view of multiple and conflicting requirements of machining performance yields viz. material removal rate, surface roughness, SR $$(\hbox {R}_{\mathrm{a}})$$ ( R a ) (of the turned product) and cutting force. This study initially derives mathematical models (objective functions) by using statistics of nonlinear regression for correlating various process parameters with respect to the output responses. In the next phase, the study utilizes a recently developed advanced optimization algorithm teaching–learning based optimization (TLBO) in order to determine the optimal machining condition for achieving satisfactory machining performances. Application potential of TLBO algorithm has been compared to that of genetic algorithm (GA). It has been observed that exploration of TLBO appears more fruitful in contrast to GA in the context of this case experimental research focused on machining of CFRP composites.

Keywords: Carbon fibre reinforced polymer (CFRP); Machining; Teaching–learning based optimization (TLBO) (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10845-015-1050-8

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