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

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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 \((\hbox {R}_{\mathrm{a}})\) (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.

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Abbreviations

\(\hbox {X}_{1}\) :

Spindle speed

\(\hbox {X}_{2}\) :

Feed rate

\(\hbox {X}_{3}\) :

Depth of cut

\(\hbox {X}_{4}\) :

Orientation of fiber

\(\hbox {F}_{\mathrm{x}}\) :

Feed force

\(\hbox {F}_{\mathrm{y}}\) :

Tangential force

\(\hbox {F}_{\mathrm{z}}\) :

Longitudinal force

\(\hbox {Z}_{1}\) :

Mathematical equation for cutting force

\(\hbox {Z}_{2}\) :

Mathematical equation for surface roughness

\(\hbox {Z}_{3}\) :

Mathematical equation for material removal rate (MRR)

Z:

Mathematical equation for multi objective

CF:

Cutting force

SR:

Surface roughness

MRR:

Material removal rate

TLBO:

Teaching–learning based optimization

ABC:

Artificial bee colony

ACO:

Ant colony optimization

PSO:

Particle swarm optimization

SA:

Simulated annealing

GA:

Genetic algorithm

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Acknowledgments

The research is supported by a Sponsored Project (SERB, DST, Govt. of India). [Sanction Ref. No.: SR/FTP/ETA-0140/2011 Dated 21 November 2011]. The authors sincerely express their heartiest thanks to the Chief Editor, Journal of Intelligent Manufacturing, and the anonymous reviewers for their valuable comments and suggestions to make the paper a good contributor.

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  1. Department of Mechanical Engineering, National Institute of Technology, Rourkela, 769008, Odisha, India

    Kumar Abhishek, V. Rakesh Kumar, Saurav Datta & Siba Sankar Mahapatra

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  1. Kumar Abhishek
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Correspondence to Saurav Datta.

Appendix

Appendix

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Fig. 12
figure 12

Determination of cutting forces using turning tool dynamometer (for sample no. 20)

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Abhishek, K., Rakesh Kumar, V., Datta, S. et al. Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm). J Intell Manuf 28, 1769–1785 (2017). https://doi.org/10.1007/s10845-015-1050-8

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