Non-dominated sorting modified teaching–learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE)

Elango Natarajan (), Varadaraju Kaviarasan, Wei Hong Lim, Sew Sun Tiang, S. Parasuraman and Sangeetha Elango
Additional contact information
Elango Natarajan: UCSI University
Varadaraju Kaviarasan: Sona College of Technology
Wei Hong Lim: UCSI University
Sew Sun Tiang: UCSI University
S. Parasuraman: Monash University
Sangeetha Elango: FTMS College

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 4, No 8, 935 pages

Abstract: Abstract A non-dominated sorting modified teaching–learning-based optimization (NSMTLBO) is proposed to obtain the optimum solution for a multi-objective problem related to machining Polytetrafluoroethylene. Firstly, an experimental design is done and the L27 orthogonal array with three-level of cutting speed $$ \left( {V_{c} } \right) $$Vc, feed rate (f), depth of cut (ap) and nose radius $$ \left( {N_{r} } \right) $$Nr is formulated. A CNC turning machine is used to perform experiments with cemented carbide tool at an insert angle of 80° and the response variables known as surface finish and material removal rate are measured. A response surface model is rendered from the experimental results to derive the minimization function of surface roughness $$ \left( {R_{a} } \right) $$Ra and maximization function of material removal rate (MRR). Both optimization functions are solved simultaneously using NSMTLBO. A fuzzy decision maker is also integrated with NSMTLBO to determine the preferred optimum machining parameters from Pareto-front based on the relative importance level of each objective function. The best responses Ra = 2.2347 µm and MRR = 96.835 cm3/min are predicted at the optimum machining parameters of Vc = 160 mm/min, f = 0.5 mm/rev, ap = 0.98 mm and Nr = 0.8 mm. The proposed NSMTLBO is reported to outperform other six peer algorithms due to its excellent capability in generating the Pareto-fronts which are more uniformly distributed and resulted higher percentage of non-dominated solutions. Furthermore, the prediction results of NSMTLBO are validated experimentally and it is reported that the performance deviations between the predicted and actual results are lower than 3.7%, implying the applicability of proposed work in real-world machining applications.

Keywords: Design of experiments; Multi-response; Non-dominated sorting modified teaching–learning-based optimization; Response surface model; Surface roughness (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10845-019-01486-9

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