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Laser micro-drilling of CNT reinforced polymer nanocomposite: A parametric study using RSM and APSO

  • Lipsamayee Mishra (Department of Production Engineering, Veer Surendra University of Technology) ;
  • Trupti Ranjan Mahapatra (Department of Production Engineering, Veer Surendra University of Technology) ;
  • Debadutta Mishra (Department of Production Engineering, Veer Surendra University of Technology) ;
  • Akshaya Kumar Rout (School of Mechanical Engineering, KIIT Deemed to be University)
  • Received : 2022.03.05
  • Accepted : 2023.07.10
  • Published : 2024.02.25

Abstract

The present experimental investigation focuses on finding optimal parametric data-set of laser micro-drilling operation with minimum taper and Heat-affected zone during laser micro-drilling of Carbon Nanotube/Epoxy-based composite materials. Experiments have been conducted as per Box-Behnken design (BBD) techniques considering cutting speed, lamp current, pulse frequency and air pressure as input process parameters. Then, the relationship between control parameters and output responses is developed using second-order nonlinear regression models. The analysis of variance test has also been performed to check the adequacy of the developed mathematical model. Using the Response Surface Methodology (RSM) and an Accelerated particle swarm optimization (APSO) technique, optimum process parameters are evaluated and compared. Moreover, confirmation tests are conducted with the optimal parameter settings obtained from RSM and APSO and improvement in performance parameter is noticed in each case. The optimal process parameter setting obtained from predictive RSM based APSO techniques are speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), Air pressure (1 kg/cm2) for Taper and speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), air pressure (3 kg/cm2) for HAZ. From the confirmatory experimental result, it is observed that the APSO metaheuristic algorithm performs efficiently for optimizing the responses during laser micro-drilling process of nanocomposites both in individual and multi-objective optimization.

Keywords

Acknowledgement

The research described in this paper was financially supported by TEQIP III, VSSUT, Burla (VSSUT/TEQIP/82/2020, dated 20/01/2020 and VSSUT/TEQIP/86/2020, dated 20/01/2020).

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