A MULTI-OBJECTIVE GREY RELATIONAL APPROACH AND REGRESSION ANALYSIS ON OPTIMIZATION OF DRILLING PROCESS PARAMETERS FOR GLARE FIBER METAL LAMINATES

K. Logesh, A. Arul Marcel Moshi, S. R. Sundara Bharathi and P. Hariharasakthisudhan
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K. Logesh: Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala, R&D Institute of Science and Technology, Chennai 600062, Tamil Nadu, India
A. Arul Marcel Moshi: ��Department of Mechanical Engineering, National Engineering College, Kovilpatti 628503, Tamil Nadu, India
S. R. Sundara Bharathi: ��Department of Mechanical Engineering, National Engineering College, Kovilpatti 628503, Tamil Nadu, India
P. Hariharasakthisudhan: ��Department of Mechanical Engineering, National Engineering College, Kovilpatti 628503, Tamil Nadu, India

Surface Review and Letters (SRL), 2022, vol. 29, issue 05, 1-13

Abstract: Fiber metal laminates (FML) are used in outer covering of the fuselage skin structure. The thrust force and the torque generated during drilling process affect the quality of the holes on the structure. The magnitude of cutting forces is controlled by optimizing the drilling process parameters. In this study, the influence of drilling parameters such as spindle speed, feed rate and the weight percentage of layered double hydroxides (LDH) in the binder epoxy on the thrust force and torque during drilling operation was studied. The experiments were designed based on Taguchi’s L9 orthogonal array. The Gray Relational Analysis was used as multi-objective optimization tool for finding the optimal combination of process parameters. The spindle speed was identified as the most influencing process parameter to affect the drill quality in the FMLs. SEM images taken on the drilled specimens for the best and worst input parameter settings were compared and discussed. The regression models were generated to predict the output response values within the range of actual experiments.

Keywords: Fiber metal laminate; drilling; process parameter optimization; grey relational analysis; regression equations (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218625X22500664

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