Evolutionary Algorithms for Optimization of Drilling Variables for Reduced Thrust Force in Composite Material Drilling
Shikha Bhardwaj, Department of Computer Science, Mahatma Jyoti Rao Phoole University, Jaipur (R.J), India.
Manuscript received on 18 April 2023 | Revised Manuscript received on 22 April 2023 | Manuscript Accepted on 15 May 2023 | Manuscript published on 30 May 2023 | PP: 15-19 | Volume-13 Issue-2, May 2023 | Retrieval Number: 100.1/ijsce.B36100513223 | DOI: 10.35940/ijsce.B3610.0513223
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study aims to optimize drilling variables to reduce the thrust force required for drilling composite materials. The optimization process involves using evolutionary algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) to determine the best combination of drilling parameters, including drill speed, feed rate, and point angle. The objective is to minimize the thrust force required for drilling while maintaining the desired quality of the drilled holes. ANOVA and regression analysis is implemented to discuss the impact of drilling variable on the thrust force. The results demonstrate that the proposed approach is effective in reducing thrust force and improving drilling efficiency. The optimized drilling parameters obtained can be used to enhance the performance of composite material drilling processes. Performance output of both algorithms for optimization of problem is discussed in detail.
Keywords: Drilling, Natural fiber, Genetic Algorithm, Particle Swarm Optimization, ANOVA, Regression Analysis.
Scope of the Article: Composite Material