Multi-Hole Drilling Tool Path Planning and Cost Management through Hybrid SFLA-ACO Algorithm for Composites and Hybrid Materials

Abstract

In the process of drilling multiple holes in composites and hybrid materials, almost 70% of the time is consumed in tool traveling and tool changing. Recently, researchers have focused on this consumption of time for optimization of the tool path. A literature review revealed the following research gap: little work has been performed on the hybridization of metaheuristics. In the present study, the hybridization of SFLA and ACO metaheuristic algorithms is carried out, which is based on this research gap. The hybridization of SFLA and ACO metaheuristic algorithms provides originality and novelty in this study. The main objective of this study is to minimize the tool path in drilling problems. The proposed algorithm was applied to five benchmark multi-hole drilling problems and one industrial problem from the literature. The outcome of this work is evaluated with the results of dynamic programming (DP), ACO, an immune-based evolutionary approach (IA), and a modified SFLA for five benchmark problems. The accuracy of the results was improved by 2.27% using the proposed hybrid algorithm, indicating that the proposed algorithm is superior to DP, ACO, IA, and the modified SFLA. Additionally, the results of the proposed hybrid algorithm for an example industrial problem from the literature were compared with those of the SFLA and modified SFLA. The proposed algorithm reduced the total cost by 6.17% and 3.76% compared with the SFLA and modified SFLA, respectively. Thus, the efficacy of the proposed hybrid algorithm was confirmed, along with its applicability.