Optimization of multi-holes drilling toolpath using tiki-taka algorithm - Norazlina, Abdul Rahman (2024)
The growth of semiconductors and computer technologies has enhanced machining operations, especially in multi-hole drilling with multiple tools (MDMT). The MDMT process is crucial in manufacturing for creating multiple holes of varying sizes and shapes in a product, directly impacting product quality and cost across industries such as aerospace, automotive, and electronics. Efficient toolpath optimization for multi-hole drilling is essential for improving this process. However, most existing research focuses on multi-hole drilling with a single tool (MDST), resulting in limited studies on MDMT. Furthermore, MDMT complexity increases factorially with the number of holes, making manual toolpath optimization impractical. Although various methods have been proposed, most of them encounter convergence issues with MDMT due to its combinatorial complexity. This research highlights the need for novel optimization strategies specifically tailored to MDMT. Therefore, this research introduces the TikiTaka Algorithm (TTA) to address these challenges and optimize the toolpath for MDMT problems. The study aims to model the MDMT toolpath using the Traveling Salesman Problem (TSP) concept, apply TTA to optimize this model, and validate the model and algorithm through machining experiments on this problem. The research methodology involves problem modeling, algorithm development, and validation. The TSP concept formulated the MDMT problem, representing holes as cities and tools as a salesman in finding the shortest path to develop a mathematical model or fitness function. TTA was then developed to optimize this model, inspired by the player movement in the Tiki-Taka tactic in football. A computational experiment was conducted on 12 test problems across small, medium, and large problem categories using the TTA, then compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Crayfish Optimization Algorithm (COA), and Geometric Mean Optimizer (GMO). Finally, the computational results were validated by machining experiments. The findings indicate that TTA outperformed GA, PSO, ACO, COA, and GMO in 83% of the cases, securing the shortest total toolpath distance in 10 out of 12 test problems. Machining experiments confirmed these results, with TTA reducing machining time by an average of 10.34% in the large category of test problems compared to GA and PSO. This efficiency gain is especially valuable, where reduced machining time contributes to lower production costs, less tool wear, and energy savings. These results demonstrate the effectiveness of TTA for optimizing MDMT, showcasing its robustness across different problem sizes. The research concludes that TTA is highly effective in addressing the complexity of MDMT problems, with its unique exploration and exploitation phases allowing it to explore wider solution pathways effectively. Future research should prioritize exploring the potential of TTA for integration into automated manufacturing processes and its applicability in maximizing efficiency advantages in other optimization tasks.