Collision-Free Tool Path Optimization for Louvre Geometries Using an Adaptive Discrete Particle Swarm Framework - Selvi Lukman; Jimmy Loekito (DRPM - ITB, 2026)
This study proposes a novel application of Particle Swarm Optimization (PSO) for tool path planning in complex louvre geometries utilized in heat transfer systems. Unlike conventional approaches, the proposed framework explicitly integrates geometric smoothness and collision avoidance into the optimization process, as it enables the generation of continuous and non-intersecting tool trajectories. This is particularly significant as surface quality in louvre fins directly influences boundary layer disruption, which in turn affects convective heat transfer efficiency and pressure drop characteristics. By minimizing abrupt tool movements and machining-induced surface roughness, the method addresses a critical gap between manufacturing precision and thermal performance. The PSO-based approach simultaneously optimizes machining time and trajectory feasibility, ensuring safe and efficient tool movements. The experimental results demonstrated rapid convergence with the objective function significantly decreasing within the first 50 iterations and stabilizing around iteration 80. The optimized solution achieved a machining time of 0.60 minutes (36 seconds) while maintaining consistent minimum objective values throughout the process. These findings highlight the robustness and stability of the proposed method. Overall, this work contributes a novel optimization framework that bridges advanced manufacturing and thermal performance considerations, establishing PSO as an effective solution for high-precision tool path planning in complex industrial geometries.