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Abstract <p>Computer-aided manufacturing (CAM) software plays a crucial role in manufacturing by facilitating the creation of tool paths that guide machine tools in producing intricate parts. The trajectory of the end-effector or cutting tool significantly impacts the efficiency of industrial processes. Therefore, algorithms have been developed to optimize tool paths, considering various goals. The choice of tool path generation method directly influences the cost and time of machining operations. Decision-makers prioritize solutions with shorter blanking lengths to enhance machining efficiency and reduce costs. Consequently, effective planning of tool path generation is vital for optimizing part features, minimizing expenses and maximizing operational efficiency. In this research, evolutionary algorithms like Non-dominated Sorting Algorithm-II (NSGA-II) and Genetic algorithm (GA) have been investigated to propose an approach for the creation of efficient zig-zag tool path planning using voxel-based CAD models and focus on rough milling of complex parts. After processing the CAD (STL) part model, the system looks at the voxelized version of the component model to determine which parts may be machined and which cannot. This is followed by the creation of cutter location points for the machinable region and then multi-objective evolutionary optimization technique NSGA-II by considering tool path length reduction, reducing the number of turns to achieve an optimized bidirectional zig-zag tool path strategy. The developed system underwent rigorous testing across parts with diverse machining feature complexities. It outperformed traditional zigzag roughing toolpath generation methods by significantly reducing tool path length, the number of turns, tool lift-offs and the length of an air-cutting path. Comparative analysis revealed that, for bidirectional zig-zag tool path planning, NSGA-II achieved a 3.96% reduction in tool path length compared to Mastercam X5 and a 2.18% improvement over GA. For minimizing air-cut path length, GA performed best, while in reducing the number of turns, both algorithms delivered nearly identical results.</p> Graphical Abstract <p></p>

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Voxel-based toolpath planning and optimization using NSGA-II

  • 期刊论文

  • 发表日期：

    2025年11月6日

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  • Gurabvaiah Punugupati,
  • K. R. Kiran,
  • Prasanth Grandhi,
  • Hymavathi Madivada,
  • C. S. P. Rao

摘要

Abstract

Computer-aided manufacturing (CAM) software plays a crucial role in manufacturing by facilitating the creation of tool paths that guide machine tools in producing intricate parts. The trajectory of the end-effector or cutting tool significantly impacts the efficiency of industrial processes. Therefore, algorithms have been developed to optimize tool paths, considering various goals. The choice of tool path generation method directly influences the cost and time of machining operations. Decision-makers prioritize solutions with shorter blanking lengths to enhance machining efficiency and reduce costs. Consequently, effective planning of tool path generation is vital for optimizing part features, minimizing expenses and maximizing operational efficiency. In this research, evolutionary algorithms like Non-dominated Sorting Algorithm-II (NSGA-II) and Genetic algorithm (GA) have been investigated to propose an approach for the creation of efficient zig-zag tool path planning using voxel-based CAD models and focus on rough milling of complex parts. After processing the CAD (STL) part model, the system looks at the voxelized version of the component model to determine which parts may be machined and which cannot. This is followed by the creation of cutter location points for the machinable region and then multi-objective evolutionary optimization technique NSGA-II by considering tool path length reduction, reducing the number of turns to achieve an optimized bidirectional zig-zag tool path strategy. The developed system underwent rigorous testing across parts with diverse machining feature complexities. It outperformed traditional zigzag roughing toolpath generation methods by significantly reducing tool path length, the number of turns, tool lift-offs and the length of an air-cutting path. Comparative analysis revealed that, for bidirectional zig-zag tool path planning, NSGA-II achieved a 3.96% reduction in tool path length compared to Mastercam X5 and a 2.18% improvement over GA. For minimizing air-cut path length, GA performed best, while in reducing the number of turns, both algorithms delivered nearly identical results.

Graphical Abstract