目的 针对注塑成型多浇口位置优化中组合爆炸、计算效率低的问题,提出一种高效的并行启发式优化方法。方法 采用“等效流长”作为浇口平衡性快速评估指标,构建基于制品表面网格的流长计算模型。重点设计“遗传算法全局探索+混合爬山算法局部开发”的双层优化框架,通过动态种群控制、动态种群多样性监测、自适应遗传算子、智能邻域搜索及并行计算机制,实现多浇口位置的高效寻优。结果 在网格规模为5万~14万的制品测试中,所提算法在3浇口优化问题上的计算效率较已有算法提升约30倍;在多种工况下,浇口位置优化结果的充填平衡性优于或接近专业CAE软件。结论 所提并行启发式算法能有效解决多浇口组合优化难题,在保证优化质量的同时,大幅提升设计效率,具有显著的工程实用价值。
Abstract
The work aims to propose an efficient parallel heuristic optimization method to address the challenges of combinatorial explosion and low computational efficiency in multi-gate location optimization for injection molding. The “equivalent flow length” was employed as a rapid evaluation metric for filling balance and a flow-length calculation model based on the surface mesh of the molded part was established. A dual-layer optimization framework, combining “global exploration by a genetic algorithm” with local exploitation by a “hybrid hill-climbing algorithm”, was developed as the core of the proposed approach. By incorporating dynamic population control, dynamic diversity monitoring, adaptive genetic operators, intelligent neighborhood search, and a parallel computing mechanism, the method enabled efficient optimization of multi-gate locations. Experimental results for parts with mesh sizes ranging from 50 000 to 140 000 elements showed that, for three-gate optimization problems, the proposed method improved computational efficiency by approximately 30 times compared with existing algorithms. Under various working conditions, the optimized gate layouts achieved filling balance superior to or comparable with that obtained using professional CAE software. In conclusion, the proposed parallel heuristic algorithm effectively addresses the combinatorial optimization problem of multi-gate placement, substantially improves design efficiency while maintaining optimization quality, and demonstrates significant practical value for engineering applications.
关键词
注塑成型 /
多浇口优化 /
等效流长 /
表面网格 /
并行遗传算法 /
混合爬山算法
Key words
injection molding /
multi-gate optimization /
equivalent flow length /
surface mesh /
parallel genetic algorithm /
hybrid hill-climbing algorithm
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基金
湖北省中央引导地方科技发展专项（2024BGA002）
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