复合材料科学与工程 ›› 2026, Vol. 0 ›› Issue (5): 89-97.DOI: 10.19936/j.cnki.2096-8000.20260528.012

• 设计与工艺 • 上一篇    下一篇

基于背包问题与动态规划的纤维铺叠过程料片存储优化研究

李论1, 王琢然1, 高航2   

  1. 1.大连外国语大学 软件学院,大连 116044;
    2.大连理工大学 机械工程学院,大连 116024
  • 收稿日期:2025-04-07 出版日期:2026-05-28 发布日期:2026-07-01
  • 作者简介:李论(1991—),男,博士,讲师,研究方向为复合材料自动铺放控制、复合材料缺陷检测等,lilun@dlufl.edu.cn。
  • 基金资助:
    辽宁省教育厅高等学校基本科研项目(LJ212410172014)

Storage optimization for fiber layup material blanks using knapsack problem and dynamic programming

LI Lun1, WANG Zhuoran1, GAO Hang2   

  1. 1. School of Software, Dalian University of Foreign Languages, Dalian 116044, China;
    2. School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
  • Received:2025-04-07 Online:2026-05-28 Published:2026-07-01

摘要: 针对航空航天桨叶制造中纤维铺叠工艺的料片收纳优化问题,提出一种融合背包问题(knapsack problem)与动态规划的混合算法。该算法通过建立料片价值-空间双目标评估模型,构建以料仓层数、面积和利用率为约束条件的多目标优化函数,采用动态规划策略实现料片分层布局优化。试验选取料片总量为17种的典型算例。结果表明,该算法在多层空间待优化场景下表现出高效性。试验将4层方案的初始平均利用率79.3%提升至2层方案的平均空间利用率99.9%,较传统方法提升了25.9%;通过动态调整料仓空间的层间匹配,总空间减少了20.5%。研究证实,该算法通过动态调整料仓参数组合,可有效平衡空间利用率与工艺价值,为复杂构件制造提供高效收纳解决方案。

关键词: 料片收纳, 动态规划, 背包问题, 组合优化, 复合材料

Abstract: Optimizing material storage for composite material layup in aerospace blade manufacturing, a novel hybrid algorithm integrating a knapsack problem and dynamic programming is proposed. The algorithm establishes a dual-objective evaluation model for material value and spatial efficiency, constructing a multi-objective optimization function constrained by storage layer count, area, and utilization rate. A dynamic programming approach combined with backtracking pruning strategies is employed to optimize the hierarchical layout of material sheets. Experimental validation using a typical case with 17 material types demonstrates the algorithm’s efficacy. In multi-layer optimization scenarios, the average spatial utilization rate improved from 79.36% to 99.89%, representing a 25.9% enhancement over traditional methods. Additionally, the interlayer matching of the silo space is dynamically adjusted, the total space is reduced by 20.5%. The results confirm that the algorithm effectively balances spatial efficiency and process value through dynamic parameter optimization, offering a high-performance storage solution for complex component manufacturing.

Key words: material storage optimization, dynamic programming, knapsack problem, combinatorial optimization, composites

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