基于木材纹理-力学性能协同优化的实木家具零部件排料方法研究

朱正坤; 康小燕; 肖粲; 王孜豪

doi:10.12326/j.2096-9694.2025120

研究报告 | 浏览量 : 0 下载量: 9 CSCD: 0 CNKI被引量: 0 Crossref: 0 Wos: 0 Scopus: 0 更多指标

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基于木材纹理-力学性能协同优化的实木家具零部件排料方法研究
Allocation Method of Solid Wood Furniture Components Based on Collaborative Optimization of Wood Texture and Mechanical Properties
1.江西环境工程职业学院，江西赣州 341000
2.江西省林业局木竹家具绿色制造重点实验室，江西赣州 341000
3.中南林业科技大学材料与能源学院，湖南长沙 410004
4.江西铂源家具有限公司，江西赣州 341000
2026年40卷第2期页码：80-87
DOI： 10.12326/j.2096-9694.2025120
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朱正坤,康小燕,肖粲等.基于木材纹理-力学性能协同优化的实木家具零部件排料方法研究[J].木材科学与技术,2026,40(02):80-87. DOI： 10.12326/j.2096-9694.2025120.

ZHU Zhengkun,KANG Xiaoyan,XIAO Can,et al.Allocation Method of Solid Wood Furniture Components Based on Collaborative Optimization of Wood Texture and Mechanical Properties[J].Chinese Journal of Wood Science and Technology,2026,40(02):80-87. DOI： 10.12326/j.2096-9694.2025120.

摘要

针对实木家具排料过程中木材利用率低与零部件力学性能难以兼顾的问题，提出一种融合木材纹理-力学性能映射模型与动态分割策略的改进遗传算法。该方法通过量化木材纹理方向与零部件抗弯强度之间的映射关系，将力学性能指标引入改进遗传算法的适应度函数中；创新性地应用Bézier曲线进行轮廓拟合与动态数控路径规划，实现对异形零部件的高精度和高效率加工；同时引入自适应变异算子，以平衡算法全局探索与局部寻

优能力。基于企业实际板材与零部件数据的研究结果表明，与人工排料、商业排料软件及标准遗传算法排料相比，改进遗传算法排料将木材利用率提升至92.3%，纹理匹配合格率达到95.2%，零部件平均抗弯强度提高17.6%（

0.01），且收敛时间控制在8.2 min以内，满足实际生产需求。消融实验进一步验证了改进遗传算法中各模块的必要性与协同作用。研究为实现实木家具排料过程的木材原料高效利用与产品性能协同优化提供可行的解决方案。

Abstract

To address the problem of low wood utilization rate and difficulty in balancing the mechanical properties of components during the cutting process of solid wood furniture

an improved genetic algorithm that integrates a texture-mechanics mapping model and a dynamic segmentation strategy was proposed. The method quantifies the relationship between wood grain direction and component bending strength

incorporating this mechanical performance indicator into the fitness function of the improved genetic algorithm. Bézier curves were applied for contour fitting and dynamic computer numerical control path planning to enable high-precision and efficient processing of specially shaped components. Furthermore

an adaptive mutation operator was introduced to balance global exploration and local optimization capabilities. Experiments results based on actual enterprise panel and components data demonstrated that

compared to manual allocation

commercial software and a standard genetic algorithm

the improved genetic algorithm increased the wood utilization rate to 92.3%

achieved a grain orientation matching rate of 95.2%

and improved the average component flexural strength by 17.6% (

0.01)

and controls the algorithm convergence time within 8.2 minutes

meeting production requirements. The ablation experiment further verified the necessity and collaborative effect of each module in the improved genetic algorithm. This research provided a feasible solution for achieving efficient resource utilization and collaborative optimization of product performance in the cutting process of solid wood furniture.

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