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OA

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%(P<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.