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基于优选模型和灰狼算法的注塑工艺参数优化

Optimization of Injection Molding Process Parameters Based on Preferred Model and Gray Wolf Algorithm

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【作者】 林峰孙永华李国琳李西兵连灿鑫

【Author】 LIN Feng;SUN Yonghua;LI Guolin;LI Xibing;LIAN Canxin;Department of Mechanical and Electronic Engineering,Fuzhou Polytechnic;Department of Intelligent Manufacturing, Shandong Labor Vocational and Technical College;The Engineering Research Center for CAD/CAM of Fujian Universities (Putian University);College of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University;

【通讯作者】 李西兵;

【机构】 福州职业技术学院,机电工程系山东劳动职业技术学院,智能制造系CAD/CAM福建省高校工程研究中心(莆田学院)福建农林大学,机电工程学院

【摘要】 采用Moldflow软件对食品保鲜盒盖的注塑成型过程进行模拟分析,目的是通过优化注塑工艺参数,最大限度地减小产品的体积收缩率,从而提高产品质量。采用筛选试验设计的方法,确定对注塑成型过程影响较显著的参数。然后,构建多个近似模型,并对这些模型进行细致的比较分析,筛选出性能最佳的模型。最后,利用灰狼优化算法对最优模型进行参数优化,得到最优注塑工艺参数组合,并进行模拟验证和实际验证。结果表明,采用优化后的注塑工艺参数组合制备的产品的体积收缩率显著减小,由初始的5.837%下降至4.01%,下降了31.3%,证明了结合计算机模拟、更优的模型和智能优化算法在注塑工艺优化中具有有效性及较好的应用潜力。

【Abstract】 The process of injection molding for the fresh-keeping box lid was simulated using Moldflow software, with the objective of minimizing volume shrinkage through the optimization of injection molding process parameters. Initially, significant parameters affecting the molding process were identified through the screening test design. Subsequently, several approximate models were constructed and meticulously compared to select the model with optimal performance. Finally, the Grey Wolf Optimization algorithm was applied to the selected model to optimize the parameters, leading to an ideal combination of injection molding process parameters. Both simulation and practical validation confirmed that the optimized parameters substantially reduced the volume shrinkage rate, from an initial 5.837% to 4.01%, marking a significant reduction of 31.3%. This outcome demonstrates the efficacy and potential of combining computer simulation, better models and intelligent optimization algorithms in enhancing injection molding processes.

【基金】 2023年度福州职业技术学院科研基金(FZYKJJJCX202301);CAD/CAM福建省高校工程研究中心开放式基金(K202207)
  • 【分类号】TQ320.662
  • 【下载频次】120
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