中国塑料 ›› 2018, Vol. 32 ›› Issue (10): 99-104.DOI: 10.19491/j.issn.1001-9278.2018.10.015

• 加工与应用 • 上一篇    下一篇

基于DOE、RSM及PSO的大尺寸板类塑件的翘曲优化

刘月云,刘碧俊   

  1. 江苏食品药品职业技术学院机电工程学院
  • 收稿日期:2018-04-08 修回日期:2018-09-02 出版日期:2018-10-26 发布日期:2018-10-26
  • 基金资助:

    淮安市自然科学研究计划(HABZ201712);江苏省高等学校自然科学研究项目(18KJB460013)

Warpage Optimization of Large-size Plate Plastic Parts Based on DOE, RSM and PSO

  • Received:2018-04-08 Revised:2018-09-02 Online:2018-10-26 Published:2018-10-26

摘要: 首先通过实验设计(DOE)分析得出对柜式空调面板翘曲变形影响较大的工艺参数为熔体温度、模具温度、保压压力和保压时间。其次,以这4个工艺参数为实验变量,以面板的翘曲量为目标,采用响应面法(RSM)构建出两者之间的二阶响应面模型,并优化出翘曲量最小的工艺参数,翘曲量预测误差率仅为2.486 %,模型精度较高。最后,运用粒子群算法(PSO)对二阶响应面模型进行迭代寻优,得出最优工艺参数。验证结果表明,PSO优化误差为4.882 %,相比于RSM优化,翘曲量实际值由5.217 mm降为3.459 mm,降低了38.367 %,优化效果较好。

Abstract: Design of experiments (DOE) was first adopted to analyze the process parameters of a cabinet type air conditioning panel including melt temperature, mold temperature, pressure holding pressure and pressure holding time, which influenced the warpage deformation of the injectionmolded part significantly.Subsequently, the response surface methodology (RSM) was applied to construct the secondorder response surface model by taking four process parameters as experimental variables and the warpage of the panelas a goal.The process parameters with minimum warpage were optimized, which led to a high accuracy for the model as well as alow prediction error rate of 2.486 %.Finally, the optimal of process parameters were obtained by particle swarm optimization (PSO) for the twoorder response surface model.The verification results indicated that the optimization error of PSO was 4882 %.Compared to the RSM optimization, the actual value of warpage was reduced by 38367 % from 5217 mm to 3459 mm, which was reduced, suggesting that this optimization effect was much better.