基于点云数据和灰色关联分析的连接器逆向补偿优化

Connector Reverse Compensation Optimization Based on Point Cloud Data and Grey Correlation Analysis

以线束连接器上盖为研究对象,运用灰色关联分析将体积收缩率、翘曲变形量和导轨偏移量三个质量指标的多目标优化问题转换为关联度均值的单目标优化。基于响应面试验,构建工艺参数和关联度均值间的优化模型,研究各成型工艺参数在不同水平下对关联度均值的影响。在此基础之上,依据变形预补偿原理,以点云数据处理,匹配不同的缩放比,收缩补偿值以反向建模。经对比验证,收缩比为1.1,收缩补偿值为3%所对应的补偿模型同初始模型的偏差最小。结论表明,相较于工艺参数优化,逆向变形补偿方法能够进一步降低塑件变形。

Taking the top cover of wire harness connector as the research object,the multi-objective optimization problem of three quality indexes,volume shrinkage,warpage and guide offset,was transformed into a single objective optimization problem of correlation degree mean value by using grey correlation analysis.Based on the response surface test,the response surface model between the process parameters and the mean of the correlation degree was constructed to study the influence of each molding process parameter on the mean of the correlation degree at different levels.On this basis,according to the principle of deformation pre-compensation,the point cloud data is processed to match different scaling ratios,and the contraction compensation value is used for reverse modeling.By comparison and verification,the compensation model with contraction ratio of 1.1 and contraction compensation value of 3%has the smallest deviation from the initial model.The results show that comparing with the numerical optimization,the reverse deformation compensation can further reduce the deformation of plastic parts.