基于数据挖掘的印制电路样板投料优化

doi:10.16182/j.issn1004731x.joss.201807028

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (7): 2656-2665.doi: 10.16182/j.issn1004731x.joss.201807028

基于数据挖掘的印制电路样板投料优化

吕盛坪¹, 乐强生¹, 刘涛²

1. 华南农业大学工程学院,广州 510642;
2. 安徽建筑大学机械与电气工程学院,合肥 230601

收稿日期:2017-04-20 出版日期:2018-07-10 发布日期:2019-01-08
作者简介:吕盛坪(1982-),男,湖南新邵,博士,副教授,研究方向为生产计划、工艺规划与调度优化、工业数据挖掘等。
基金资助:
国家自然科学基金(51605169),广东省自然基金(2014A030310345)

Optimization of Material Release for Printed Circuit Board Template Based on Data Mining

Lü Shengping¹, Yue Qiangsheng¹, Liu Tao²

1. School of Engineering, South China Agricultural University, Guangzhou 510642, China;
2. School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230601, China

Received:2017-04-20 Online:2018-07-10 Published:2019-01-08

摘要/Abstract

摘要： 为了更准确确定PCB (Printed Circuit Board)样板投料,基于车间历史数据开展挖掘分析。梳理报废率关联参数,利用假设检验优选报废率预测建模参数。构建多元线性回归、卡方自动相互作用检测器、人工神经网络和支持向量机预测模型;定义余数入库率和补投率及两者加权和评价指标,开展投料仿真,对比优选多元线性回归预测机制。引入调节系数,结合企业成本模型进行优选;开发实施投料控件并进行验证;结果证明余数入库率和补投率较实际值均有明显降低,可减少样板生产物料投入、库存浪费、补投拖期等成本,为PCB样板投料优化提供新的参考手段。

关键词: PCB, 报废率, 数据挖掘, 预测, 投料优化

Abstract: Data mining were employed for the optimization of material release of PCB (Printed Circuit Board) template. PCB scrap ratio related parameters were specified and prediction model variables were chosen according to hypothesis test. Multiple linear regression (MLR), Chi-squared automatic interaction detector, artificial neural network and support vector machine approaches for the prediction of scrap ratio were employed. Evaluation indictors called as superfluous ratio, supplement release ratio and weighted sum of the two were presented; the material release simulation was conducted and then the four approaches were compared and MLR was taken as the preferred one. Adjust coefficient was introduced and optimized according to factory’s cost model. Finally, material release tool were developed and verified. Comparison results shown that superfluous and supplement release ratio has significant reduction which indicates that the approach can systematically reduce the cost of material release, waste of inventory, tardiness and so on.

Key words: PCB, scrap ratio, data mining, prediction, optimization of material release

中图分类号:

TN41
TP391.7

吕盛坪, 乐强生, 刘涛. 基于数据挖掘的印制电路样板投料优化[J]. 系统仿真学报, 2018, 30(7): 2656-2665.

Lü Shengping, Yue Qiangsheng, Liu Tao. Optimization of Material Release for Printed Circuit Board Template Based on Data Mining[J]. Journal of System Simulation, 2018, 30(7): 2656-2665.

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基于数据挖掘的印制电路样板投料优化

Optimization of Material Release for Printed Circuit Board Template Based on Data Mining

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