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Journal of Electrical and Computer Engineering
Volume 2018 (2018), Article ID 1852938, 17 pages
Research Article

Data Mining for Material Feeding Optimization of Printed Circuit Board Template Production

1College of Engineering, South China Agricultural University, Guangzhou 510642, China
2Department of Industrial, Manufacturing and Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA

Correspondence should be addressed to Shengping Lv; nc.ude.uacs@gnipgnehsvl

Received 7 November 2017; Accepted 18 February 2018; Published 1 April 2018

Academic Editor: Ephraim Suhir

Copyright © 2018 Shengping Lv et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Improving the accuracy of material feeding for printed circuit board (PCB) template orders can reduce the overall cost for factories. In this paper, a data mining approach based on multivariate boxplot, multiple structural change model (MSCM), neighborhood component feature selection (NCFS), and artificial neural networks (ANN) was developed for the prediction of scrap rate and material feeding optimization. Scrap rate related variables were specified and 30,117 samples of the orders were exported from a PCB template production company. Multivariate boxplot was developed for outlier detection. MSCM was employed to explore the structural change of the samples that were finally partitioned into six groups. NCFS and ANN were utilized to select scrap rate related features and construct prediction models for each group of the samples, respectively. Performances of the proposed model were compared to manual feeding, ANN, and the results indicate that the approach exhibits obvious superiority to the other two methods by reducing surplus rate and supplemental feeding rate simultaneously and thereby reduces the comprehensive cost of raw material, production, logistics, inventory, disposal, and delivery tardiness compensation.