Abstract
Accurate prediction of material feeding before production for a printed circuit board (PCB) template can reduce the comprehensive cost caused by surplus and supplemental feeding. In this study, a novel hybrid approach combining fuzzy c-means (FCM), feature selection algorithm, and genetic algorithm (GA) with back-propagation networks (BPN) was developed for the prediction of material feeding of a PCB template. In the proposed FCM–GABPN, input templates were firstly clustered by FCM, and seven feature selection mechanisms were utilized to select critical attributes related to scrap rate for each category of templates before they are fed into the GABPN. Then, templates belonging to different categories were trained with different GABPNs, in which the separately selected attributes were taken as their inputs and the initial parameter for BPNs were optimized by GA. After training, an ensemble predictor formed with all GABPNs can be taken to predict the scrap rate. Finally, another BPN was adopted to conduct nonlinear aggregation of the outputs from the component BPNs and determine the predicted feeding panel of the PCB template with a transformation. To validate the effectiveness and superiority of the proposed approach, the experiment and comparison with other approaches were conducted based on the actual records collected from a PCB template production company. The results indicated that the prediction accuracy of the proposed approach was better than those of the other methods. Besides, the proposed FCM–GABPN exhibited superiority to reduce the surplus and/or supplemental feeding in most of the case in simulation, as compared to other methods. Both contributed to the superiority of the proposed approach.
Highlights
Printed circuit board (PCB) is found in practically all electrical and electronic equipment, being the base of the electronics industry [1]
linear correlation (LC) [20], maximum information coefficient (MIC) [21], recursive feature elimination (RFE) [22], LR [23], lasso regression [24], ridge regression [23], and random forest regression (RFR) [24] seven feature selection approaches were employed to select critical attributes related to the scrap rate for each category of samples
In order to enhance the accuracy of material feeding prediction of a printed circuit board (PCB) template, an ensemble predictor fuzzy c-means (FCM)–GABPN was proposed
Summary
Printed circuit board (PCB) is found in practically all electrical and electronic equipment, being the base of the electronics industry [1]. Due to the rapid development of computer, communication, consumer electronics, 5G, and automotive electronics, as well as the update of their products, the demand of PCB orders with specialized design features and manufacturing requirements, often referred to as a PCB template in the factory, has increased rapidly. The mode of production for a PCB factory with lots of template orders has changed from mass production to customer-oriented small-batch production, and causes companies to face serious challenges. Accurate prediction of material feeding for each order is one of the critical problems.
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