# An FCM-GABPN Ensemble Approach for Material Feeding Prediction of Printed Circuit Board Template
## 1 Introduction  
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 therefore causes companies to face serious challenges. Accurate prediction of material feeding for each order is one of the critical problems. After the feeding area and production panel (production unit) of each template order is accurately predicted, several goals (including the reduction of comprehensive cost caused overproduction or supplemental feeding, alleviation of environment pollution, improvement of on-time delivery, etc.) can be simultaneously achieved. However, it is difficult to determine the material feeding area of each PCB template order in advance of the production by manual feeding. Many factories undergo the violent fluctuation in both surplus and supplemental feeding by empirical manual feeding. Individualized surplus templates can be placed only in inventory or directly disposed, while supplemental feeding brings extra production costs and increases the probability of delivery tardiness compensation [2]. Furthermore, surplus products bring extra chemical and heavy metal pollution for production and disposal. This motivates us to explore the pattern of historical records that facilitate more reasonable and accurate prediction of material feeding for new template orders. There are many applications of data mining (DM) or big data for quality improvement and optimization of PCB production [3]. [PERSON] et al. [4] developed a data mining (DM)-based approach to predict the yield of a PCB, using the event sequence. [PERSON] [5] proposed a hybrid DM approach for soldering quality classification by using self-organizing map (SOM) and K-means, based on the statistical process control databases. DB-based PCB manufacturing process optimization has also attracted many researches, such as the parameter optimization of hot solder dip [6], stencil printing process [7; 8], reflow soldering [9; 10], fluid dispensing for microchip encapsulation [11], and wave soldering [12; 13] for a component surface mount on a PCB. These models always combined artificial neural network (ANN), support vector machine (SVM), and multiple linear regression (MLR) for quality prediction with GA for parameters optimization simultaneously [7; 10; 11]. Meanwhile, many DM approaches like adaptive genetic algorithm (GA)-artificial neural network (ANN) [14], decision tree (DT) [15] have been employed for the defect diagnosis of PCB. The DM and/or big data were also widely adopted for smart production in different industries, not only for PCB manufacturing, and many reviews of these applications were reported in recent two years [3; 16; 17]. However, few of the aforementioned studies are on the prediction and optimization of material feeding in PCB template orders. In addition, the reviewed studies seldom considered the situation of diverse examples with different critical influence factors that require different prediction models to improve the prediction accuracy. Considering the abovementioned requirement of a PCB template, [PERSON] et al. [2] developed a hybrid model, multiple structural change (MSC)-ANN, to predict the feeding panel for each template, in which the template samples were pre-classified based on the required panel by the multiple structural change model. Then, the critical attributes for each category were selected based on neighborhood component approach; finally, the ANN prediction models were established for each category.