A CS-AdaBoost-BP model for product quality inspection
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A CS-AdaBoost-BP model for product quality inspection Zengyuan Wu1 · Caihong Zhou2 · Fei Xu3 · Wengao Lou4 Accepted: 9 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Quality inspection is essential in preventing defective products from entering the market. Due to the typically low percentage of defective products, it is generally challenging to detect them using algorithms that aim for the overall classification accuracy. To help solve this problem, we propose an ensemble learning classification model, where we employ adaptive boosting (AdaBoost) to cascade multiple backpropagation (BP) neural networks. Furthermore, costsensitive (CS) learning is introduced to adjust the loss function of the basic classifier of the BP neural network. For clarity, this model is called a CS-AdaBoost-BP model. To empirically verify its effectiveness, we use data from home appliance production lines from Bosch. We carry out tenfold cross-validation to evaluate and compare the performance between the CSAdaBoost-BP model and three existing models: BP neural network, BP neural network based on sampling, and AdaBoost-BP. The results show that our proposed model not only performs better than the other models but also significantly improves the ability to identify defective products. Furthermore, based on the mean value of the Youden index, our proposed model has the highest stability. Keywords Imbalanced data · Quality inspection · AdaBoost algorithm · Cost-sensitive learning · Machine learning
1 Introduction and literature review Product quality is related to the core interests of consumers and directly determines the survival and development of a firm. If a firm produces and sells products with quality defects, its reputation can be severely damaged. In the long run, this will negatively affect its competi-
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Wengao Lou [email protected]
1
College of Economics and Management, China Jiliang University, Hangzhou, China
2
School of Economics and Business Administration, Chongqing University, Chongqing, China
3
Department of Mathematics, Wilfrid Laurier University, Waterloo, ON, Canada
4
Shanghai Business School, Shanghai, China
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Annals of Operations Research
tiveness (Giovanni 2020). For example, Samsung’s mobile phones, which caused occasional explosions in 2016, significantly reduced its market value. In the production process, quality inspection is directly related to product quality. Therefore, it is essential for a firm to develop an effective quality inspection system. Manual sampling inspection is common, which mainly depends on the experience of inspectors. However, with the expansion of production volume and the increase of product complexity, manual quality inspection has become inadequate. In contrast, the artificial intelligence-based quality inspection can be superior in terms of cost, efficiency, and accuracy. For example, Intel applied smart technology to chip quality inspection, which saved about $3 million in manufacturing costs (Mangal and Kumar 2016). The main
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