Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study
AbstractUnder the international background of the transformation and promotion of manufacturing, the Chinese government proposed the “Made in China 2025” strategy, which focused on the improvement of a quality-based innovation ability. Moreover, predicting manufacturing quality is one of the crucial measures for quality management. Accurate prediction is closely related to the feature learning of manufacturing processes. Therefore, two categories of intelligent learning approaches, i.e., shallow learning and deep learning, are investigated and compared for manufacturing quality prediction in this paper. Specifically, the feed forward neural network (FFNN) with one hidden layer and the least squares support vector machine (LSSVM) with no hidden layers are selected as the representatives for shallow learning, and the deep restricted Boltzmann machine (DRBM) and the stack autoencoder (SAE) are chosen as the representatives for deep learning. The manufacturing data is collected from a competition about manufacturing quality control in the Tianchi Data Lab of China. The experiments show that the deep framework overwhelms the shallow architecture in terms of mean absolute percentage error, root-mean-square error, and threshold statistics. In addition, the prediction results also indicate that the performances depend on the length of the training data. That is, the bigger the sample size is, the better the performance is. View Full-Text
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Bai, Y.; Sun, Z.; Deng, J.; Li, L.; Long, J.; Li, C. Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study. Sustainability 2018, 10, 85.
Bai Y, Sun Z, Deng J, Li L, Long J, Li C. Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study. Sustainability. 2018; 10(1):85.Chicago/Turabian Style
Bai, Yun; Sun, Zhenzhong; Deng, Jun; Li, Lin; Long, Jianyu; Li, Chuan. 2018. "Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study." Sustainability 10, no. 1: 85.
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