Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study

Under 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 measure...

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Vydané v:Sustainability Ročník 10; číslo 1; s. 85
Hlavní autori: Bai, Yun, Sun, Zhenzhong, Deng, Jun, Li, Lin, Long, Jianyu, Li, Chuan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.01.2018
Predmet:
China
comparative study
least squares
Manufacturing
Neural networks
prediction
Quality
quality control
support vector machines
Sustainability
sustainable development
sustainable manufacturing
China
ISSN:2071-1050, 2071-1050
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Shrnutí:Under 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.
Bibliografia:ObjectType-Article-1
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ISSN:2071-1050
2071-1050
DOI:10.3390/su10010085