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Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study

Author

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  • Yun Bai

    (School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, China)

  • Zhenzhong Sun

    (School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, China)

  • Jun Deng

    (School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, China)

  • Lin Li

    (School of Computer Science and Network Security, Dongguan University of Technology, Dongguan 523808, China)

  • Jianyu Long

    (School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, China)

  • Chuan Li

    (School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, China)

Abstract

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.

Suggested Citation

  • Yun Bai & Zhenzhong Sun & Jun Deng & Lin Li & Jianyu Long & Chuan Li, 2017. "Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study," Sustainability, MDPI, vol. 10(1), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:10:y:2017:i:1:p:85-:d:124881
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    1. Jin-peng Liu & Chang-ling Li, 2017. "The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection," Sustainability, MDPI, vol. 9(7), pages 1-20, July.
    2. Donghyun Lee & Suna Kang & Jungwoo Shin, 2017. "Using Deep Learning Techniques to Forecast Environmental Consumption Level," Sustainability, MDPI, vol. 9(10), pages 1-17, October.
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