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An adversarial bidirectional serial–parallel LSTM-based QTD framework for product quality prediction

Author

Listed:
  • Zhenyu Liu

    (Zhejiang University)

  • Donghao Zhang

    (Zhejiang University)

  • Weiqiang Jia

    (Zhejiang University)

  • Xianke Lin

    (University of Ontario Institute of Technology)

  • Hui Liu

    (Zhejiang University)

Abstract

In order to capture temporal interactions among processes in manufacturing and assembly processes, an end-to-end unified product quality prediction framework called QTD is proposed in this paper. It consists of three modules: quality embedding model pool, temporal-interactive model, and decoding model. Besides, to handle the information transfer and integration problems in the time direction of parallel processes, a novel bidirectional serial–parallel LSTM (Bi-SP-LSTM) is devised as an instantiated model of temporal-interactive model. Bi-SP-LSTM is an extension of bidirectional long short-term memory. Moreover, an unsupervised task and a loss function named adversarial focal loss have been designed to give the framework the ability to assess heteroscedastic uncertainty in classification task due to intrinsic uncertainty in data. Furthermore, experiments are devised based on a subset of a public dataset from Kaggle competition to demonstrate the validity of the proposed framework. Compared with other latest methods, the proposed framework is verified to be more accurate and robust. Taking Matthews correlation coefficient as an example, the adversarial Bi-SP-LSTM-based QTD framework is superior to the best existing methods with 95% confidence interval in most cases, and its mean MCC is 4.88% higher than the best existing method. The results suggest that the proposed framework has a broad application prospect for quality prediction in manufacturing and assembly processes.

Suggested Citation

  • Zhenyu Liu & Donghao Zhang & Weiqiang Jia & Xianke Lin & Hui Liu, 2020. "An adversarial bidirectional serial–parallel LSTM-based QTD framework for product quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1511-1529, August.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:6:d:10.1007_s10845-019-01530-8
    DOI: 10.1007/s10845-019-01530-8
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    References listed on IDEAS

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    Cited by:

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    2. Shuai Ma & Jiewu Leng & Pai Zheng & Zhuyun Chen & Bo Li & Weihua Li & Qiang Liu & Xin Chen, 2025. "A digital twin-assisted deep transfer learning method towards intelligent thermal error modeling of electric spindles," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 1659-1688, March.
    3. Dayuan Wu & Ping Yan & You Guo & Han Zhou & Jian Chen, 2022. "A gear machining error prediction method based on adaptive Gaussian mixture regression considering stochastic disturbance," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2321-2339, December.
    4. Yi Zhang & Peng Peng & Chongdang Liu & Yanyan Xu & Heming Zhang, 2022. "A sequential resampling approach for imbalanced batch process fault detection in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1057-1072, April.
    5. Ning Ge & Guanghao Li & Li Zhang & Yi Liu, 2022. "Failure prediction in production line based on federated learning: an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2277-2294, December.
    6. Wei Guo & Yijin Wang & Xin Chen & Pingyu Jiang, 2024. "Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application," Journal of Intelligent Manufacturing, Springer, vol. 35(4), pages 1439-1454, April.
    7. Hasan Tercan & Tobias Meisen, 2022. "Machine learning and deep learning based predictive quality in manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1879-1905, October.

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