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

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 mod...

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Bibliographic Details
Published in:Journal of intelligent manufacturing 2020-08, Vol.31 (6), p.1511-1529
Main Authors: Liu, Zhenyu, Zhang, Donghao, Jia, Weiqiang, Lin, Xianke, Liu, Hui
Format: Article
Language:English
Subjects:
Assembly
Business and Management
Confidence intervals
Control
Correlation coefficients
Information transfer
Machines
Manufacturing
Mechatronics
Processes
Product quality
Production
Robotics
Uncertainty
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Summary: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.
ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-019-01530-8