Abstract
Manufacturing systems contain a large number of parameters, and a proper configuration of parameters is very important to ensure the stability of product quality. Traditional configuration methods rely heavily on manual tuning, which is labor-intensive, time-consuming, and poor performance. In this paper, we propose to build deep learning models on the vast amount of industrial data collected by IIoT devices for automatic configuration tuning. In order to address key challenges such as high data redundancy, limited device capacity, latency-sensitivity, and system heterogeneity, we propose a two-level federated deep learning framework. We first extract representative features from redundant data, and reduce network traffic and latency through joint training on plants and the cloud. Timely configuration tuning is made through local models of plants, and the tuning accuracy is improved through the global model in the cloud. We have deployed and evaluated the performance of the proposed model in real-world smart manufacturing systems, and the experimental results confirm its effectiveness.
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Acknowledgment
The work described in this paper was supported by the National Natural Science Foundation of China (61802003, 61872006), and the Anhui Innovation Program for Overseas Students.
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Zhang, Y., Li, X., Zhang, P. (2020). Real-Time Automatic Configuration Tuning for Smart Manufacturing with Federated Deep Learning. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds) Service-Oriented Computing. ICSOC 2020. Lecture Notes in Computer Science(), vol 12571. Springer, Cham. https://doi.org/10.1007/978-3-030-65310-1_22
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