Multi-temporal process quality prediction based on graph neural network
Authors: 
Bin Yi, Wenqi Li, Jun Tang, Xiaohua Gao, Bing Zhou, Xiaoli Xu, Peng Qin, Wenqiang LinAuthors Info & Claims
Bin Yi, Wenqi Li, Jun Tang, Xiaohua Gao, Bing Zhou, Xiaoli Xu, Peng Qin, Wenqiang LinAuthors Info & ClaimsPages 183 - 188
Abstract
For the complex dependencies of production data in time and space, a multi-temporal processing process quality prediction model GLSTM based on graph neural networks is proposed, which uses graph structure data to model the process relationships among production indicators, uses graph neural networks to aggregate spatial information among production indicators, and uses long and short term memory networks to model the complex dependencies of shop floor processing quality indicator sequences in time, and the experimental The results show that the model is able to achieve relative performance improvements of 5.40%, 15.04% and 0.30% compared to time series analysis methods.
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- Multi-temporal process quality prediction based on graph neural network
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Published In
March 2023
193 pages
ISBN:9781450399531
DOI:10.1145/3589572
Copyright © 2023 ACM.
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Publication History
Published: 09 June 2023
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- Refereed limited
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- Key Scientific and Technological Projects of Yunnan China Tobacco Industry Co., Ltd
Conference
ICMVA 2023
ICMVA 2023: 2023 The 6th International Conference on Machine Vision and Applications
March 10 - 12, 2023
Singapore, Singapore
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