1. Prediction of camber defect of hot-rolled plates using sequence to sequence learning incorporating attention mechanism.
- Author
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Dong, Zishuo, Li, Xu, Luan, Feng, Cao, Jianzhao, Ding, Jingguo, and Zhang, Dianhua
- Subjects
STANDARD deviations ,HOT rolling ,COMPUTER vision ,REAL variables ,PREDICTION models - Abstract
Camber in hot-rolled plates significantly impacts product quality and rolling process stability, making accurate camber prediction crucial. However, it is challenging to measure asymmetric factors impacting camber in real production, hindering the ability of current models to predict and analyze the overall camber of rolled plates. This study proposes a method that combines asymmetric fluctuations of measurable variables with deep learning to predict camber. We developed a data analysis platform for plate processing and constructed a camber dataset using machine vision technology. During the modeling phase, a hot-rolled plate camber prediction model, BiLSTM-AM-Seq2Seq, was proposed, integrating a bidirectional long short-term memory network, an attention mechanism, and a sequence-to-sequence model. Additionally, an improved scheduled sampling method was also introduced to steer model training. Model performance was evaluated with real-world production data, demonstrating superior accuracy and stability. Specifically, the model achieved a mean absolute error of 12.29 mm and a root mean square error of 17.32 mm. Consequently, this model meets the demands of practical production and addresses the need for overall camber prediction of hot rolled plates. • Data-driven prediction model for overall camber shape of rolled plates is proposed. • Modeling using fluctuations of observable variables to address the absence of key asymmetric variables in real production. • Data analysis platform was developed and real data were used to demonstrate the superiority of the proposed model. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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