Access the full text.
Sign up today, get DeepDyve free for 14 days.
(2020)
Analysis on the causes of collapse and internal convex defects in the process of coiling hot rolled steel
(2017)
Analysis and control of coil shape in strip hot rolling
(2022)
License plate detection and tracking algorithm based on Siamese network
(2020)
Development, application and prospect of process control technologies of thin slab continuous casting and rolling
(2021)
Optimization strategy for automatic control of coiling side guides of strip rolling mill
Boeun Kim, K. Ryu, Seongmin Heo (2022)
Mean squared error criterion for model-based design of experiments with subset selectionComput. Chem. Eng., 159
(2019)
Types of defective coils of 2250mm hot-rolled strip and field improvement
(2017)
Recent research on the slump/collapse of hot-rolled steel coil
(2016)
Cause analysis and improvement practice of coil defects in thin-gauge hot rolled steel coils
Gongzhuang Peng, Yinliang Cheng, Hongwei Wang, Weiming Shen (2022)
Industrial IoT-Enabled Prediction Interval Estimation of Mechanical Performances for Hot-Rolling SteelIEEE Transactions on Instrumentation and Measurement, 71
Zhengjia Huang, Jiajun Wu, Feng Xie (2021)
Automatic recognition of surface defects for hot-rolled steel strip based on deep attention residual convolutional neural networkMaterials Letters
Jianbo Yu, Xun Cheng, Qingfeng Li (2022)
Surface Defect Detection of Steel Strips Based on Anchor-Free Network With Channel Attention and Bidirectional Feature FusionIEEE Transactions on Instrumentation and Measurement, 71
Xiaodong Zhao, Yaran Chen, Jin Guo, Dongbin Zhao (2020)
A spatial-temporal attention model for human trajectory predictionIEEE/CAA Journal of Automatica Sinica, 7
(2022)
Health status identification of shearer based on denoising autoencoder and improved convolutional neural network
(2017)
New control technology of gradual tension recoiling for strip finishing line
(2020)
Surface defect recognition method based on multiple Siamese neural network
Xinglong Feng, Xian-wen Gao, Ling Luo (2021)
A ResNet50-Based Method for Classifying Surface Defects in Hot-Rolled Strip SteelMathematics
Huang Hui-ju (2014)
Steel strip surface defects classification based on machine learningComputer Engineering and Design
Jiahao Chen, Hong Qiao (2020)
Motor-Cortex-Like Recurrent Neural Network and Multitask Learning for the Control of Musculoskeletal SystemsIEEE Transactions on Cognitive and Developmental Systems, 14
Niclas Ståhl, G. Mathiason, G. Falkman, Alexander Karlsson (2019)
Using recurrent neural networks with attention for detecting problematic slab shapes in steel rollingApplied Mathematical Modelling
(2020)
Control of the coil shape of Tangshan iron and steel’s stainless steel 1580mm hot continuous rolling strip
Hanqing Xu, Ming Yang, Liuyuan Deng, Yeqiang Qian, Chunxiang Wang (2021)
Neutral Cross-Entropy Loss Based Unsupervised Domain Adaptation for Semantic SegmentationIEEE Transactions on Image Processing, 30
(2021)
Optimal analysis and improvement of automatic control strategy of coiler helper roller
(2019)
Strip steel surface defect detection based on improved YOLOv3 algorithm
(2021)
China steel rolling technology progress in the 13th five-year plan and prospection
(2019)
Control of coiled strip shape of Tiantie 1750 mm hot rolling mill
Lu Yanuo, Chen Bingcai, Chen Degang, Yan Shixiang, Li Shunping (2021)
Recognition Algorithm of Strip Steel Surface Defects Based on Attention ModelLaser & Optoelectronics Progress, 58
Jiahao Chen, Hong Qiao (2021)
Muscle-Synergies-Based Neuromuscular Control for Motion Learning and Generalization of a Musculoskeletal SystemIEEE Transactions on Systems, Man, and Cybernetics: Systems, 51
(2022)
Autoencoder and hypergraph-based semi-supervised broad learning system
M. Pezeshki, Linxi Fan, Philemon Brakel, Aaron Courville, Yoshua Bengio (2015)
Deconstructing the Ladder Network Architecture
(2020)
Flatness defect recognition based on RBF-BP network by hybrid optimization
Coil shape quality is the external representation of strip product quality, and it is also a direct reflection of strip production process level. This paper aims to predict the coil shape results in advance based on the real-time data through the designed algorithm.Design/methodology/approachAiming at the strip production scale and coil shape application requirements, this paper proposes a strip coil shape defects prediction algorithm based on Siamese semi-supervised denoising auto-encoder (DAE)-convolutional neural networks. The prediction algorithm first reconstructs the information eigenvectors using DAE, then combines the convolutional neural networks and skip connection to further process the eigenvectors and finally compares the eigenvectors with the full connect neural network and predicts the strip coil shape condition.FindingsThe performance of the model is further verified by using the coil shape data of a steel mill, and the results show that the overall prediction accuracy, recall rate and F-measure of the model are significantly better than other commonly used classification models, with each index exceeding 88%. In addition, the prediction results of the model for different steel grades strip coil shape are also very stable, and the model has strong generalization ability.Originality/valueThis research provides technical support for the adjustment and optimization of strip coil shape process based on the data-driven level, which helps to improve the production quality and intelligence level of hot strip continuous rolling.
Assembly Automation – Emerald Publishing
Published: Dec 6, 2022
Keywords: Hot strip rolling; Coil shape defects; Siamese network; DAE
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.