Modeling and Simulation of Void Closure during Steckel Mill Rolling for Steel Plate

Ganguly, Shouvik; Wang, Xin; Chandrashekhara, K.; Buchely, Mario F.; Lekakh, Semen Naumovich; O’Malley, Ronald J.; Bai, D.; Wang, Yufeng

doi:10.1002/srin.202000293

Cited by 8 publications

(1 citation statement)

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“…The strip quality prediction based on the mathematical model ignores and simplifies the influence of many on-site factors, and the dynamic effects sometimes produce false results, leading to large errors. Later, people paid attention to deformation laws in the forming process, and the finite element method and finite element simulation software were applied to the simulation of the strip production process and the quality control of the strip [6,7]. However, the finite element analysis software has high requirements for the user's ability, and it often requires level-by-level training to use it proficiently.…”

Section: Related Workmentioning

confidence: 99%

Quality Prediction of Strip in Finishing Rolling Process Based on GBDBN‐ELM

Wang

Chen

2021

Journal of Sensors

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With the continuous development of the manufacturing industry, the requirement for strip steel quality is becoming higher and higher in automobile manufacturing, mechanical processing, and electronic and electrical industries. The precise control of strip quality depends on the accurate prediction of strip quality to a certain extent. However, the data collected by a large number of sensors on the complex strip production line and generated by the computer control system presents the characteristics of high dimensionality, high coupling, and nonlinearity, which brings difficulties to the prediction of strip quality. The continuous production of massive data in the production line also forces steel enterprises to seek new data mining methods, mining the relationship between sensor data to predict and control strip quality. To solve these problems, this paper proposes a GBDBN-ELM model, which is more efficient and more accurate than other algorithms. In this model, the RBM in DBN is replaced with GBRBM, so that RBM no longer depends on the binary distribution, can handle continuity values, and retain more data features. In order to solve the problem of too long DBN training time, this article replaces the BP network in DBN with an ELM regression model. The ELM model predicts the strip quality based on the extracted data abstract features, thereby improving the model’s prediction accuracy and shortening the training time. In this paper, the GBDBN-ELM model is compared with the BP neural network, ELM, and DBN, and root mean square error, R square coefficient of determination, and training time are selected as evaluation indexes of the models. The experimental results show that the improved GBDBN-ELM model can not only improve the accuracy of strip steel quality prediction but also shorten the time of model training. The model proposed in this paper has achieved good results in prediction accuracy and performance.

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Section: Related Workmentioning

confidence: 99%