Optimization of Metal Rolling Control Using Soft Computing Approaches: A Review

Hu, Ziyu; Wei, Zhihui; Sun, Hao; Yang, Jingming; Wei, Lixin

doi:10.1007/s11831-019-09380-6

Cited by 54 publications

(10 citation statements)

References 59 publications

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“…In recent years, besides ANN modeling, some new Metals 2020, 10, 685 3 of 16 machine learning methods have emerged, such as Support Vector Machine (SVM), Classification and Regression Tree (CART), Bagging Regression Tree (BRT), Least Absolute Shrinkage and Selection Operator (LASSO), and Gaussian Process Regression (GPR). For the prediction research in the rolling field, some scholars have realized that better prediction results and prospects can be obtained by adopting new machine learning methods [21,23,24], and so far, there are few literature reports on bending force prediction.…”

Section: Strip Stripmentioning

confidence: 99%

A Comparative Assessment of Six Machine Learning Models for Prediction of Bending Force in Hot Strip Rolling Process

Feng

2020

Metals

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In the hot strip rolling (HSR) process, accurate prediction of bending force can improve the control accuracy of the strip crown and flatness, and further improve the strip shape quality. In this paper, six machine learning models, including Artificial Neural Network (ANN), Support Vector Machine (SVR), Classification and Regression Tree (CART), Bagging Regression Tree (BRT), Least Absolute Shrinkage and Selection operator (LASSO), and Gaussian Process Regression (GPR), were applied to predict the bending force in the HSR process. A comparative experiment was carried out based on a real-life dataset, and the prediction performance of the six models was analyzed from prediction accuracy, stability, and computational cost. The prediction performance of the six models was assessed using three evaluation metrics of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results show that the GPR model is considered as the optimal model for bending force prediction with the best prediction accuracy, better stability, and acceptable computational cost. The prediction accuracy and stability of CART and ANN are slightly lower than that of GPR. Although BRT also shows a good combination of prediction accuracy and computational cost, the stability of BRT is the worst in the six models. SVM not only has poor prediction accuracy, but also has the highest computational cost while LASSO showed the worst prediction accuracy.

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Section: Strip Stripmentioning

confidence: 99%

A Comparative Assessment of Six Machine Learning Models for Prediction of Bending Force in Hot Strip Rolling Process

Feng

2020

Metals

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“…The reduction of dimensionality of a massive document may include millions of words through clustering by having a meaningful and interpretable number of similar groups or clusters that may occur naturally in the data. However, a significant improvement of text mining models has been observed in the recent past with the intense use of modern supervised and unsupervised machine learning algorithms, especially for the high dimensional setting of data [19]. For instance, NLP frameworks for online opinion mining based on GA and ontology [20], a prediction model for creativity education using clustering methods based on discussion and records [21], and forums hotspot detection and forecast through a newly developed mixed unsupervised machine learning-based text mining and sentiment technique [22] have been observed in the literature.…”

Section: Introductionmentioning

confidence: 99%

Text Mining and Sentiment Analysis of Newspaper Headlines

2021

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Text analytics are well-known in the modern era for extracting information and patterns from text. However, no study has attempted to illustrate the pattern and priorities of newspaper headlines in Bangladesh using a combination of text analytics techniques. The purpose of this paper is to examine the pattern of words that appeared on the front page of a well-known daily English newspaper in Bangladesh, The Daily Star, in 2018 and 2019. The elucidation of that era’s possible social and political context was also attempted using word patterns. The study employs three widely used and contemporary text mining techniques: word clouds, sentiment analysis, and cluster analysis. The word cloud reveals that election, kill, cricket, and Rohingya-related terms appeared more than 60 times in 2018, whereas BNP, poll, kill, AL, and Khaleda appeared more than 80 times in 2019. These indicated the country’s passion for cricket, political turmoil, and Rohingya-related issues. Furthermore, sentiment analysis reveals that words of fear and negative emotions appeared more than 600 times, whereas anger, anticipation, sadness, trust, and positive-type emotions came up more than 400 times in both years. Finally, the clustering method demonstrates that election, politics, deaths, digital security act, Rohingya, and cricket-related words exhibit similarity and belong to a similar group in 2019, whereas rape, deaths, road, and fire-related words clustered in 2018 alongside a similar-appearing group. In general, this analysis demonstrates how vividly the text mining approach depicts Bangladesh’s social, political, and law-and-order situation, particularly during election season and the country’s cricket craze, and also validates the significance of the text mining approach to understanding the overall view of a country during a particular time in an efficient manner.

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“…erefore, the high-precision prediction method of rolling process based on industrial big data has attracted attention [5].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Bending Force in the Hot Strip Rolling Process Using Multilayer Extreme Learning Machine

et al. 2021

Mathematical Problems in Engineering

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In the hot strip rolling process, accurate prediction of bending force can improve the control accuracy of the strip flatness and further improve the quality of the strip. In this paper, based on the production data of 1300 pieces of strip collected from a hot rolling factory, a series of bending force prediction models based on an extreme learning machine (ELM) are proposed. To acquire the optimal model, the parameter settings of the models were investigated, including hidden layer nodes, activation function, population size, crossover probability, and hidden layer structure. Four models are established, one hidden layer ELM model, an optimized ELM model (GAELM) by genetic algorithm (GA), an optimized ELM model (SGELM) by hybrid simulated annealing (SA) and GA, and two-hidden layer optimized ELM model (SGITELM) optimized by SA and GA. The prediction performance is evaluated from the mean absolute error (MAE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). The results show that the SGITELM has the highest prediction accuracy in the four models. The RMSE of the proposed SGITELM is 11.2678 kN, and 98.72% of the prediction data have an absolute error of less than 25 kN. This indicates that the proposed SGITELM with strong learning ability and generalization performance can be well applied to hot rolling production.

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Optimization of Metal Rolling Control Using Soft Computing Approaches: A Review

Cited by 54 publications

References 59 publications

A Comparative Assessment of Six Machine Learning Models for Prediction of Bending Force in Hot Strip Rolling Process

A Comparative Assessment of Six Machine Learning Models for Prediction of Bending Force in Hot Strip Rolling Process

Text Mining and Sentiment Analysis of Newspaper Headlines

Prediction of Bending Force in the Hot Strip Rolling Process Using Multilayer Extreme Learning Machine

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