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

Metals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 685 ◽  
Author(s):  
Xu Li ◽  
Feng Luan ◽  
Yan Wu
Keyword(s):  
Prediction Accuracy ◽  
Computational Cost ◽  
Regression Tree ◽  
Prediction Performance ◽  
Learning Models ◽  
Hot Strip Rolling ◽  
Strip Rolling ◽  
Bending Force ◽  
Hot Strip ◽  
Machine Learning Models

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.

scholarly journals Application of support vector machine for the prediction of strip crown in hot strip rolling

2020 ◽  
Author(s):  
Ya-feng Ji ◽  
Le-Bao Song ◽  
Hao Yuan ◽  
Wen Peng ◽  
Hua-Ying Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Prediction Performance ◽  
Support Vector ◽  
Hot Strip Rolling ◽  
Strip Rolling ◽  
Hot Strip ◽  
Strip Crown

Abstract In order to enhance the prediction accuracy of the strip crown and improve the quality of final product in the hot strip rolling, an optimized model based upon support vector machine (SVM) is proposed firstly. Meanwhile, for purposes of enriching data information and ensuring data quality, the actual data from a hot-rolled plant are collected to establish prediction model, as well as the prediction performance of models was evaluated by using multiple indicators. Besides, the traditional SVM model and the combined prediction models with the particle swarm optimization (PSO) and the cuckoo search (CS) optimization algorithm are also proposed. Furthermore, the prediction performance comparisons of the three different methods are discussed and validated. The results show that the CS-SVM has the highest prediction accuracy compared to the other two methods, and the root mean squared error (RMSE) of the proposed CS-SVM is 2.05µm, and 98.11% of prediction data have an absolute error below 4.5μm. In addition, the results also demonstrated that the CS-SVM not only with faster convergence speed and higher prediction accuracy but can be well applied to the actual hot strip rolling production.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yan Wu ◽  
Hongchao Ni ◽  
Xu Li ◽  
Feng Luan ◽  
Yaodong He
Keyword(s):  
Extreme Learning Machine ◽  
Hot Rolling ◽  
Absolute Error ◽  
Rolling Process ◽  
Hot Strip Rolling ◽  
Strip Rolling ◽  
Bending Force ◽  
Hot Strip ◽  
Learning Machine ◽  
Hidden Layer

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.

scholarly journals Performance Comparison of Machine Learning Models for Annual Precipitation Prediction Using Different Decomposition Methods

2021 ◽  
Vol 13 (5) ◽  
pp. 1018
Author(s):  
Chao Song ◽  
Xiaohong Chen
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Prediction Accuracy ◽  
Decomposition Methods ◽  
Prediction Performance ◽  
Learning Models ◽  
Precipitation Prediction ◽  
Mode Decomposition ◽  
Machine Learning Models

It has become increasingly difficult in recent years to predict precipitation scientifically and accurately due to the dual effects of human activities and climatic conditions. This paper focuses on four aspects to improve precipitation prediction accuracy. Five decomposition methods (time-varying filter-based empirical mode decomposition (TVF-EMD), robust empirical mode decomposition (REMD), complementary ensemble empirical mode decomposition (CEEMD), wavelet transform (WT), and extreme-point symmetric mode decomposition (ESMD) combined with the Elman neural network (ENN)) are used to construct five prediction models, i.e., TVF-EMD-ENN, REMD-ENN, CEEMD-ENN, WT-ENN, and ESMD-ENN. The variance contribution rate (VCR) and Pearson correlation coefficient (PCC) are utilized to compare the performances of the five decomposition methods. The wavelet transform coherence (WTC) is used to determine the reason for the poor prediction performance of machine learning algorithms in individual years and the relationship with climate indicators. A secondary decomposition of the TVF-EMD is used to improve the prediction accuracy of the models. The proposed methods are used to predict the annual precipitation in Guangzhou. The subcomponents obtained from the TVF-EMD are the most stable among the four decomposition methods, and the North Atlantic Oscillation (NAO) index, the Nino 3.4 index, and sunspots have a smaller influence on the first subcomponent (Sc-1) than the other subcomponents. The TVF-EMD-ENN model has the best prediction performance and outperforms traditional machine learning models. The secondary decomposition of the Sc-1 of the TVF-EMD model significantly improves the prediction accuracy.

scholarly journals Classification and Prediction on the Effects of Nutritional Intake on Overweight/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4–7th Korea National Health and Nutrition Examination Survey

2021 ◽  
Vol 18 (11) ◽  
pp. 5597
Author(s):  
Hyerim Kim ◽  
Dong Hoon Lim ◽  
Yoona Kim
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Decision Tree ◽  
Prediction Accuracy ◽  
Nutritional Intake ◽  
Nutrition Examination Survey ◽  
Learning Models ◽  
Health And Nutrition ◽  
Machine Learning Models

Few studies have been conducted to classify and predict the influence of nutritional intake on overweight/obesity, dyslipidemia, hypertension and type 2 diabetes mellitus (T2DM) based on deep learning such as deep neural network (DNN). The present study aims to classify and predict associations between nutritional intake and risk of overweight/obesity, dyslipidemia, hypertension and T2DM by developing a DNN model, and to compare a DNN model with the most popular machine learning models such as logistic regression and decision tree. Subjects aged from 40 to 69 years in the 4–7th (from 2007 through 2018) Korea National Health and Nutrition Examination Survey (KNHANES) were included. Diagnostic criteria of dyslipidemia (n = 10,731), hypertension (n = 10,991), T2DM (n = 3889) and overweight/obesity (n = 10,980) were set as dependent variables. Nutritional intakes were set as independent variables. A DNN model comprising one input layer with 7 nodes, three hidden layers with 30 nodes, 12 nodes, 8 nodes in each layer and one output layer with one node were implemented in Python programming language using Keras with tensorflow backend. In DNN, binary cross-entropy loss function for binary classification was used with Adam optimizer. For avoiding overfitting, dropout was applied to each hidden layer. Structural equation modelling (SEM) was also performed to simultaneously estimate multivariate causal association between nutritional intake and overweight/obesity, dyslipidemia, hypertension and T2DM. The DNN model showed the higher prediction accuracy with 0.58654 for dyslipidemia, 0.79958 for hypertension, 0.80896 for T2DM and 0.62496 for overweight/obesity compared with two other machine leaning models with five-folds cross-validation. Prediction accuracy for dyslipidemia, hypertension, T2DM and overweight/obesity were 0.58448, 0.79929, 0.80818 and 0.62486, respectively, when analyzed by a logistic regression, also were 0.52148, 0.66773, 0.71587 and 0.54026, respectively, when analyzed by a decision tree. This study observed a DNN model with three hidden layers with 30 nodes, 12 nodes, 8 nodes in each layer had better prediction accuracy than two conventional machine learning models of a logistic regression and decision tree.

X-ray thickness gauge for hot strip rolling mills

1948 ◽  
Vol 67 (5) ◽  
pp. 441-444 ◽  
Author(s):  
C. W. Clapp ◽  
R. V. Pohl
Keyword(s):  
Thickness Gauge ◽  
Hot Strip Rolling ◽  
Strip Rolling ◽  
Rolling Mills ◽  
X Ray ◽  
Hot Strip
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