Online mechanical property prediction system for hot rolled IF steel

Mohanty, Itishree; Sarkar, Santanu; Jha, Bhawesh Chandra; Das, Souvik; Kumar, Rajeev

doi:10.1179/1743281214y.0000000178

Cited by 27 publications

(7 citation statements)

References 16 publications

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“…Based on these data, the machine learning algorithm can be used for data mining and relating chemical compositions, process parameters and mechanical properties, so as to realize the mechanical property prediction of steels [10][11][12]. Some researchers have applied the commonly used algorithms, such as artificial neural network [13][14][15][16][17][18], support vector machine [19], nerofuzzy inference system [20], semi-parametric single index model [21] etc., to investigate the prediction model of mechanical properties and have made some achievements. However, the artificial neural network is prone to over fitting with the increase of the hidden layer neuron number [22].…”

Section: Introductionmentioning

confidence: 99%

Elevating Prediction Performance for Mechanical Properties of Hot-Rolled Strips by Using Semi-Supervised Regression and Deep Learning

Yang

Cao

et al. 2020

IEEE Access

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In the present work, to solve the problem of the lacking enough labeled training data for deep learning, a safe semi-supervised regression supporting Bayesian optimization deep neural network (SAFER-BODNN) model was proposed to establish mechanical property prediction model of hot-rolled strips. The Pearson correlation coefficient was applied to reduce the data dimension. The safe semi-supervised regression was implemented to add the pseudo labels to the unlabeled data for training dataset expansion. The deep neural network was trained with Bayesian optimization to determine the optimal hyper-parameters of the network. The results show that the SAFER-BODNN model achieves good accuracy for mechanical property prediction of hot-rolled strips with R of 0.9610 for yield strength, 0.9682 for tensile strength, and 0.8619 for elongation, respectively. Compared with the deep neural network trained on the labeled dataset, the SAFER-BODNN model obtains stable smaller predicted errors. Among all the variables, C content and Mn content have large influence on the yield strength and tensile strength, coiling temperature has the largest influence on the elongation. The investigation makes full use of unlabeled data to elevate the accuracy of the deep neural network, and also provides a way for deep learning modeling when the data are insufficient.

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Section: Introductionmentioning

confidence: 99%

Elevating Prediction Performance for Mechanical Properties of Hot-Rolled Strips by Using Semi-Supervised Regression and Deep Learning

Yang

Cao

et al. 2020

IEEE Access

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“…In order to obtain optimal hyperparameters, different number of depths, width and convolutional kernels are attempted on network structure. Four indicators are adopted as the evaluation metrics to assess the prediction capability comprehensively, such as mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R 2 ) [27].…”

Section: B Results Of Proposed Cnn Modelmentioning

confidence: 99%

“…First, the reheating process provides a uniform temperature to the slab to provide a uniform initial austenite grain size Then, the roughing and finishing processes refine austenite by dynamic and static recrystallization. Furthermore, the steel sheet is continuously cooled by the laminar cooling system for refining transformed ferrite and pearlite grain [27]. The size and volume fraction of these grains determine the mechanical properties of the steel.…”

Section: B Hot Rolled Processing Of Alloy Steelmentioning

confidence: 99%

Mechanical Properties Prediction for Hot Rolled Alloy Steel Using Convolutional Neural Network

Liu²,

Zhang³

2019

IEEE Access

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A convolutional neural network (CNN)-based method for predicting the mechanical properties of hot rolled steel using chemical composition and process parameters is proposed. The novel contribution of this research is to introduce the prediction method of CNN into the steel properties prediction field by converting the production data into two-dimensional data images. Compared with the traditional artificial neural network method, the CNN adopts the idea of local connection and weight sharing, which reduces the complexity of the network model, and uses convolution and pooling operations to extract local features for better prediction precision. The experiments in this paper show that the proposed CNN model with the optimal structure provides higher prediction accuracy and higher robustness when compared with other prediction model reported in the literature. Finally, the metallurgical phenomena in the steel rolling processes are verified by sensitivity analysis using the proposed CNN model. The results are consistent with the metallurgical properties of the steel materials used in the experiments. Therefore, the proposed CNN model has a guiding significance in predicting the mechanical properties of hot rolled steel products in practical applications.INDEX TERMS Convolutional neural network, mechanical property prediction, deep learning, hot rolled alloy steel.

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“…If the product's surface finish/size is on a tolerance level, the product is accepted else rejected and again sent to the furnace for melting. Mohanty et al [28] presented that the Networkbased online system can predict the mechanical properties of interstitial free (IF) steel strip by capturing various metallurgical phenomena during rolling.…”

Section: C: Quality Managementmentioning

confidence: 99%

IE Tools for Boosting Competitiveness in Iron and Steel Industry: A Review

Akarte

Khanzode

Iqbal

et al. 2021

Trans Indian Inst Met

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Integrated steelmaking operations, from ironmaking to final steel categories/forms, are a complex industrial system. Decision-makers, in addition to the domain knowledge, need to understand many facets related to operations management, process execution, involved risk factors and material handling aspects, etc. The solution approach must address trade-offs, uncertainties, multiple stages, multiple objectives, many decision variables, and constraints. Literature has presented several Industrial Engineering approaches like multi-objective optimisation, heuristics, evolutionary algorithm, Simulations, lean sixsigma, etc., to address the iron and steel industry's complex problems like integrated production planning. The manuscript reviews recent application of these IE tools/solution approaches in iron and steel industries to enhanced productivity/competitiveness. A Systematic Literature Review (SLR) methodology has been adopted. The review will help the practitioners and academicians to know the different problem areas about iron and steel industries discussed in the literature, and the solution approaches used to solve the problem. Keywords Industrial Engineering Á Optimization Á Iron and steel industry Á Productivity improvement Á Solution approaches 1 Introduction Industrial Engineering (IE), also referred to as Industrial and Systems Engineering or Industrial Engineering and Management or Management Science is a multidisciplinary branch that covers several productivity improvement techniques. The branch of knowledge started developing in the early twentieth century when F. W. Taylor invented principles of scientific management, and Henry Ford implemented assembly lines to improve productivity at reduced costs in manufacturing processes. Over a period, developments like Industry 4.0, predictive analytics, Operations Research, Toyota Production Systems, TQM & Six Sigma, Ergonomics, and Human Factors Engineering, created a vast body of knowledge creation/dissemination. Institute of Industrial Engineers in January 2019 created a document representing a repository of essential knowledge areas for Industrial Engineering [1].

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Online mechanical property prediction system for hot rolled IF steel

Cited by 27 publications

References 16 publications

Elevating Prediction Performance for Mechanical Properties of Hot-Rolled Strips by Using Semi-Supervised Regression and Deep Learning

Elevating Prediction Performance for Mechanical Properties of Hot-Rolled Strips by Using Semi-Supervised Regression and Deep Learning

Mechanical Properties Prediction for Hot Rolled Alloy Steel Using Convolutional Neural Network

IE Tools for Boosting Competitiveness in Iron and Steel Industry: A Review

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