Online prediction of mechanical properties of hot rolled steel plate using machine learning

Xie, Qian; Suvarna, Manu; Li, Jiali; Zhu, Xinzhe; Cai, Jiajia; Wang, Xiaonan

doi:10.1016/j.matdes.2020.109201

Cited by 112 publications

(52 citation statements)

References 33 publications

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“…Generally, ANN model contains three layers and ANN model with more than hidden layers is known as DNN model [ 18 ]. DNN is widely used to investigate the correlation between variables for critical problems [ 41 ]. Automatic creation and exploration of information from previous learning is an interesting feature of DNN [ 42 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

Prediction of Methylene Blue Removal by Nano TiO2 Using Deep Neural Network

2021

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This paper deals with the prediction of methylene blue (MB) dye removal under the influence of titanium dioxide nanoparticles (TiO2 NPs) through deep neural network (DNN). In the first step, TiO2 NPs were prepared and their morphological properties were analysed by scanning electron microscopy. Later, the influence of as synthesized TiO2 NPs was tested against MB dye removal and in the final step, DNN was used for the prediction. DNN is an efficient machine learning tools and widely used model for the prediction of highly complex problems. However, it has never been used for the prediction of MB dye removal. Therefore, this paper investigates the prediction accuracy of MB dye removal under the influence of TiO2 NPs using DNN. Furthermore, the proposed DNN model was used to map out the complex input-output conditions for the prediction of optimal results. The amount of chemicals, i.e., amount of TiO2 NPs, amount of ehylene glycol and reaction time were chosen as input variables and MB dye removal percentage was evaluated as a response. DNN model provides significantly high performance accuracy for the prediction of MB dye removal and can be used as a powerful tool for the prediction of other functional properties of nanocomposites.

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Section: Materials and Methodsmentioning

confidence: 99%

Prediction of Methylene Blue Removal by Nano TiO2 Using Deep Neural Network

2021

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“…The reconstruction error was computed using Euclidean distances, which was later minimized using VAE based training encoding method, as shown in step 4 of Figure 13. The study [65] has suggested a deep learning-based forecasting model to identify mechanical properties of industrial steel plates such as elongation (EL), yield strength (YS), impact energy (Akv), according to the process parameters along with raw steel combination. The model was later applied on a real steel manufacturing plant online.…”

Section: First Shell Particle-clustermentioning

confidence: 99%

A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery

et al. 2022

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Research has become increasingly more interdisciplinary over the past few years. Artificial intelligence and its sub-fields have proven valuable for interdisciplinary research applications, especially physical sciences. Recently, machine learning-based mechanisms have been adapted for material science applications, meeting traditional experiments’ challenges in a time and cost-efficient manner. The scientific community focuses on harnessing varying mechanisms to process big data sets extracted from material databases to derive hidden knowledge that can successfully be employed in technical frameworks of material screening, selection, and recommendation. However, a plethora of underlying aspects of the existing material discovery methods needs to be critically assessed to have a precise and collective analysis that can serve as a baseline for various forthcoming material discovery problems. This study presents a comprehensive survey of state-of-the-art benchmark data sets, detailed pre-processing and analysis, appropriate learning model mechanisms, and simulation techniques for material discovery. We believe that such an in-depth analysis of the mentioned aspects provides promising directions to the young interdisciplinary researchers from computing and material science fields. This study will help devise useful modeling in the materials discovery to positively contribute to the material industry, reducing the manual effort involved in the traditional material discovery. Moreover, we also present a detailed analysis of experimental and computation-based artificial intelligence mechanisms suggested by the existing literature.

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“…Degtyarev et al [17] explored different ML boosting algorithms to predict the elastic shear buckling loads and the ultimate shear strength of cold-formed steel, e.g., gradient boosting regression (GBR) and extreme gradient boosting (XGB). Xie et al [18] developed a deep neural network (DNN) model to predict the mechanical properties of four different types of hot-rolled steel plates, and this model was adopted for an actual production line. However, in these works, [13][14][15][16][17][18][19][20][21][22][23][24][25][26] most of the computational analysis systems achieved accurate property prediction based on a simple database with a single or a few specific steel grades, and few studies paid attention to establishing the universal prediction framework regarding a complex industrial database containing various kinds of commercial steels.…”

Section: Introductionmentioning

confidence: 99%

“…Xie et al [18] developed a deep neural network (DNN) model to predict the mechanical properties of four different types of hot-rolled steel plates, and this model was adopted for an actual production line. However, in these works, [13][14][15][16][17][18][19][20][21][22][23][24][25][26] most of the computational analysis systems achieved accurate property prediction based on a simple database with a single or a few specific steel grades, and few studies paid attention to establishing the universal prediction framework regarding a complex industrial database containing various kinds of commercial steels. Thus, the extensibility of prediction models is limited and hinders the wide applications of ML-based DOI: 10.1002/srin.202100820 Various computational analysis systems based on machine learning (ML) methods have been established for the analysis of steel industrial data.…”

Section: Introductionmentioning

confidence: 99%