Prediction on the Distributions of the Strength and Toughness of Thick Steel Plates Based on Bayesian Neural Network

Kim, Cheolhyeong; Shin, Jin Young; Roh, Jong-Won; Hwang, Hyung Ju

doi:10.1002/srin.202100566

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…Kim et al predicted the strength and toughness of thick steel plates using a Bayesian neural network (BNN) model and evaluated uncertainty. They successfully employed the technique using a steelmaking production data set of Pohang Iron and Steel Company (POSCO) firms [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of steelmaking process variables using K-medoids and a time-aware LSTM network

Zheng,

Bao,

Zhao

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Prediction of steelmaking process variables using K-medoids and a time-aware LSTM network

Zheng,

Bao,

Zhao

et al. 2024

Heliyon

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“…[18,22,26] At present, popular artificial intelligence (AI) methods including traditional statistical methods, traditional symbolic AI, and computational intelligence (CI) are applied to solving complicated real-world issues for which conventional approaches are insufficient or impracticable. [27][28][29][30][31] Artificial neural networks (ANNs), fuzzy logic (FL), and evolutionary algorithms (EAs) are the most frequently employed CI techniques to solve different engineering problems. [27,31] EAs is a class of global optimization methods with wide applicability, [27] which provides a new way to optimize the fuzzy rules proposed by ordinary operators.…”

Section: Introductionmentioning

confidence: 99%

Control of the Molten Steel Level in the Top Side‐Pouring Twin‐Roll Casting Process Based on Fuzzy Rules Optimized by Particle Swarm Optimization Algorithm

Zhou

Mao

Xuan

et al. 2023

steel research int.

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Twin‐roll strip casting is a near‐net‐shape casting technology that can produce thin steel strips directly from molten steel. Stably controlling the molten steel level is regarded as an important issue to ensure strip quality and casting process stability. As the control of the molten steel level is a time‐varying, nonlinear, and multidisturbance complex system, it is difficult to establish an accurate process model for designing a model‐based controller. Top side‐pouring twin‐roll casting is a new kind of twin‐roll strip casting technology. This study introduces the control system of the top side‐pouring twin‐roll casting process. A fuzzy logic controller (FLC) with its fuzzy rules optimized by particle swarm optimization (PSO) is developed to regulate the molten steel level. Simulation results show that the performance of the FLC can be improved while its fuzzy rules are optimized by PSO. The objective function of PSO has a great influence on the optimization of the fuzzy rules. The top side‐pouring twin‐roll casting experiments are carried out using the FLC with its fuzzy rules optimized by PSO; the results show that strip quality and casting process stability are guaranteed.

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“…While many of the methods and algorithms to deal with large quantities of data have been developed already years ago, only recently there has been an increase of big data and machinelearning (ML) approaches to industrial processes. [4,5] Specific solutions ranging from the prediction of process variables or behavior in steelmaking [6][7][8] or quality monitoring and prediction [9,10] have been developed; however, the application of ML and big data tools is in general not widespread in the steel industry. In the following, we will discuss the possibilities of data analyses with big data methods and ML, considering the different data available from the RH plant.…”

Section: Introductionmentioning

confidence: 99%

Big Data Handling in Process Surveillance and Quality Control of Secondary Metallurgical Processes

Gruber¹,

Bückner²,

Schatzl³

et al. 2022

steel research int.

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The Ruhrstahl–Heraeus (RH) degassing plant is essential for producing ultraclean steel, but its vacuum‐based nature is hiding the process from scrutiny. Despite recording the many inputs and outputs of process control and measurements surrounding the plant, the utilization of produced data is comparatively low. Data handling is a challenge, especially considering correlations of process control data with indicators such as actual steel homogeneity and cleanliness after casting. Here in this study, approaches to improving sensor data quality and maximizing the utilization of the abundance of data obtained during the RH process are presented. Time series data from process control measurements like off‐gas composition or chamber pressure, even though highly connected to specific domain knowledge, can be tackled with machine‐learning methods, such as recognition of characteristic curve sections or categorization of curves with clustering algorithms. Visual surveillance offers the possibility of image analyses of the surface flow observation inside the vacuum chamber with different approaches such as image classification or feature detection algorithms. Herein, a digitalization and data analysis strategy developed for an improved understanding of the RH process, considering linear as well as integral data from the plant, are presented.

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Prediction on the Distributions of the Strength and Toughness of Thick Steel Plates Based on Bayesian Neural Network

Abstract: The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/srin.202100566.

Cited by 4 publications

References 16 publications

Prediction of steelmaking process variables using K-medoids and a time-aware LSTM network

Prediction of steelmaking process variables using K-medoids and a time-aware LSTM network

Control of the Molten Steel Level in the Top Side‐Pouring Twin‐Roll Casting Process Based on Fuzzy Rules Optimized by Particle Swarm Optimization Algorithm

Big Data Handling in Process Surveillance and Quality Control of Secondary Metallurgical Processes

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