Automatic system for intelligent support of continuous cast billet production control processes

Логунова, О. С.; Matsko, Ivan; Posohov, I. A.; Luk’ynov, S. I.

doi:10.1007/s00170-014-6056-4

Cited by 25 publications

(7 citation statements)

References 5 publications

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“…The amount of information coming from different sources can easily grow overwhelming, however, which will increase the need for intelligent pre-processing in the systems. Decision support systems, for example, become intelligent when combined with AI tools such as fuzzy logic, casebased reasoning, evolutionary computing, artificial neural networks, and intelligent agents (Phillips-Wren, 2013;Logunova et al, 2014).…”

Section: Motivationmentioning

confidence: 99%

An online quality monitoring tool for information acquisition and sharing in manufacturing: requirements and solutions for the steel industry

Tamminen

Ferreira

Tiensuu

et al. 2019

IJISE

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The purpose of this study was to develop an innovative online supervisor system to assist the operators of an industrial manufacturing process in discovering new solutions for improving both the products and the manufacturing process itself. In this paper, we discuss the requirements and practical aspects of building such a system and demonstrate its use and functioning with different types of statistical modelling methods applied for quality monitoring in industrial applications. The two case studies presenting the development work were selected from the steel industry. One case study predicting the profile of a stainless steel strip tested the usability of the tool offline, while the other study predicting the risk of roughness of a steel strip had an online test period. User experiences from a test use period were collected with a system usability scale questionnaire.

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Section: Motivationmentioning

confidence: 99%

An online quality monitoring tool for information acquisition and sharing in manufacturing: requirements and solutions for the steel industry

Tamminen

Ferreira

Tiensuu

et al. 2019

IJISE

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“…В указанных агрегатах затруднены прямые измерения по методикам, приведенным в [7,8]. Однако математические модели с переменными свойствами материала необходимы при создании новых конструкций металлургических агрегатов [9][10][11], разработке новых функций автоматизированных систем управления технологическими процессами [12][13][14].…”

Section: введение unclassified

Mathematical Models for Investigation of the Heat Condition of Objects and Heat Processes Control

Логунова

Чусавитина

Andreev

et al. 2019

ESC

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The purpose of the study is the systematization of problems related to the study of the thermal state of objects and melts in industrial production and ways to solve them. In particular, the problems characteristic for a metallurgical enterprise are: mathematical modeling of electromagnetic fields in systems for induction heating of the hardware industry; development of information support for an energy-saving mode of heating billets before rolling in a continuous-type heating furnace; application of the basis of the classical equations of heat and mass transfer and multivariate analysis to simulate the processes in the ladle furnace unit; development of pseudo-dynamic and quasidynamic models describing the thermal state of an infinitely extended object. All these tasks have a thermal nature and despite the difference in the objects of study, they use mathematical models to study the thermal state of objects and control thermal processes. The implementation of mathematical models is made using modern computer software. All studies were conducted by scientific schools of the Nosov Magnitogorsk State Technical University and were extensively tested and implemented in operating production facilities.

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“…AI methods may improve both the competitiveness and the efficiency of a company. For example, Logunova et al [6] reported that they have obtained significant savings from quality improvements in a continuous cast billet production facility by developing an automatic system for the intelligent support of billet production control processes.…”

Section: Introductionmentioning

confidence: 99%

Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing

et al. 2021

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This article demonstrates the use of data mining methods for evidence-based smart decision support in quality control. The data were collected in a measurement campaign which provided a new and potential quality measurement approach for manufacturing process planning and control. In this study, the machine learning prediction models and Explainable AI methods (XAI) serve as a base for the decision support system for smart manufacturing. The discovered information about the root causes behind the predicted failure can be used to improve the quality, and it also enables the definition of suitable security boundaries for better settings of the production parameters. The user’s need defines the given type of information. The developed method is applied to the monitoring of the surface roughness of the stainless steel strip, but the framework is not application dependent. The modeling analysis reveals that the parameters of the annealing and pickling line (RAP) have the best potential for real-time roughness improvement.

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Automatic system for intelligent support of continuous cast billet production control processes

Cited by 25 publications

References 5 publications

An online quality monitoring tool for information acquisition and sharing in manufacturing: requirements and solutions for the steel industry

An online quality monitoring tool for information acquisition and sharing in manufacturing: requirements and solutions for the steel industry

Mathematical Models for Investigation of the Heat Condition of Objects and Heat Processes Control

Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing

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