Modelling hot rolling manufacturing process using soft computing techniques

Faris, Hossam; Sheta, Alaa; Öznergiz, Ertan

doi:10.1080/0951192x.2013.766937

Cited by 23 publications

(10 citation statements)

References 19 publications

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“…In some metaheuristics, fitness values affect the search direction of the algorithm. Evolutionary Algorithms were introduced to solve many problems in manufacturing and industry [31][32][33]33]. Evolutionary algorithms are mainly inspired by the Darwinian theory of evolution and natural selection.…”

Section: Nature-inspired Metaheuristicsmentioning

confidence: 99%

Estimating ARMA Model Parameters of an Industrial Process Using Meta-Heuristic Search Algorithms

Sheta

Faris²,

Aljarah³

2018

IJET

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This paper addresses the parameter estimation problem for a manufacturing process based on the Auto-Regressive Moving Average (ARMA) model. The accurate estimation of the ARMA model’s parameter helps to reduce the production costs, provide better product quality, increase productivity and profit. Meta-heuristic algorithms are among these approximate techniques which have been successfully used to search for an optimal solution in complex search space. Meta-heuristic algorithms can converge to an optimal global solution despite traditional parameter estimation techniques which stuck by local optimal. A comparison between Meta-heuristic algorithms: Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and the Accelerated PSO, Cuckoo Search, Krill Herd and Firefly algorithm is provided to handle the parameter estimation problem for a Winding process in the industry. The developed ARMA-meta-heuristics models for a winding machine are evaluated based on different evaluation metrics. The results reveal that meta-heuristics can provide an outstanding modeling performance.

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Section: Nature-inspired Metaheuristicsmentioning

confidence: 99%

Estimating ARMA Model Parameters of an Industrial Process Using Meta-Heuristic Search Algorithms

Sheta

Faris²,

Aljarah³

2018

IJET

Self Cite

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“…Within a specific time, the chromosome can be transformed to implement a better solution. This document proposes a partial diversity maintaining method to avoid the algorithm falling into local optimum solutions [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Using the Taguchi-Genetic Algorithm to Improve Lithographic Photoresist Operating Conditions of Touch Panels to Upgrade After-Develop Inspection

Wang

2018

Applied Sciences

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In order to use touch control products more conveniently, a general objective is to develop lighter and smaller touch panels. A touch panel using the one glass solution (OGS) is an important development. The black matrix (BM) in an OGS touch panel is used as a black frame. The photoresist is divided into a positive photoresist and a negative photoresist. The BM photoresist is negative. After coating, exposure, and development in the BM process, after-develop inspection is implemented to check if the appearance is abnormal. It is quite difficult to rework the negative photoresist process. There is still room for improving the BM photoresist process capability Cpk. Thus, in order to reduce the customer complaint rate and enhance stability, the photolithography process is improved to enhance Cpk. Among the BM black negative photoresist forming process conditions of OGS products, the pre-baking time is the most important process control factor. The method set up herein improves the original Cpk = 0.90. This study employs the fast messy genetic algorithm (fmGA) to select the optimum orthogonal array of the Taguchi method, so as to implement the decision process of optimum parameter design. The Cpk of the optimum parameter is 2.12.

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“…Neural networks have been used in many cases, including plate width set-up value estimation in a hot plate mill (Lee et al 2000), temperature prediction for steel slabs (Laurinen and Röning 2005), steel hardness prediction (Das and Datta 2007) and the prediction of work roll thermal expansion (Alaei et al 2016). Furthermore, Faris et al (2013) used genetic programming to predict rolling force, torque, and slab temperature. Serdio et al (2014) proposed residual-based fault detection using soft computing techniques for condition monitoring in rolling mills.…”

Section: Introductionmentioning

confidence: 99%

Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties

et al. 2017

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The continuous development of steel products generates new challenges for the maintenance of manufacturing machines in steel mills. Substantial mechanical stress is inflicted on the machines during the processing of modern high-strength steels. This increases the risks of damage and flaws in the processed material may appear if the capability of a machine is exceeded. Therefore, new approaches are needed to prevent the machine condition from deteriorating. This study introduces an approach to the prediction of mechanical stress inflicted on a roller leveler during the processing of cold steel strips. The relative stress level is indicated by features extracted from an acceleration signal. These features are based on the calculation of generalized norms. Steel strip properties are used as explanatory variables in regression models to predict values for the extracted vibration features. The models tested in this study include multiple linear regression, partial least squares regression and generalized regression neural network. The models were tested using an extensive data set from a roller leveler that is in continuous operation in a steel mill. The prediction accuracy of the best models identified indicates that the relative stress level inflicted by each steel strip could be predicted based on its properties.

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Modelling hot rolling manufacturing process using soft computing techniques

Cited by 23 publications

References 19 publications

Estimating ARMA Model Parameters of an Industrial Process Using Meta-Heuristic Search Algorithms

Estimating ARMA Model Parameters of an Industrial Process Using Meta-Heuristic Search Algorithms

Using the Taguchi-Genetic Algorithm to Improve Lithographic Photoresist Operating Conditions of Touch Panels to Upgrade After-Develop Inspection

Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties

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