L. A. Alvarado
ABSTRACT
Improvement of processes in metallurgical industry is a constant of competitive enterprises, however, changes made in a process are risky and involves high cost and time, considering this, a model can be made even using inputs usually not presented in real process and its analysis could be useful for the improvement of the process. In this work, a mathematical model is built using only experimental data of a four high tandem cold rolling mill, a set of input variables involving characteristics of the process. The performance of the model is determined by residual analysis considering new data. Results are a non black box model with a good performance; by this way, the model is a good representation of the process under study.
- M. Bagheripoor and H. Bisadi. Application of artificial neural networks for the prediction of roll force and roll torque in hot strip rolling process. Applied Mathematical Modelling, 37(7):4593--4607, 2013.Google Scholar
Cross Ref
- J. L. Calvo-Rolle, J. L. Casteleiro-Roca, H. Quintín, and M. del Carmen Meizoso-Lopez. A hybrid intelligent system for {PID} controller using in a steel rolling process. Expert Systems with Applications, 40(13):5188--5196, 2013.Google ScholarCross Ref
- N. Chakraborti, B. S. Kumar, V. S. Babu, S. Moitra, and A. Mukhopadhyay. A new multi-objective genetic algorithm applied to hot-rolling process. Applied Mathematical Modelling, 32(9):1781--1789, 2008.Google ScholarCross Ref
- S. K. Das. Neural network modelling of flow stress and mechanical properties for hot strip rolling of trip steel using efficient learning algorithm. Ironmaking and Steelmaking, 40:298--304, 2013.Google ScholarCross Ref
- H. Faris, A. Sheta, and E. Öznergiz. Modelling hot rolling manufacturing process using soft computing techniques. International Journal of Computer Integrated Manufacturing, 26(8):762--771, 2013. Google ScholarDigital Library
- J. Ghaisari, H. Jannesari, and M. Vatani. Artificial neural network predictors for mechanical properties of cold rolling products. Advances in Engineering Software, 45(1):91--99, 2012. Google ScholarDigital Library
- M. A. M. C. A. C. A. A. J. L. A. J. (Marquez, Itziar); Arribas. Optimisation of total roll power using genetic algorithms in a compact strip production plant. International journal of materials research, 104:686--696, 2013.Google ScholarCross Ref
- D. C. Montgomery. Introduction to Linear Regression Analysis. John Wiley and Sons, 2007. Google ScholarDigital Library
- L. M. T.-T. no. Evonorm, a new evolutionary algorithm to continuous optimization. In Workshop on Optimization by Building and Using Probabilistic Models (OBUPM) Genetic and Evolutionary Computation Conference (GECCO), 2006.Google Scholar
- L. T.-T. no. Evonorm: Easy and effective implementation of estimation of distribution algorithms. Journal of Research in Computing Science, 23:75--83, 2006.Google Scholar
- N. Reddy, B. Panigrahi, and J. Krishnaiah. Modeling mechanical properties of low carbon hot rolled steels. In J. C. Bansal, P. Singh, K. Deep, M. Pant, and A. Nagar, editors, Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), volume 202 of Advances in Intelligent Systems and Computing, pages 221--228. Springer India, 2013.Google Scholar
- P. Saravanakumar, V. Jothimani, L. Sureshbabu, S. Ayyappan, D. Noorullah, and P. Venkatakrishnan. Prediction of mechanical properties of low carbon steel in hot rolling process using artificial neural network model. Procedia Engineering, 38(0):3418--3425, 2012. INTERNATIONAL CONFERENCE ON MODELLING OPTIMIZATION AND COMPUTING.Google ScholarCross Ref
Index Terms
- A mathematical model of a cold rolling mill by symbolic regression alpha-beta
Recommendations
Modeling Synthesis Processes of Photocatalysts Using Symbolic Regression α-β
MICAI '14: Proceedings of the 2014 13th Mexican International Conference on Artificial IntelligenceSymbolic regression is an application of genetic programming and is used for modeling different dynamic processes. Industrial processes problems have been solved using this technique. In this work a symbolic regression algorithm is used for modeling the ...
Application of symbolic regression on blast furnace and temper mill datasets
EUROCAST'11: Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part IThis work concentrates on three different modifications of a genetic programming system for symbolic regression analysis. The coefficient of correlation R2 is used as fitness function instead of the mean squared error and offspring selection is used to ...
1450 Cold Rolling Mill Rack Finite Element Modal Analysis
MACE '12: Proceedings of the 2012 Third International Conference on Mechanic Automation and Control EngineeringThe vibration of the fifth frame mill of 1450 Cold Tandem Rolling Mill in the rolling process was studied using finite element software ANSYS. The dynamic analysis of one-piece frame and two-piece frame on the fifth frame mill were carried on. The ...
Comments