Application of Machine Learning in the Control of Metal Melting Production Process
Source abstract
Abstract
This paper presents the application of machine learning in the control of the metal melting process. Metal melting is a dynamic production process characterized by nonlinear relations between process parameters. In this particular case, the subject of research is the production of white cast iron. Two supervised machine learning algorithms have been applied: the neural network and the support vector regression. The goal of their application is the prediction of the amount of alloying additives in order to obtain the desired chemical composition of white cast iron. The neural network model provided better results than the support vector regression model in the training and testing phases, which qualifies it to be used in the control of the white cast iron production.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Metallurgical Processes and Thermodynamics · Industrial Technology and Control Systems · Metallurgy and Material Forming
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Technical Sciences Čačak, University of Kragujevac, 32000 Čačak, Serbia
Department of Mechanical Engineering, Technical College Čačak, 32000 Čačak, Serbia
Research and Development Institute Lola Ltd., 11000 Belgrade, Serbia
Faculty of Mechanical Engineering, University of Niš, 18000 Niš, Serbia
Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
References (22)
Show source-provided references
1. Modeling of ladle metallurgical treatment using neural networks
2. Online estimation of electric arc furnace tap temperature by using fuzzy neural networks10.1016/j.engappai.2007.11.008 ↗
3. Process control strategies for a steel making furnace using ANN with Bayesian regularization and ANFIS10.1016/j.eswa.2009.06.056 ↗
4. Prevention of defects in castings using back propagation neural networks10.1007/s00170-007-1289-0 ↗
5. Multi-objective optimization of green sand mould system using evolutionary algorithms10.1007/s00170-011-3365-8 ↗
6. Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithm
7. Optimization of the gating system for sand casting using genetic algorithm
8. An adaptive neuro-fuzzy inference system (ANFIS) model for high pressure die casting10.1177/0954405411406054 ↗
9. An intelligent system for low-pressure die-cast process parameters optimization10.1007/s00170-012-4190-4 ↗
10. Breakout prediction for continuous casting using genetic algorithm based back propagation neural network model10.1504/IJMIC.2012.047727 ↗
11. A particle swarm approach for optimization of secondary cooling process in slab continuous casting10.1016/j.ijheatmasstransfer.2015.10.025 ↗
12. Comparison of some neural network and multivariate regression for predicting mechanical properties of investment casting10.1007/s11665-014-1029-1 ↗
13. Optimization of the investment casting process using genetic algorithm
14. Modeling and optimization of surface roughness in single point incremental forming process10.1016/j.jmrt.2015.01.003 ↗
15. Tool breakage detection using support vector machine learning in a milling process10.1016/j.ijmachtools.2004.08.016 ↗
16. Support vector machine in machine condition monitoring and fault diagnosis10.1016/j.ymssp.2006.12.007 ↗
17. Fault diagnosis of ball bearings using machine learning methods10.1016/j.eswa.2010.07.119 ↗
18. Reliable multi-objective optimization of high-speed WEDM process based on Gaussian process regression10.1016/j.ijmachtools.2007.07.011 ↗
19. Hagan, M. (2014). Neural Network Design, Oklahoma State University. [2nd ed.].
20. Training feed-forward networks with the Marquardt algorithm10.1109/72.329697 ↗
21. Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer.10.1007/978-1-4757-2440-0 ↗
22. Hsu, C.W., Chang, C.C., and Lin, C.J. (2003). A Practical Guide to Support Vector Classification, Department of Computer Science and Information Engineering, University of National Taiwan. Technical Report.