Control and Identification of Mould Bath Level Using Neural Networks

Bouhouche, Salah; Lahreche, M.; Meradi, H.; Bast, Jürgen

doi:10.1016/s1474-6670(17)30108-8

Cited by 1 publication

(2 citation statements)

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“…Below are provided the main results of the studies in which artificial intelligence techniques have been applied in the following technologies in foundries: the melting metal process, sand casting process, die casting process, continuous casting process and investment casting process. Bouhouche et al (2004) presented the application of neural networks in the steel industry. Namely, they introduced the control of the melting process by using neural networks, with the goal to optimally control the input variables such as the weights of additives (FeMn, FeSi, and coke) and heating temperature (T) [1].…”

Section: Introductionmentioning

confidence: 99%

“…Bouhouche et al (2004) presented the application of neural networks in the steel industry. Namely, they introduced the control of the melting process by using neural networks, with the goal to optimally control the input variables such as the weights of additives (FeMn, FeSi, and coke) and heating temperature (T) [1]. Fernandez et al (2008) developed a neuro-fuzzy model for improving the control through a better prediction of the final temperature and, as a consequence, to reducing the consumption of energy in the electric arc furnace [2].…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning in the Control of Metal Melting Production Process

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%