Abstract
Despite the statistical control methods are extensively explored in the literature, there are rarely published researches in ferrotitanium industries. Ferrotitanium is used by steelmakers as a stabilizer to prevent chromium carbide forming at grain boundaries and in the production of low-carbon steels. Steels with relatively high titanium content include interstitial-free, stainless and high-strength low-alloy steels. Ferrotitanium is lighter, stronger and has higher resistance of corrosion compared with iron. The main statistical method which is applying by ferrotitanium industries is a statistical process control method which ignores the correlations between the chemical components to determine the main predict variables for each response variable. In this paper, by applying the supervised learning methods we recognize the possible correlations between the main alloys and prioritize them in the production process of this industry to be a guidance in predicting the quality results of production and to decrease the manufacturing cost of this industry because of producing out-of-range products.
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Notes
- 1.
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.
Multiple R-squared: 0.5526, Adjusted R-squared: 0.5504.
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Pour, M.B., Partovinia, V., Pellerin, R. (2020). Application of Statistical Learning in Ferro-Titanium Industry. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication. FICC 2020. Advances in Intelligent Systems and Computing, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-030-39442-4_17
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