LS-SVM approach for modeling the growthe kinetics of FeB and Fe2B layers formed on Armco iron

LS-SVM approach for modeling the growthe kinetics of FeB and Fe2B layers formed on Armco iron

Bendaoud Mebarek Mourad Keddam  Hicham Aboshighiba 

Department of Computer Science, Ibn Khaldoun university of Tiaret, Tiaret, Algeria

Laboratoire de Technologie des Matériaux, Faculté de Génie Mécanique et Génie des Procédés, USTHB, B.P No. 32, 16111, El-Alia, Bab-Ezzouar, Alger, Algérie

The Research Laboratory of Industrial Technologies, Ibn Khaldoun University, Tiaret, Algérie

Corresponding Author Email:
31 October 2018
| Citation



The present study is based on the Least Squares Support Vector Machines (LS-SVM) approach for simulating the boronizing kinetics of Armco iron. This work adopts the Least Square Support Vector Machine for the growth kinetics of FeB and Fe2B layers. This approach uses the regression technique with the theory of statistical learning LS-SVM has been used to simulate the thickness of each boride layer (FeB or Fe2B), the input data of the simulation model are the process temperature and the treatment time. The LS-SVM results are compared to experimental data. The good agreement between the two results confirms the validity of the mathematical model. After the validation, the root mean square error and coefficients of determination are calculated to achieve a good performance and a better accuracy. In this work, the comparison results in a value of root mean square error of 0.14 µm for Fe2B and 0.16 µm for FeB. Furthermore, an equation has been proposed to estimate the thickness of boronized layer as a function of time and temperature using the present model.


LS-SVM, prediction, boronizing, model, simulation

1. Introduction
2. LS-SVM model
3. The collection of the data
4. Results and discussions
5. Conclusions

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