A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe₂B layers during the boronizing process

Abstract

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

A Stochastic Model and Investigation into the Probability Distribution of the Thickness of Boride Layers Formed on Low-Carbon Steel

“…This sequence of actions explains why the FeB is typically found on the outer layer in carbon steels that are subjected to boriding for relatively long periods, and at high temperatures. Therefore, the thickness and quality of the boride layer are dependent on the chemical composition of the substrate, boron potential of the boron source, temperature, and treatment duration [1,6]. Thus, the morphology, growth, and phase composition of the boride layer are affected by the alloying elements contained in the substrate [7], which can hinder the boride diffusion process.…”

Effect of Carbon Content and Boronizing Parameters on Growth Kinetics of Boride Layers Obtained on Carbon Steels

“…While many authors investigated boronizing kinetics using the Arrhenius equation, other authors based their research on other approaches. Campos et al [ 28 ] investigated growth kinetics using dimensional analysis, Mebarek et al [ 29 ] used a fuzzy neural network-based approach, while Velázquez-Altamirano et al [ 30 ] took a stochastic approach and used the Markov chain to model growth kinetics. Statistical methods, in particular the response surface methodology (RSM), have also been used in the study of boronizing.…”

Application of integral method for investigating the boriding kinetics of AISI 316 steel

“…For these reasons, the modeling of boriding kinetics is crucial for optimizing the desired boride layer thickness that matches the practical utilization of borided steels at industrial scale. To reach this objective, several studies were devoted in the literature for modeling the boriding kinetics of ferrous alloys: (Armco iron [3][4][5][6], steels [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] and cast irons [25][26][27][28]) by using different approaches.…”