Prediction model for studying the growth kinetics of Fe2B boride layers during boronizing

被引：6

作者：

Mebarek B. ^{[1
]}

Keddam M. ^{[2
]}

机构：

[1] Department of Computer Science, Ibn Khaldoun University, Tiaret

[2] Laboratoire de Technologie des Matériaux, Département de Sciences des Matériaux, Faculté de Génie Mécanique et Génie des Procédés, USTHB, B.P No. 32, El-Alia, Bab-Ezzouar, Alger

来源：

Ingenierie des Systemes d'Information | 2019年 / 24卷 / 02期

关键词：

Artificial neural network; Boriding; Fe[!sub]2[!/sub]B; Simulation; Thermochemical treatment;

D O I：

10.18280/isi.240212

中图分类号：

学科分类号：

摘要：

The simulation and modelling of the boriding process are considered as a necessary tool to select the suitable parameters for obtaining an adequate boride layer thickness. In spite of the importance of the boriding process in the industrial field, there are no fully successful mathematical models for simulating the boriding process. In this study, we developed a model based on the application of the artificial neural network (ANN) for the thermochemical boronizing process of the AISI 316L stainless steel. We are attempting to apply the ANN approach to determine the layer's thickness and predict the influence of different parameters on the growth kinetic of the boride layers.In order to validate the ANN approach, we used experimental data obtained on AISI 316L steel, borided in a liquid medium (70 % borax + 30 % SiC). The comparison of results obtained by artificial neural network (ANN) model with those given by the mathematical model based on Fick's law and experimental data allow us to validate the ANN model. In addition, the average error generated from the neural network was between 1 and 1.25 μm. © 2019 International Information and Engineering Technology Association. All rights reserved.

引用

页码：201 / 205

页数：4

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