Abstract

The basic problem of the numerical model’s quenching process is establishing the characteristics of the boundary conditions. The existing descriptions of the boundary conditions, which represent the parameters of equipment used in heat treatment processes, do not accurately reflect the actual process conditions. In the present study, the method of choice for superficial heat source parameters for TIG (tungsten inert gas) heating is modeled using artificial neural networks (ANN) and the finite element method (FEM). A comparison of the calculations obtained from the numerical model of non-steady state heat transfer with the results of the experimental studies is presented. The possibility of using ANN to compute the parameters of the boundary conditions for the heating treatment is analyzed. A multilayer feed-forward backpropagation network is developed and trained using value of temperature in the selected nodes obtained from numerical simulation.

Highlights

  • The continuous development of knowledge in the range of technical fields has increased demands on modern engineers

  • Based on the obtained results and the accuracy of the artificial neural network, it can be concluded that the trained neural network can select parameters of simulation for modeling the tungsten inert gas (TIG) process of medium carbon steels

  • For 62,500 elements (250 elements and 250 epochs) of the training set, this error was below 10%

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Summary

Introduction

The continuous development of knowledge in the range of technical fields has increased demands on modern engineers. Both during the design and implementation of manufacturing processes, special attention is paid to minimizing costs, shortening working time, and improving the efficiency of technological processes. One of the main tools to achieve these goals is optimization. Artificial neural networks (ANN) belong to the dynamically developing field of computational tools called artificial intelligence. They are an attempt to imitate phenomena and processes occurring in nervous systems of living organisms while searching for new technological solutions. It is easier to list the areas in which they do not exist than all those in which they are applied to

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