Add to ORKGAlthough the correct determination of the material constitutive properties is the key to the successful FEM simulation of machining, the material rheological behaviour at the high temperature, strain rate, and strain conditions encountered in chip formation cannot be provided by normal stress-strain curves or impact tests. In this paper, a neural network approach is used to model the flow dynamics work material behaviour by reconstructing the stress-strain curves of carbon steel work material from tensile test experimental data
In this paper neural networks are utilised to represent the rheological behaviour of the Nickel-base...
In this paper an Artificial Intelligent approach that performs materials' tests and evaluates their ...
Finite element method has, in recent years, been widely used as a powerful tool in analysis of engin...
Although the correct determination of the material constitutive properties is the key to the success...
The fully integrated use of a neural network (N) paradigm for material rheological behaviour modelli...
In this paper, neural network based constitutive models relating stress to deformation conditions of...
A number of semi-empirical models are available in literature to predict flow stress of steel during...
Semi-empirical models for the constitutive behaviour of steels often fail to predict the flow stress...
Introduction Finite element modeling of manufacturing processes has been gaining wider acceptance ov...
The main objectives of this paper are investigations on the usability of artificial neuronal network...
The finite element method (FEM) is widely used for structural analysis in engineering. In order to pr...
Constitutive behavior models for steels are typically semi-empirical, however recently neural networ...
The rheological behaviour of mild steel subjected to hot forming was modelled through a parallel dis...
The hot deformation behaviour of austenite in steels is a complicated process which depends on chemi...
Neural networks provide a potentially viable alternative to a differential equation based constituti...
In this paper neural networks are utilised to represent the rheological behaviour of the Nickel-base...
In this paper an Artificial Intelligent approach that performs materials' tests and evaluates their ...
Finite element method has, in recent years, been widely used as a powerful tool in analysis of engin...
Although the correct determination of the material constitutive properties is the key to the success...
The fully integrated use of a neural network (N) paradigm for material rheological behaviour modelli...
In this paper, neural network based constitutive models relating stress to deformation conditions of...
A number of semi-empirical models are available in literature to predict flow stress of steel during...
Semi-empirical models for the constitutive behaviour of steels often fail to predict the flow stress...
Introduction Finite element modeling of manufacturing processes has been gaining wider acceptance ov...
The main objectives of this paper are investigations on the usability of artificial neuronal network...
The finite element method (FEM) is widely used for structural analysis in engineering. In order to pr...
Constitutive behavior models for steels are typically semi-empirical, however recently neural networ...
The rheological behaviour of mild steel subjected to hot forming was modelled through a parallel dis...
The hot deformation behaviour of austenite in steels is a complicated process which depends on chemi...
Neural networks provide a potentially viable alternative to a differential equation based constituti...
In this paper neural networks are utilised to represent the rheological behaviour of the Nickel-base...
In this paper an Artificial Intelligent approach that performs materials' tests and evaluates their ...
Finite element method has, in recent years, been widely used as a powerful tool in analysis of engin...