4-5-1 Neural Network results

input layer with 7 nodes for each of ε, ε,’ and T, the logarithmic functions of ε and ε’, the inverse function of T, and curve peak strain εp
hidden layer had 5 nodes
output layer had 1 node for flow stress σ prediction

Desired and predicted flow stress vs. strain for test 28

The rheological behaviour of a work material can be introduced into the FEM code in different ways
An analytical model for the material rheological behaviour can be user selected from a list in the code (e.g. power law, rate power law, Johnson-Cook, Kumar, etc.). Then, the necessary coefficients are manually input by the user
If this method is used, the stress value for each FEM model node is calculated during the simulation using the analytical model
Alternatively, the user can enter three tables representing the work hardening (table(x)), temperature (table(y)) and strain rate (table(z)) effects on the flow stress. The data in the tables are typically obtained from experimental tests

In this paper, the three tables representing the work hardening (table(x)), temperature (table(y)) and strain rate (table(z)) were used for material rheological behaviour input into the FEM code.
However, these tables were compiled through the help of the learned NN for flow stress prediction
The learned NN provides for the flow stress curves of the AISI 1010 C steel for any temperature and strain rate within the experimental range used for NN training set construction

The NN material behaviour prediction is introduced into the FEM code according to the following procedure
The work hardening, temperature and strain rate effects on the flow stress must be computed, i.e. table(x), table(y) and table(z) are needed
These tables can be calculated by considering the stress as the product of a function of strain, a function of temperature and a function of strain rate and by neglecting the coupling effects
Thus, the flow stress is given by:

This work a part of a wider scope research on the thermo-mechanical coupled FEM simulation of AISI 1010 C steel machining
The material rheological behaviour was modelled through a NN approach able to account for the real effects of strain hardening, strain rate and temperature dependencies of the workpiece material into the FEM analysis
By utilizing a learned NN endowed with the knowledge on flow stress of the AISI 1010 C steel, the work material properties can be provided to the FEM code for each node of the workpiece mesh during the simulation
The enormous potential of NN material modelling can be exploited in the simulation of processes, like metal cutting, where classical material description is unsatisfactory
To achieve this results, a continuous information exchange between the learned NN and the FEM code during each iteration must be achieved