Abstract
A limited number of material models or flow curves are available in commercial finite element softwares at varying temperature and strain rate ranges for plasticity analysis. To obtain more realistic finite element results, flow curves at wide temperature and strain rate ranges are required. For this purpose, a material model for a medium carbon alloy steel material which is used for fastener production was prepared. Firstly, flow curves of the material were obtained at 4 temperatures (20, 100, 200, 400 °C) and 3 strain rates (1, 10, 50 s-1). Then, experimental data was used to construct an artificial neural networks model (ANN) for the material. 75% of the experimental data was used to train the model and the rest was employed for validation and verification. ANN model used in flow curve prediction was developed using the scikit-learn library on Python. Temperature, strain rate and strain were employed as input parameters and flow stress as output parameter in ANN model. In order to increase the accuracy of the ANN model, the number of hidden layers and the number of neurons were also optimized by mean squared error approach. As a result of studies, an ANN-based material model that can be used for wide range of temperature and strain rate values were developed based on the experimental data.
Highlights
Numerical simulation softwares are actively used in many areas of the industry today
It was concluded that the proposed Artificial Neural Network (ANN) model reflected the input parameters very well and made promising predictions in very short CPU time as 8.10 seconds
A limited number of material models are preloaded on Simufact.forming software, so it finds values by interpolating or extrapolating for material property ranges that are not defined during analysis
Summary
Numerical simulation softwares are actively used in many areas of the industry today. They studied ANN, fuzzy logic, genetic algorithms and the hybridization of these methods to show the ability of SC tools and techniques to effectively address various metal forming problems and related issues They observed that the basic applications of the mentioned SC tools can be classified into the areas of design, optimization and forming processes prediction. Zhang et al (2020) [9] aimed to validate the use of machine vision and deep learning for structural health monitoring, focusing on the practice of detecting a specific bolt loosening In this direction, a data set containing 300 images was collected first and a faster region-based convolutional neural network was trained and validated. The errors are avoided resulting from interpolation or extrapolation that the simulation program uses for missing material data
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