A Comparison Study of Constitutive Equation, Neural Networks, and Support Vector Regression for Modeling Hot Deformation of 316L Stainless Steel

Clicks: 228
ID: 111083
2020
Free PDF
Article Quality & Performance Metrics
Overall Quality Improving Quality
9.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
Abstract
In this research, hot deformation experiments of 316L stainless steel were carried out at a temperature range of 800–1000 °C and strain rate of 2 × 10−3–2 × 10−1. The flow stress behavior of 316L stainless steel was found to be highly dependent on the strain rate and temperature. After the experimental study, the flow stress was modeled using the Arrhenius-type constitutive equation, a neural network approach, and the support vector regression algorithm. The present research mainly focused on a comparative study of three algorithms for modeling the characteristics of hot deformation. The results indicated that the neural network approach and the support vector regression algorithm could be used to model the flow stress better than the approach of the Arrhenius-type equation. The modeling efficiency of the support vector regression algorithm was also found to be more efficient than the algorithm for neural networks.
Reference Key
song2020materialsa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shin-Hyung Song;Song, Shin-Hyung;
Journal Materials (Basel, Switzerland)
Year 2020
DOI
10.3390/ma13173766
URL
https://doi.org/10.3390/ma13173766
Keywords
Neural network 316l stainless hot deformation flow stress support vector regression National Center for Biotechnology Information NCBI NLM MEDLINE pubmed abstract nih national institutes of health national library of medicine pmid:32858909 pmc7503741 doi:10.3390/ma13173766 shin-hyung song

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.