Abstract
The computer simulation is replacing mechanical experiments in many cases due to its cost-effectiveness and improved accuracy. Nevertheless, its application fields are still limited to elastic analysis, as there exists a significant amount of model error in present inelastic material models. In this paper, we first propose a material model using neural networks, which has the ability to describe plasticity and cyclic plasticity. The proposed model was first created with the material data of 2 1/4Cr-1 Mo steel, and the results show that the model can represent the actual cyclic plastic material behavior within a 2% error. The model is then implemented in a commercially available finite element analysis package and the cyclic plastic deformation behavior of a test specimen of the material is predicted within a 3% error.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
