Abstract
The paper involves the establishment of a neural network model with improved algorithm for reverse analysis of simulated indentation tests considering the effects of friction on the contact surfaces. Extensive finite element analyses covering a wide practical range of materials obeying power law strain-hardening have been carried out to simulate the indentation tests. The results obtained from the simulated dual indentations using conical indenters with different geometries considering effects of friction are adopted in the training and verification of the least squares support vector machines involving structural risk optimization. The characteristics and performances of the neural network model for this class of problems are given and deliberated. The tuned networks are able to predict accurately the mechanical properties of a new set of materials. The approach has great potential for the applications on the characterization of a small volume of materials in micro-and nano-electromechanical systems (MEMS & NEMS).
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 callMore From: Modelling and Simulation in Materials Science and Engineering
Disclaimer: 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.
