Abstract
In this paper, we proposed a novel approach to extract the plastic properties of materials using the loading curve (P-h) in spherical indentation experiment. The new method uses proper orthogonal decomposition to build the sub-space of indentation P-h snapshots, and then a statistical Bayesian inference model is derived based on the correlations between constitutive parameters and the sub-space coordinates of indentation P-h snapshots. Posterior sampling of material plastic parameters is performed using the Transitional Markov chain Monte Carlo (TMCMC) algorithm. The established new method has several advantages: 1). Bayesian model updating is performed in the sub-space of indentation P-h snapshots, of which the overall computation costs can be reduced greatly; 2). unlike most of the current analysis which can only give a single optimal estimation point of material properties, the present method can take account of the potential model and experiment uncertainties in indentation analysis. It's able to give the probabilistic information of the estimates, and provide a confidence interval (CI) of the inverse identified set of material properties under a certain confidence level, e.g. 95%. The effectiveness of the method is verified by its application on the 20MnB5 steel, of which two hardening laws, Hollomon and Ludwigson, are separately used. The unique solution of material plastic properties is obtained, and the parameters estimated by indentation and uniaxial experiment show good agreement. The influence of the selected hardening law and prescribed maximum indentation experiment depth on the numerical results are also investigated. Results indicate the new method is very reliable and effective. Besides, Ludwigson law gives more stable numerical results of 20MnB5.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.