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Estimation of thermo-elasto-plastic properties of thin-film mechanical properties using MD nanoindentation simulations and an inverse FEM/ANN computational scheme

D. S. Liu1, C.Y. Tsai1

Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Ming-Hsiung, Chia-Yi, 621, Taiwan, R.O.C.

Computer Modeling in Engineering & Sciences 2009, 39(1), 29-48. https://doi.org/10.3970/cmes.2009.039.029

Abstract

Utilizing a thin copper substrate for illustration purposes, this study presents a novel numerical method for extracting the thermo-mechanical properties of a thin-film. In the proposed approach, molecular dynamics (MD) simulations are performed to establish the load-displacement response of a thin copper substrate nanoindented at temperatures ranging from 300~1400 K. The load data are then input to an artificial neural network (ANN), trained using a finite element model (FEM), in order to extract the material constants of the copper substrate. The material constants are then used to construct the corresponding stress-strain curve, from which the elastic modulus and the plastic level of the thin copper film are subsequently derived. The results show that both the elastic modulus and the plastic level decrease more than 30% as the simulation temperature increases from 300 K to 900 K. Comparing the result obtained from the ANN scheme for the elastic modulus of the thin copper film at room temperature with that obtained experimentally from a micro-force indentation test, it is found that the numerically-derived value (174 GPa) is higher than that obtained experimentally (145 GPa) [Pelletier, Krier, Cornet and Mille (2000)]. The discrepancy between the two sets of results is thought to arise primarily as a result of the high strain rate employed in the MD simulations and the assumption of a perfect, defect-free monocrystalline copper microstructure.

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Liu, D. S., Tsai, C. (2009). Estimation of thermo-elasto-plastic properties of thin-film mechanical properties using MD nanoindentation simulations and an inverse FEM/ANN computational scheme. CMES-Computer Modeling in Engineering & Sciences, 39(1), 29–48.



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