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Material Model for Predicting Dynamic Response of Copper and Nickel at Very High Strain Rates Under Cold Spray Conditions

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Abstract

Numerous material models have been proposed to describe material properties under impact-induced extreme conditions. All these models suffer from the fact that the tests used to provide the required empirical parameters were performed on bulk material at limited strain rates, much lower than those found in the cold spray process. In this work, recent published results available in the literature that were obtained using advanced imaging techniques are used to modify material plasticity models to accurately predict the material plastic flow stress under cold spray conditions. The capability of two frequently used material models, Johnson–Cook and Preston–Tonks–Wallace models, to predict real-time experimental data for copper and nickel particles is evaluated. A series of finite element simulations are conducted and the comparison is made with the coefficient of restitution from laser-induced projectile impact test data. The original empirical parameters in Preston–Tonks–Wallace model are re-calibrated so that the coefficient of restitution matches the laser-induced projectile impact test data, providing an enhanced quality in the particle impact behavior predictions. Additionally, experiments and modeling are conducted to explore the bonding mechanism and rebound phenomena of copper powder cold sprayed on low carbon steel substrate.

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Acknowledgment

The authors would like to express our great appreciation to Professor Christopher A. Schuh and Mr. Yuchen Sun for providing us with the experimental SEM images published in the Journal of Applied Mechanics. we would also like to thank the publisher and the American Society of Mechanical Engineering (ASME) for granting us the permission to reproduce the material in this article.

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Razavipour, M., Jodoin, B. Material Model for Predicting Dynamic Response of Copper and Nickel at Very High Strain Rates Under Cold Spray Conditions. J Therm Spray Tech 30, 324–343 (2021). https://doi.org/10.1007/s11666-020-01137-z

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