Data-Driven Accelerated Parameter Identification for Chaboche-Type Visco-Plastic Material Models to Describe the Relaxation Behavior of Copper Alloys
Published in Experimental Mechanics Journal, 2024
Recommended citation: Morand, L., Norouzi, E., Weber, M. et al. Data-Driven Accelerated Parameter Identification for Chaboche-Type Visco-Plastic Material Models to Describe the Relaxation Behavior of Copper Alloys. Exp Mech 64, 691–702 (2024). https://doi.org/10.1007/s11340-024-01057-x
The objective of this paper is to assess the feasibility of using machine learning to identify the parameters of a Chaboche-type material model that describes copper alloys. Specifically, we apply and analyze this identification approach using short-term uniaxial relaxation tests on a C19010 copper alloy.
Recommended citation: Morand, L., Norouzi, E., Weber, M. et al. Data-Driven Accelerated Parameter Identification for Chaboche-Type Visco-Plastic Material Models to Describe the Relaxation Behavior of Copper Alloys. Exp Mech 64, 691–702 (2024). https://doi.org/10.1007/s11340-024-01057-x