Login / Signup

Experimental Investigation of Strain Rate Influence on Anisotropy of Uniaxial Tensile Mechanical Properties of CuFe2P Alloy Sheet.

Ante BubaloZdenko TonkovićLovre Krstulović-OparaVedrana Cvitanić
Published in: Materials (Basel, Switzerland) (2024)
Wire crimping, a process commonly used in the automotive industry, is a solderless method for establishing electrical and mechanical connections between wire strands and terminals. The complexity of predicting the final shape of a crimped terminal and the imperative to minimize production costs indicate the use of advanced numerical methods. Such an approach requires a reliable phenomenological elasto-plastic constitutive model in which material behavior during the forming process is described. Copper alloy sheets, known for their ductility and strength, are commonly selected as terminal materials. Generally, sheet metals exhibit significant anisotropy in mechanical properties, and this phenomenon has not been sufficiently investigated experimentally for copper alloy sheets. Furthermore, the wire crimping process is conducted at higher velocities; therefore, the influence of the strain rate on the terminal material behavior has to be known. In this paper, the influence of the strain rate on the anisotropic elasto-plastic behavior of the copper alloy sheet CuFe2P is experimentally investigated. Tensile tests with strain rates of 0.0002 s -1 , 0.2 s -1 , 1 s -1 , and 5.65 s -1 were conducted on sheet specimens with orientations of 0°, 45°, and 90° to the rolling direction. The influence of the strain rate on the orientation dependences of the stress-strain curve, elastic modulus, tensile strength, elongation, and Lankford coefficient was determined. Furthermore, the breaking angle at fracture and the inelastic heat fraction were determined for each considered specimen orientation. The considered experimental data were obtained by capturing the loading process using infrared thermography and digital image correlation techniques.
Keyphrases
  • high resolution
  • electronic health record
  • machine learning
  • big data
  • magnetic resonance
  • single molecule
  • ultrasound guided
  • stress induced