Fatigue Properties of Rolled Steel Sheets for Automotive Structure
Source abstract
Abstract
The drive toward weight reduction in the automotive industry has led to high-strength steel sheets for structural applications. Pressing and stamping are important processes in automotive production, but they result in additional strain on the vehicle body and chassis sheet material. Therefore, it is important to know the reliability and mechanical behavior of rolled sheets, such as tensile and fatigue strength, according to the type of sheet. This study examined the fatigue properties of the rolled steel sheet with different chemical compositions, which caused changes in the fatigue behavior of sheets. Rolled steel sheets, TWIP, DP-2, SPFH540, and SPFH590 steels with a nominal initial thickness of 2.0–2.5 mm, were used to evaluate the high-cycle fatigue and low-cycle fatigue properties. The fatigue tests revealed cyclic hardening behavior or cyclic softening behavior according to the type of steel sheets, which caused ductile or brittle fatigue fracture patterns, respectively. When the low-cycle fatigue life was evaluated using the Coffin–Manson relation, the TWIP steel, which showed the longest elongation in the monotonic tensile tests, had the shortest fatigue life. In contrast, when the fatigue life was evaluated using the hysteresis loop energy applying the strength and elongation of materials, the TWIP steel displaying cyclic hardening behavior had the longest fatigue life. Hence, a consideration of the changes in the properties of steel sheets with repeating deformation and a representation of the fatigue life using the hysteresis loop energy can allow accurate evaluations of the lifetime and selection of optimal fatigue resistance materials.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Microstructure and Mechanical Properties of Steels · Metal Forming Simulation Techniques · Fatigue and fracture mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (33)
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