Influence of uniaxial and biaxial pre-straining on the low cycle fatigue performance of DP590 steel

Ghosal, Puja; Raj, Abhishek; Paul, Surajit Kumar

doi:10.1016/j.ijfatigue.2021.106260

Cited by 23 publications

(21 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Masing behavior is an important feature of cyclically deformed materials, which is generally analyzed and compared using the hysteretic curve of the cyclic stable section. Studies have shown that the dual‐phase steel can exhibit Masing behavior under the tension‐compression cycle path 17 . The Masing behavior is influenced by microstructure and processing method 28 .…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The Masing behavior is influenced by microstructure and processing method 28 . Different loading paths and deformation histories can increase or decrease the Masing behavior 13,17 . The half‐life hysteresis curve was extracted, since the DP900 shear cycle hysteresis curve had no evident stable phase.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Studies have shown that the dual-phase steel can exhibit Masing behavior under the tension-compression cycle path. 17 The Masing behavior is influenced by microstructure and processing method. 28 Different loading paths and deformation histories can increase or decrease the Masing behavior.…”

Section: Hysteresis Curve and Plastic Strain Energymentioning

confidence: 99%

“…16 The fatigue life and mechanical property of materials are affected by different strain paths and strain ratios. 17 At present, two methods are used to study the fatigue properties of sheet metals. The first method includes tensile and compression cycling experiments performed on fatigue test machines with the help of clamps, which are used to fix the specimen in the direction of thickness; strain is measured with an extensometer.…”

Section: Introductionmentioning

confidence: 99%

“…Cracks are often initiated in ferrite because of the high strength of martensite, and the expansion behavior is influenced by its distribution 16 . The fatigue life and mechanical property of materials are affected by different strain paths and strain ratios 17 …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Low cycle fatigue performance of DP900 steel under cyclic shear paths

Zhang

Yue

et al. 2021

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

In this study, a sheet fatigue shear test device is designed and applied to the low‐cycle fatigue testing of DP900 with varying strain amplitudes within the range of 0.5%–6.0%. The microstructure is analyzed by using electron backscatter diffraction, and fracture surfaces are examined via scanning electron microscopy. Results indicate that the material exhibits cyclic softening behavior after the first two cycles of hardening, with a stable softening rate and a high damage evolution rate related to loading amplitude. The variation of the hysteresis curve in the cyclic process is shown. Total plastic strain energy absorbed increases as loading amplitude decreases and it reaches the peak at approximately 1%. The life prediction model based on plastic strain energy density and strain amplitude is verified to be suitable for the cyclic shear path. The influence of microinhomogeneity on the distribution of stress and strain, especially the deformation of martensite, is closely related to the bi‐linear region of fatigue life curve.

show abstract

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Hysteresis Curve and Plastic Strain Energymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Low cycle fatigue performance of DP900 steel under cyclic shear paths

Zhang

Yue

et al. 2021

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

show abstract

Prediction of the fatigue curve of high-strength steel resistance spot welding joints by finite element analysis and machine learning

Murakawa

et al. 2023

Int J Adv Manuf Technol

View full text Add to dashboard Cite

The process of resistance spot welding is extensively utilized in automotive assembly. Analyzing the fatigue strength of resistance spot welded (RSW) joints of thin plate high-strength steel holds significant importance in reducing production costs and enhancing vehicle safety during operation. By combining finite element analysis (FEA) and machine learning (ML), a novel method has been developed to predict fatigue curves of RSW joints with high-strength steels of different thicknesses, widths, and nugget diameters. In this study, the impact of various experimental conditions, such as the thickness and width of the sheet material, and the diameter of the nugget, on the fatigue test results, has been quantified. Moreover, the model established through this research enables accurate prediction of the F-N fatigue curves of RSW joints without the need for fatigue testing, thereby saving costs and time required for experimentation. The average error is approximately 8% of the experimental results.

show abstract