Constitutive modeling of cyclic plasticity deformation and low–high-cycle fatigue of stainless steel 304 in uniaxial stress state

Bondar, V. S.; Dansin, Vladimir V.; Vu, Long D.; Duc, Nguyen Dinh

doi:10.1080/15376494.2017.1342882

Cited by 15 publications

(6 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The low cycle underwent softening and then hardening to fracture, which is a similar phenomenon reported in the literature [13,14]. The hardening and softening behavior of the annealed 304 stainless steel could be analyzed further on the hysteresis curve (stress vs. strain) [10]. Based on Figure 5, the evolution of stressstrain changes in each cycle or the hysteresis curve on the results of the low-cycle fatigue test was found in Figure 5, with a strain amplitude of 0.007 mm/mm.…”

Section: With Variations In Strain Amplitudesupporting

confidence: 71%

“…However, technical components, such as pipes made of type 304 stainless steel through cold drawing, have not been widely conducted or even carried out by several overseas researchers. The cold-tensile manufacturing process developed on structural components of high-alloy low carbon steel (304 austenitic stainless steel), generally improves mechanical strength performance with good material ductility [9,10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Low-Cycle Fatigue of Cold-Drawn Type 304 Austenitic Stainless Steel in Annealing Conditions

2022

View full text Add to dashboard Cite

Type 304 austenitic stainless steel, or better known as SS304, generally has alloying elements: C < 0.1%, Cr 18 – 20%, Fe 66 – 74%, Mn <2%, Ni 8 – 10.5%, P <0.045%, Si <1%, and S <0.030%. In general, this material has good ductility, high tensile strength, and excellent corrosion resistance. In application, type SS304 will be subjected to repeated loading, and eventually, the material will undergo plastic deformation, which leads to structural failure in a short life. The failure of SS304 is generally due to the inability of the material to repeat loading, which results in large amounts of plastic deformation so that the SS304 will experience fatigue and then fracture. Based on the description above, this study aims to evaluate the LCF properties of 304 CDS stainless steel with annealing heat treatment. The parameter used was the strain amplitude 0.003 – 0.013 mm/mm. The results of this study revealed that the highest fatigue life in the LCF test was experienced by steel with heat treatment at an amplitude of 0.003 mm/mm with 48367 cycles. In contrast, at the amplitude condition of 0.013 mm/mm, the fatigue life of the steel decreased drastically with the resulting plastic strain being larger, namely 0.0094 mm/mm and elastic strain of 0.0035 mm/mm, with an average modulus of elasticity of 194 GPa. Annealing treatment conditions experienced decreased mechanical strength but tended to be ductile. Using Basquin-Coffin-Manson Equation, empirical equations to predict LCF of 304 stainless steels can be determined.

show abstract

Section: With Variations In Strain Amplitudesupporting

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Low-Cycle Fatigue of Cold-Drawn Type 304 Austenitic Stainless Steel in Annealing Conditions

2022

View full text Add to dashboard Cite

show abstract

“…The training of the BP neural network is to output the data neural network according to the system's input so that the trained BP network can predict the input and output of the system. According to the existing 500 sets of input and output data [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][40][41][42][43][44][45][46][47][48][49][50][51][52], 400 sets of them are selected as the training data of the network, and the remaining 100 sets are used as the test data to verify the fitting ability of the network. The training function uses the fast convergence L-M optimization algorithm trainlm function with fast convergence, and the specific parameters are set as training times 100, training accuracy 0.00001, and learning rate 0.1.…”

Section: Bp Neural Network Training Algorithmmentioning

confidence: 99%

Prediction model of low cycle fatigue life of 304 stainless steel based on genetic algorithm optimized BP neural network

Duan

Cao

et al. 2022

Mater. Res. Express

View full text Add to dashboard Cite

The low cycle fatigue life of 304 stainless steel is an essential basis for safety assessment. Usually, there is a complex nonlinear relationship between fatigue life and influencing factors, which is difficult to be predicted by traditional fatigue life models. Based on this, the BP algorithm and genetic optimization algorithm (GA) for the fatigue life prediction problem of 304 stainless steel is proposed. Based on the existing large amount of test data, the fatigue life of 304 stainless steel material is predicted by using BP and GA-BP learning models. The results show that the GA-BP prediction model is more flexible, the correlation coefficient R reaches 0.98158, the prediction data are within the 2 times error limit and closer to the ideal line, and the model prediction is better.

show abstract

“…Учениками Тверской научной школы под руководством В.Г. Зубчанинова наиболее подробно экспериментально исследованы простые процессы в пространстве напряжений, при нагружении оболочек силами растяжения, сжатия, кручения или внутреннего давления [6][7][8][9][10][11]. В то же время оценка нагружения оболочек под одновременным действием нескольких сил, которое можно назвать сложным нагружением, в работах встречается крайне редко и требует внимания в настоящее время [12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionunclassified

Theoretical and experimental modeling of deformation of a cylindrical shell made of 45 steel under complex loading

Черемных

2022

Struct. Mech. of Eng. Const. and Build

View full text Add to dashboard Cite

Thin-walled cylindrical shells are used in elements of highly loaded products of mechanical engineering and energy. Along with their frequent use in production, experimental research in laboratories is also carried out constantly. This allows to simulate the behavior of the shell when exposed to external forces. But sometimes conducting an experiment becomes little possible due to the limitation of the power of the experimental apparatus when modeling the corresponding conditions of exposure to the shell in practice, therefore, improving theoretical methods for calculating the limiting states of shells when working in the elastoplastic region is relevant. The purpose of the study is to verify the conformity of the results of the experiment conducted on a thin-walled cylindrical shell made of steel 45 (GOST 1050-2013) when exposed to the sample by stretching, compression and torsion forces with theoretical calculations based on the equations of the theory of elastic-plastic processes by A.A. Ilyushin. The equations of the defining relations of the theory of elastic-plastic processes by A.A. Ilyushin for arbitrary trajectories of complex loading and deformation of materials in the deviatory deformation space Э1-Э3 are presented. All theoretical results are checked for compliance with the experiment, the reliability of the existing theory of stability is assessed. The solution is presented in the form of graphs of the dependence of the vector and scalar properties of the material on the length of the arc of the deformation trajectory and other parameters. Numerical values are selectively presented for different loading stages.

show abstract

Constitutive modeling of cyclic plasticity deformation and low–high-cycle fatigue of stainless steel 304 in uniaxial stress state

Cited by 15 publications

References 49 publications

Low-Cycle Fatigue of Cold-Drawn Type 304 Austenitic Stainless Steel in Annealing Conditions

Low-Cycle Fatigue of Cold-Drawn Type 304 Austenitic Stainless Steel in Annealing Conditions

Prediction model of low cycle fatigue life of 304 stainless steel based on genetic algorithm optimized BP neural network

Theoretical and experimental modeling of deformation of a cylindrical shell made of 45 steel under complex loading

Contact Info

Product

Resources

About