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Effect of grain size on fatigue strength of 304 stainless steel

  • Hongyan Duan , Zengwang Zhang EMAIL logo , Yingjian Zhao , Yang Liu , Sunqiang Yue and Hong He
Published/Copyright: March 25, 2024
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High Temperature Materials and Processes
From the journal High Temperature Materials and Processes Volume 43 Issue 1

Abstract

In this study, three types of 304 stainless steel samples with different strengths were prepared by refining the grain size through rolling. The microstructure of the samples was observed by electron microscopy. The influence of grain size on the static tensile properties and fatigue strength of the material is mainly attributed to changes in the plastic deformation fracture mechanism and micro-deformation mechanism. In addition, a new fatigue strength prediction model is proposed based on the influence of tensile strength and work-hardening capacity. Compared with the staircase method and Basquin formula models, the proposed model maintains the accuracy of fatigue strength prediction while reducing the cost of fatigue experiments. This provides a new approach for predicting the fatigue strength of specific materials and improving anti-fatigue design capabilities.

Keywords: grain size; microscopic deformation mechanism; tensile strength; work hardening ability; fatigue strength prediction

1 Introduction

Fatigue failure has been widely recognized as one of the primary failure modes for engineering materials under long-term cyclic loading. Statistics show that 90% of failures in metallic materials are caused by fatigue damage. Typically, fatigue performance optimization is achieved through anti-fatigue design [1]. However, the time-consuming and effort-consuming of fatigue experiments make the development and selection of materials lag seriously. To solve this problem, a simple and effective fatigue strength prediction model is very necessary.

The prediction of fatigue strength has been one of the hot topics in materials science in recent years. In 1870, Wohler proposed a linear relationship between fatigue strength and tensile strength/yield strength. Based on this correlation, Wohler’s criterion suggested that improving the tensile strength of metal materials may improve their fatigue performance. This criterion is still in use today [1,2]. Basquin discovered an obvious linear relationship between the logarithms of S–N curves, which led to the empirical relationship of S–N curves [3,4,5]. In addition, fatigue strength has a certain relationship with other mechanical properties and the microstructure of the material. With the development of material-strengthening mechanisms, such as solid solution strengthening, grain refinement, precipitation strengthening, the strength of materials has been significantly improved [6,7,8]. Murakami proposed a fatigue strength prediction model based on the relationship between material hardness and defect size [9]. Thus began the quantitative study of fatigue strength. Beretta improved Murakami’s model and proposed a method for predicting fatigue strength based on maximum material defects [10]. Pang believed that there was a linear relationship between material hardness and tensile strength. Based on this insight, he improved Murakami’s model and concluded that there is a parabolic relationship between fatigue strength and tensile strength [11]. Liu and Zhang proposed a theoretical model that simultaneously considered fatigue damage and internal resistance. The ratio of fatigue strength to yield strength is used as an indicator of the degree of fatigue damage localization, while the ratio of yield strength to fatigue strength is used as an indicator of the material’s resistance to internal fatigue damage [12]. There is a linear relationship between the two ratios. With the continuous improvement of the experimental level, researchers found that the engineering stress–strain curve ignored the influence of the Poisson effect of materials, so they took into account the non-uniformity of stress distribution and put forward the real stress–strain curve, which can more accurately study the mechanical properties of materials. Mengxiao Zhang and Jian Chao Pang combined the fatigue strength with the real stress–strain curve and took the difference between the highest point and the elastic limit in the real stress–strain curve as the characteristic quantity of the plastic limit [13]. Stinville and Charpagne conducted a quantitative analysis of the relationship between microstructure and single slip positioning events. They utilized multi-modal data to examine the effects of slip action on the deformation of metal alloys and proposed that the fatigue strength of these alloys could be predicted by considering the amplitude of slip positioning during the initial loading cycle [14]. The common feature of these studies is to predict fatigue strength based on the tensile properties of materials.

In this article, we investigated the influence of grain size on static tensile properties and fatigue strength. A series of 304 austenitic stainless steel (SS) specimens with different grain sizes through rolling treatment were obtained. The effects of different rolling deformation on mechanical and fatigue properties were examined, and the relationship between tensile strength and fatigue strength was explored. In addition, a new fatigue strength prediction model was proposed based on the influence of tensile strength and work-hardening ability. The fatigue strength was calculated using this model and compared with the fatigue strength calculated using the Basquin method and staircase method. The advantages and disadvantages of these three methods in calculating fatigue strength were compared.

2 Experimental materials and methods

In this experiment, 304 austenitic SS was used as the experimental object, and its chemical composition is listed in Table 1.

Table 1

Chemical composition of 304 SS

Element C Mn S P Si Cr Ni Fe
Content (%) 0.054 0.79 0.002 0.034 0.42 17.27 8.14 73.29

To obtain 304 SS with different grain sizes, the samples were prepared by the following methods. The 304 SS was heated at 600°C, held for 30 min, and taken out as the original sample (sample A). Then, 30 and 70% deformation treatments were carried out on the rolling mill, respectively, and two 5 mm-thick SS plates were obtained. The original and rolled samples were regarded as three samples with different grain sizes, denoted as A, B, and C, respectively. They are processed into standard tensile and fatigue specimens by a wire-cutting machine. To eliminate the surface differences between the samples, 400# 800# 2000# sandpaper was used for polishing. In this experiment, the original state, 30% deformation, and 70% deformation samples were, respectively, tested with a uniaxial tensile test and a high cycle fatigue test. The sample sizes of the tensile test and fatigue test were the same, as shown in Figure 1.

Figure 1 Size of the tensile specimen and fatigue specimen (top view).
Figure 1

Size of the tensile specimen and fatigue specimen (top view).

Tensile tests were conducted at a strain rate of 10−3 s−1 on the electronic universal test machine INSTIRN 5982, which was controlled by displacement. Three sample tests were carried out on different rolled samples to measure the engineering stress–strain curve of each specimen. The full load of the testing machine was ±100 kN.

Stress-controlled symmetric push–pull fatigue tests were carried out on a servo hydraulic fatigue testing machine INSTORN 8850. Fatigue tests were carried out under uniaxial and sinusoidal loading at a frequency of 30 Hz up to 5 × 106 cycles. The fatigue strength at 5 × 106 cycles was determined by the staircase method.

The average size of the three grains was measured by the intercept method. First, we drew a horizontal or vertical straight line on the electron back scatter diffraction (EBSD) diagram and calculated the number of intersections between the grain boundary and the straight line. Then, the grain size was calculated according to the following formula:

(1) d = L / P i ,

where d is the grain size, L is the length of the straight line, and P i is the number of intersections between the grain boundary and the line. To reduce the error, each specimen was calculated five times. We measured rolling direction (RD) and transverse direction (TD) on the EBSD diagram and finally took the average value as the final value. The grain sizes of A, B, and C samples were 22.3, 3.5, and 2.0 μm, respectively.

3 Results

The staircase method is one of the most commonly used methods for determining the fatigue strength of materials. It is a widely accepted method in the field and has established credibility in evaluating the fatigue properties of materials. Therefore, this experiment adopted the staircase method to measure sample’s fatigue strength.

The staircase method is a method to determine the fatigue limit under a specified cycle base or to determine the fatigue strength under a fixed cycle. The test starts at a stress level higher than fatigue strength and then gradually decreases. If fatigue strength is unknown, yield power or 0.3–0.45σ b may be selected as the initial stress. The first test shall be conducted at the specified stress level. If the failure occurs before the specified life of N = 107 times, the following sample shall be carried out at a lower stress level. Otherwise, it shall be carried out at a higher stress level until all pieces are completed. The difference in stress levels is called the “stress increment,” which should remain constant throughout the test. In this case, we took the stress increment of 4%. After the experiment, 5–6 groups of experimental data of 304 austenitic SS with failure and non-failure were selected, and the weighted average method was used to calculate the fatigue strength of the test samples:

(2) σ w = 1 m ∑ i = 1 n V i σ i ,

where m is the number of data obtained in the experiment. In this experiment, m = 6; n is the stress level of the test; σ i is the ith stress level; V i is the number of tests under i stress level, and the stress values of the three specimens are, respectively, 4, 3, and 3. According to Formula (1), the experimental data shown in Figure 2 are calculated, and the fatigue strength of the three samples is 215, 443, and 480 MPa.

Figure 2 (a)–(c) represent small sample lifting test results of A, B, and C specimens.
Figure 2

(a)–(c) represent small sample lifting test results of A, B, and C specimens.

4 Influencing factors of fatigue strength

In theory [13], a linear relationship exists between tensile strength and fatigue strength. After searching the data [11,13–16], with further increasing the tensile strength, the linear equation cannot adequately be applied to estimate the fatigue strength of those high-strength materials such as steels, Cu and Al alloys. After consideration and analysis of the microchanges in the processing technology and tensile process [17,18], it is considered that the factors affecting the separation of the linear relationship between fatigue strength and tensile strength are as follows.

4.1 Change of fatigue damage location

After searching the data [19], fatigue damage localization is one of the reasons for the fatigue strength decline. Fatigue damage localization intensifies the sensitivity of material defects and reduces microstructure stability. Since the samples obtained in this experiment are all from the same material in the same batch and their surfaces have been polished, the influence of surface defects on fatigue strength can be approximately regarded as constant. The fatigue strength can be regarded as the threshold of a non-propagating crack, and the stress amplitude below the entry will not cause irreparable damage to the material [11]. Therefore, crack initiation dominates the fatigue life of materials. By scanning electron microscopy, we found that the macrofracture morphology on the surface can be divided into three parts: Ⅰ fatigue crack origin zone, П fatigue crack growth zone, and Ⅲ instantaneous fracture zone, as is shown in Figure 3(a)–(c). To identify the initial location of the crack, the origin region of the fatigue crack was enlarged and observed, as shown in Figure 3(d)–(f). Due to the constant change of load, the cracks of the material keep opening and closing to form bright arcs. The origin of these arcs is the origin of fatigue cracks. It can be seen that the fatigue cracks of samples A, B, and C almost all start from the corner of the specimen and propagate along the diagonal. Most of the cracks in specimens A, B, and C started from the corner and propagate along the diagonal direction in the experiment because of the stress concentration caused by the sharp shape of the corner of the rectangular specimen, which indicates that the stress concentration is the dominant influence on fatigue crack generation compared with the defect size. The specimens used in this experiment are samples of regular fatigue test model from the same batch, and their surfaces have been polished. Therefore, the influence of stress concentration caused by shape on fatigue strength can also be regarded as a constant.

Figure 3 Macroscopic fracture morphologies and initial crack locations of three samples: (a) and (d) are the macroscopic fracture morphologies and initial crack locations of sample A, (b) and (e) are the macroscopic fracture morphologies and initial crack locations of sample B, and (c) and (f) are the macroscopic fracture morphologies and initial crack locations of sample C.
Figure 3

Macroscopic fracture morphologies and initial crack locations of three samples: (a) and (d) are the macroscopic fracture morphologies and initial crack locations of sample A, (b) and (e) are the macroscopic fracture morphologies and initial crack locations of sample B, and (c) and (f) are the macroscopic fracture morphologies and initial crack locations of sample C.

4.2 Change of microscopic fracture mechanism

The fracture mechanism changes from ductile fracture to dissociative fracture when there is a significant difference in grain size [20]. As shown in Figure 4, the original 304 SS sample A has low strength and strong toughness due to its large grain size. Under external force, the local defect is generated by microholes. Then, adjacent microholes are connected to form dimples with larger defect sizes. The continuous connection of dimples makes the defect size grow again. Finally, it leads to the formation and propagation of cracks until they fracture.

Figure 4 Microstructure of tensile fracture: (a) Specimen A, (b) Specimen B, and (c) specimen C.
Figure 4

Microstructure of tensile fracture: (a) Specimen A, (b) Specimen B, and (c) specimen C.

Dimple size is related to material plasticity for the same material. The larger the dimple size, the better the plasticity of the material [21,22]. We used the average of the maximum transverse and longitudinal sizes as the size of the dimples in the red circle in Figure 4, and the dimple sizes of specimens A, B, and C are 1.8, 0.6, and 0.2 μm, respectively.

As shown in Figure 4, dimple sizes of specimens A, B, and C gradually decreased with grain refinement, which indicated that the tensile fracture mode of the material changed from ductile fracture of specimen A to semi-quasi state not complete brittle state of specimen C. As shown in Figure 4(a), the dimples of sample A are large, deep, and uneven; the dimple of sample B became shallower and the size decreased significantly, as shown in Figure 4(b). The fracture color of the semi-quasi-state of specimen C is dark, and the dimples are small and shallow (red circle in Figure 4(c)). The same trend also exists in the data of 201LN austenitic SS [21]: The average grain size of 201 LN austenitic SS is refined from 18 μm in coarse-grained samples to 0.9 μm in ultrafine-grained samples, and the average dimple size decreased from 1.5 to 0.9 μm, while the yield strength increased from 389 to 704 MPa.

4.3 Change of microscopic deformation mechanism

In this experiment, the grain sizes of the RDs of specimens 304 SS specimens obtained by different degrees of plastic deformation are 22.3, 3.5, and 2.0 μm, respectively. The cross-section and RD of specimens A, B, and C were, respectively, observed by an EBSD microscope, as shown in Figure 5. It can be observed from Figure 5(a) and (d) that the original specimen A can be approximately regarded as an equiaxed crystal, with little difference in size between the transverse section and the RD. With an increase in the degree of plastic deformation, the grain size slightly decreased in the TD and significantly decreased in the RD. The grain shape gradually changed from the original equiaxed crystal to an elongated crystal, and the grain boundaries increased and became more tortuous. With the decrease of grain size, the mean free path of dislocation inside the material decreased and the dislocation produced plugging in the grain boundary. As a result, the microstructure deformation mechanism inside the material gradually changed from dislocation slip to dislocation plugging, and the tensile strength and fatigue strength of the material are enhanced. Similarly, the scope of application of the Hall–Petch relation based on dislocation slip theory proves this: The Hall–Petch relation is only applicable to materials with grain size ≥20–30 nm [22].

Figure 5 EBSD microstructure of specimens with different grain sizes: (a)–(c) are the TDs of specimens A, B, and C, respectively, and (d)–(f) are the RDs of specimens A, B, and C, respectively.
Figure 5

EBSD microstructure of specimens with different grain sizes: (a)–(c) are the TDs of specimens A, B, and C, respectively, and (d)–(f) are the RDs of specimens A, B, and C, respectively.

In summary, the influence of grain size on tensile strength can be summarized as follows: as the grain size decreased, the surface area per unit volume increased, and there was more dislocation accumulation at grain boundaries. Excessive dislocation packing increased the tensile strength of materials. On the other hand, the effect of grain size on fatigue strength is as follows: a smaller grain size reduces the defect size, making it more difficult for stress to concentrate at sizes exceeding the material’s critical stress. As a result, this inhibits the early nucleation and propagation of cracks, ultimately enhancing fatigue strength.

5 A new fatigue strength prediction model

From the aforementioned content, fatigue strength is influenced not only by tensile strength but also by other factors. In general [12,13,14,15,16], it is believed that tensile strength is the resistance to the dislocation motion. The larger the tensile strength, the higher the resistance to the dislocation motion. Therefore, it can be inferred that an increase in tensile strength is beneficial to fatigue strength. The decrease of plastic deformation capacity during material strengthening could be a general answer to the fatigue strength decline, which is expressed in terms of work hardening capacity. The tensile strength and work-hardening ability influence the fatigue strength of the material synergistically. These two variables are selected as dependent variables to build the fatigue strength prediction model based on the relationship between strength and plasticity, and the decline of plastic ability often accompanies the increase of strength, as shown in Figure 6.

Figure 6 Influencing factors of fatigue strength.
Figure 6

Influencing factors of fatigue strength.

Because the fatigue strength of high cycle or very high cycle is regarded as the threshold stress of fatigue crack propagation, rather than the critical stress caused by crack, yield strength σ y demarcates the start of macroscopic plastic deformation, which more coincides with the meaning of fatigue strength. Therefore, compared to the elastic limit, yield strength is more suitable for representing the resistance to plastic non-uniform deformation related to fatigue strength in static tensile performance. △σ T is used to represent the work-hardening capacity in this article. Since the deformation of the material is taken into account, the true stress–strain curve enables a more accurate evaluation of material properties. The highest point in the real stress–strain curve is regarded as the real tensile strength S b . In the same way, the definition of the yield strength of the engineering stress–strain curve is analogically extrapolated to the real stress–strain curve (the stress value that produces 0.2% residual deformation) as the true yield strength σ T , y , as is shown in Figure 7:

(3) Δ σ T = S b − σ T , y ,

where S b is the real tensile strength and σ T , y is the true yield strength.

Figure 7 Two forms of work hardening capacity.
Figure 7

Two forms of work hardening capacity.

The fatigue strength of materials can be expressed by the following formula:

(4) σ w = g ( σ b ) + f ( Δ σ T ) .

f ( σ b ) represents the relationship between the fatigue strength and the tensile strength of the material, Δσ T represents the work-hardening ability of the material, and f(Δσ T) represents the local deformation resistance of the material. The higher it is, the stronger the ability of the material to maintain dislocation slip and dislocation entanglement, and the better the uniformity of the material deformation.

As is shown in Figure 8, the tensile strengths of samples A, B, and C in the tensile experiment are 725 ± 15, 886 ± 15, and 958 ± 12 MPa respectively. The fatigue strength of the specimen increases with the increase of tensile strength. The experimental data of samples 3, 5, 8 and 316L [12] collected were processed by formulas (5) and (6):

(5) s = σ ( 1 + ε ) ,

(6) e = ln ( 1 + ε ) .

Figure 8 Engineering stress–strain curve of 304 specimens A, B, and C.
Figure 8

Engineering stress–strain curve of 304 specimens A, B, and C.

The resulting true stress–strain curve is shown in Figure 9.

Figure 9 True stress–strain curve of 304 and 316L SSs treated by different processes.
Figure 9

True stress–strain curve of 304 and 316L SSs treated by different processes.

In order to compare with 316L, we chose 304-3, 304-5, and 304-8 as the representative of sample A (primitive state), sample B (HR30%), and sample C (HR70%), respectively. The grain sizes of 304-3, 304-5, and 304-8 are 22.3, 3.5, and 2.0 μm, respectively. It can be seen from Figure 9 that grain refinement improved the tensile strength and decreased work-hardening capability of 304 SS samples. The tensile strengths in the true stress–strain curves of the three 304 samples are 1,303, 1,340, and 1,380 MPa, respectively. And Δσ T of the three 304 samples are 928, 534, and 429 MPa, respectively. From a macropoint of view, work-hardening ability indicates the degree of resistance of a material to local deformation. The higher Δσ T means better ability to keep dislocation slipping and tangling, which means better deformation homogeneity when the heterogeneous deformation occurs. 316L SS and 304 SS have the same changing trend.

Figure 10 shows the relationships between mechanical properties and fatigue strength. It can be seen from Figure 10(a) that the relationship between tensile strength and fatigue strength of 304 SS and 316L SS can be approximately regarded as linear in the early stage. With the increase of the tensile strength, their fatigue strength appeared to increase linearly. With the further increase of the tensile strength, the relationship between tensile strength and fatigue strength is gradually losing linearity. Figure 10(b) shows the relationship between fatigue strength and work-hardening capacity. It is clear that the relationship between fatigue strength and work-hardening capacity is non-linear. So we use the following formula (7) and formula (8) to represent them:

(7) g ( σ b ) = A S b + B ,

(8) f ( Δ σ T ) = C Δ σ T 2 + D σ T + E ,

where A, C, and D are the material parameters, and B and E are the constants.

Figure 10 (a) and (b) the relationship between fatigue strength and true tensile strength of 304,316L [13], and the relationship between the fatigue strength and △σ T of 304, 316L [13].
Figure 10

(a) and (b) the relationship between fatigue strength and true tensile strength of 304,316L [13], and the relationship between the fatigue strength and △σ T of 304, 316L [13].

By ignoring the influence of constant terms and combining similar terms, a new fatigue strength prediction model is proposed:

(9) σ w = a ( S b − σ T , y ) 2 + b S b + c σ T , y ,

where, a, b, and c are the material constants, S b is the real tensile strength, and σ T,y is the true yield strength.

The parameters required by the model are shown in Table 2, and the fitting effect is shown in Figure 11. It is found that the prediction error is within ±7%, which proves that the model is also suitable for these five materials.

Table 2

Tensile properties, fatigue strength, and fitting values of different materials

Material Technology Microstructure σ b σ T,y σ w,exp Model parameter σ w,cal Error (%)
304 Primitive state CG 1,303 380 215 a = −0.00027, b = 0.32, c = 0.14 203 −0.058
HR30% FG 1,340 806 443 456 0.029
HR70% FG 1,380 951 480 515 0.72
316L [13] HR90% FG 1,300 1,160 618 a = −0.00026, b = 0.3, c = 0.19 605 −0.024
HR40% FG 1,225 925 483 519 0.063
Primitive state CG 1,155 337 237 232 −0.02
Cu11%at Al [23] CR10% FG 606 226 190 a = −0.00031, b = 0.29, c = 0.17 176 −0.0721
CR50% UFG 631 302 210 209 −0.004
ECAP NG 488 90 110 117.7 6.3
35Crmo steel [12] Primitive state 2,098 1,840 627 a = −0.00032, b = 0.17, c = 0.17 648 0.033
Temper at 400°C for 90 min 1,617 1,510 548 528 −0.037
Temper at 500°C for 90 min 1,346 1,210 418 428 0.024
Fe–18Mn TWIP steel [24] initial conditions CG 1,004 545 420 a = −0.00052, b = 0.32, c = 0.38 409 −0.026
CR10% CG 1,129 736 570 544 −0.045
CR30% FG 1,357 1,152 845 846 0.001
Figure 11 Comparison of fatigue strength predicted by four material models and experimental fatigue strength.
Figure 11

Comparison of fatigue strength predicted by four material models and experimental fatigue strength.

ECAP is the equal diameter angular extrusion; HR is the hot rolling; CR is the cold rolling; SP is the standardized treatment; RH is the repeated heating; CG is the coarse crystal; FG is the fine crystal; UFG is a superfine crystal. NG for nanocrystals. Error = (σ w,cal − σ w,exp)/σ w,exp

Basquin formula is a traditional S–N curve fitting method, and its expression is

(10) σ a = σ f ′ * ( 2 N f ) b ,

where σ a is the stress amplitude, σ f is the fatigue strength coefficient, N f is the number of fatigue failure cycles, and b is the fatigue strength index.

Basquin formula was used to fit the S–N curves of material 304 and 316L SS, and its parameters and prediction errors are shown in Figure 12. Figure 12(a)–(c) shows the fatigue test data of samples 304 A, B, and C and the fitting curves of the Basquin formula. Figure 12(d)–(f) shows the fatigue test data of samples A, B, and C of 316 and the fitting curve of the Basquin formula. It can be seen from Figure 12(a) and (f) that the original fatigue data of sample 304, 316L have higher dispersion, and the fitting data are more accurate after the strengthening process, such as rolling deformation. This is because the differences between the treated samples are reduced.

Figure 12 (a)–(c) shows the fitting curve of samples A, B, and C Basquin formula for 304. Figure 12(d)–(f) shows the fitting curve of samples A, B, and C Basquin formula for 316.
Figure 12

(a)–(c) shows the fitting curve of samples A, B, and C Basquin formula for 304. Figure 12(d)–(f) shows the fitting curve of samples A, B, and C Basquin formula for 316.

By comparing the staircase method, the Basquin formula, and the fatigue model proposed in this study, the results indicated that the staircase method is the most precise approach for measuring fatigue strength among them. However, it is worth noting that this method also incurs the highest cost. The Basquin formula has dramatically reduced the required fatigue test samples, but it still needs to carry out some fatigue tests. Each fitting value only applies to the same batch of samples prepared by the same process. The advantage is that it can accurately predict the fatigue strength under different cycles in more experimental samples and has higher convincing power, but it consumes time and energy. This study proposes a novel fatigue strength prediction model, which utilizes the static tensile properties obtained from natural stress–strain curves to predict fatigue strength. This model applies to the same material manufactured through different processes, and it can effectively reduce the cost of fatigue experiments without compromising the accuracy of fatigue strength prediction, which offers a fresh perspective and introduces a new approach to fatigue strength prediction.

6 Conclusion

  1. From the microscopic point of view, the reduction of grain size changed the plastic deformation fracture mechanism and the microscopic deformation mechanism of the material: the plastic deformation fracture mechanism changed from ductile fracture to dissociation fracture, and the microscopic deformation mechanism gradually changed from dislocation slip to dislocation plugging, thus enhancing the static tensile property and fatigue strength of the material.

  2. For high-strength materials, due to the influence of stress concentration caused by the shape of standard fatigue samples, the fatigue cracks are often generated from the corner and spread along the diagonal. Compared with surface defects and inclusions, the stress concentration caused by the shape of the sample used in this experiment has a more significant influence on fatigue cracks. Since the fatigue cracks of the experimental samples are all generated from the corners and spread along the diagonal, this effect can be regarded as a constant in this study.

  3. Based on the influence of tensile strength and work-hardening capacity on fatigue strength, a new fatigue strength prediction model is proposed, which can accurately predict the fatigue strength of materials by using the tensile strength and yield strength of materials and greatly reduce the cost of fatigue experiments.

Acknowledgement

The authors express their sincere thanks to Mr. Tang Guoxin for his guidance and Mr. Zhang Mengxiao for his help in the experiment.

  1. Funding information: This work was supported by the National Natural Science Foundation of China (No. 51665028) and Gansu Key R&D programs–Industrial projects (Grant No. 23YFGA0068).

  2. Author contributions: Hongyan Duan: Ideas; formulation or evolution of overarching research goals and aims. Zengwang Zhang: Preparation, creation and/or presentation of the published work, specifically writing the initial draft (including substantive translation). Yingjian Zhao, Yang Liu, Sunqiang Yue, and Hong He: Conducting a research and investigation process, specifically performing the experiments, or data/evidence collection.

  3. Conflict of interest: The authors state no conflict of interest.

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Received: 2023-10-05
Revised: 2024-01-17
Accepted: 2024-01-19
Published Online: 2024-03-25

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  4. Study on properties of CaF2–CaO–Al2O3–MgO–B2O3 electroslag remelting slag for rack plate steel
  5. The origin of {113}<361> grains and their impact on secondary recrystallization in producing ultra-thin grain-oriented electrical steel
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https://doi.org/10.1515/htmp-2022-0314
Keywords for this article
grain size; microscopic deformation mechanism; tensile strength; work hardening ability; fatigue strength prediction
Creative Commons
BY 4.0

Articles in the same Issue

  1. Research Articles
  2. De-chlorination of poly(vinyl) chloride using Fe2O3 and the improvement of chlorine fixing ratio in FeCl2 by SiO2 addition
  3. Reductive behavior of nickel and iron metallization in magnesian siliceous nickel laterite ores under the action of sulfur-bearing natural gas
  4. Study on properties of CaF2–CaO–Al2O3–MgO–B2O3 electroslag remelting slag for rack plate steel
  5. The origin of {113}<361> grains and their impact on secondary recrystallization in producing ultra-thin grain-oriented electrical steel
  6. Channel parameter optimization of one-strand slab induction heating tundish with double channels
  7. Effect of rare-earth Ce on the texture of non-oriented silicon steels
  8. Performance optimization of PERC solar cells based on laser ablation forming local contact on the rear
  9. Effect of ladle-lining materials on inclusion evolution in Al-killed steel during LF refining
  10. Analysis of metallurgical defects in enamel steel castings
  11. Effect of cooling rate and Nb synergistic strengthening on microstructure and mechanical properties of high-strength rebar
  12. Effect of grain size on fatigue strength of 304 stainless steel
  13. Analysis and control of surface cracks in a B-bearing continuous casting blooms
  14. Application of laser surface detection technology in blast furnace gas flow control and optimization
  15. Preparation of MoO3 powder by hydrothermal method
  16. The comparative study of Ti-bearing oxides introduced by different methods
  17. Application of MgO/ZrO2 coating on 309 stainless steel to increase resistance to corrosion at high temperatures and oxidation by an electrochemical method
  18. Effect of applying a full oxygen blast furnace on carbon emissions based on a carbon metabolism calculation model
  19. Characterization of low-damage cutting of alfalfa stalks by self-sharpening cutters made of gradient materials
  20. Thermo-mechanical effects and microstructural evolution-coupled numerical simulation on the hot forming processes of superalloy turbine disk
  21. Endpoint prediction of BOF steelmaking based on state-of-the-art machine learning and deep learning algorithms
  22. Effect of calcium treatment on inclusions in 38CrMoAl high aluminum steel
  23. Effect of isothermal transformation temperature on the microstructure, precipitation behavior, and mechanical properties of anti-seismic rebar
  24. Evolution of residual stress and microstructure of 2205 duplex stainless steel welded joints during different post-weld heat treatment
  25. Effect of heating process on the corrosion resistance of zinc iron alloy coatings
  26. BOF steelmaking endpoint carbon content and temperature soft sensor model based on supervised weighted local structure preserving projection
  27. Innovative approaches to enhancing crack repair: Performance optimization of biopolymer-infused CXT
  28. Structural and electrochromic property control of WO3 films through fine-tuning of film-forming parameters
  29. Influence of non-linear thermal radiation on the dynamics of homogeneous and heterogeneous chemical reactions between the cone and the disk
  30. Thermodynamic modeling of stacking fault energy in Fe–Mn–C austenitic steels
  31. Research on the influence of cemented carbide micro-textured structure on tribological properties
  32. Performance evaluation of fly ash-lime-gypsum-quarry dust (FALGQ) bricks for sustainable construction
  33. First-principles study on the interfacial interactions between h-BN and Si3N4
  34. Analysis of carbon emission reduction capacity of hydrogen-rich oxygen blast furnace based on renewable energy hydrogen production
  35. Just-in-time updated DBN BOF steel-making soft sensor model based on dense connectivity of key features
  36. Effect of tempering temperature on the microstructure and mechanical properties of Q125 shale gas casing steel
  37. Review Articles
  38. A review of emerging trends in Laves phase research: Bibliometric analysis and visualization
  39. Effect of bottom stirring on bath mixing and transfer behavior during scrap melting in BOF steelmaking: A review
  40. High-temperature antioxidant silicate coating of low-density Nb–Ti–Al alloy: A review
  41. Communications
  42. Experimental investigation on the deterioration of the physical and mechanical properties of autoclaved aerated concrete at elevated temperatures
  43. Damage evaluation of the austenitic heat-resistance steel subjected to creep by using Kikuchi pattern parameters
  44. Topical Issue on Focus of Hot Deformation of Metaland High Entropy Alloys - Part II
  45. Synthesis of aluminium (Al) and alumina (Al2O3)-based graded material by gravity casting
  46. Experimental investigation into machining performance of magnesium alloy AZ91D under dry, minimum quantity lubrication, and nano minimum quantity lubrication environments
  47. Numerical simulation of temperature distribution and residual stress in TIG welding of stainless-steel single-pass flange butt joint using finite element analysis
  48. Special Issue on A Deep Dive into Machining and Welding Advancements - Part I
  49. Electro-thermal performance evaluation of a prismatic battery pack for an electric vehicle
  50. Experimental analysis and optimization of machining parameters for Nitinol alloy: A Taguchi and multi-attribute decision-making approach
  51. Experimental and numerical analysis of temperature distributions in SA 387 pressure vessel steel during submerged arc welding
  52. Optimization of process parameters in plasma arc cutting of commercial-grade aluminium plate
  53. Multi-response optimization of friction stir welding using fuzzy-grey system
  54. Mechanical and micro-structural studies of pulsed and constant current TIG weldments of super duplex stainless steels and Austenitic stainless steels
  55. Stretch-forming characteristics of austenitic material stainless steel 304 at hot working temperatures
  56. Work hardening and X-ray diffraction studies on ASS 304 at high temperatures
  57. Study of phase equilibrium of refractory high-entropy alloys using the atomic size difference concept for turbine blade applications
  58. A novel intelligent tool wear monitoring system in ball end milling of Ti6Al4V alloy using artificial neural network
  59. A hybrid approach for the machinability analysis of Incoloy 825 using the entropy-MOORA method
  60. Special Issue on Recent Developments in 3D Printed Carbon Materials - Part II
  61. Innovations for sustainable chemical manufacturing and waste minimization through green production practices
  62. Topical Issue on Conference on Materials, Manufacturing Processes and Devices - Part I
  63. Characterization of Co–Ni–TiO2 coatings prepared by combined sol-enhanced and pulse current electrodeposition methods
  64. Hot deformation behaviors and microstructure characteristics of Cr–Mo–Ni–V steel with a banded structure
  65. Effects of normalizing and tempering temperature on the bainite microstructure and properties of low alloy fire-resistant steel bars
  66. Dynamic evolution of residual stress upon manufacturing Al-based diesel engine diaphragm
  67. Study on impact resistance of steel fiber reinforced concrete after exposure to fire
  68. Bonding behaviour between steel fibre and concrete matrix after experiencing elevated temperature at various loading rates
  69. Diffusion law of sulfate ions in coral aggregate seawater concrete in the marine environment
  70. Microstructure evolution and grain refinement mechanism of 316LN steel
  71. Investigation of the interface and physical properties of a Kovar alloy/Cu composite wire processed by multi-pass drawing
  72. The investigation of peritectic solidification of high nitrogen stainless steels by in-situ observation
  73. Microstructure and mechanical properties of submerged arc welded medium-thickness Q690qE high-strength steel plate joints
  74. Experimental study on the effect of the riveting process on the bending resistance of beams composed of galvanized Q235 steel
  75. Density functional theory study of Mg–Ho intermetallic phases
  76. Investigation of electrical properties and PTCR effect in double-donor doping BaTiO3 lead-free ceramics
  77. Special Issue on Thermal Management and Heat Transfer
  78. On the thermal performance of a three-dimensional cross-ternary hybrid nanofluid over a wedge using a Bayesian regularization neural network approach
  79. Time dependent model to analyze the magnetic refrigeration performance of gadolinium near the room temperature
  80. Heat transfer characteristics in a non-Newtonian (Williamson) hybrid nanofluid with Hall and convective boundary effects
  81. Computational role of homogeneous–heterogeneous chemical reactions and a mixed convective ternary hybrid nanofluid in a vertical porous microchannel
  82. Thermal conductivity evaluation of magnetized non-Newtonian nanofluid and dusty particles with thermal radiation
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