Stress In Austenitic Stainless Steel Research Articles

Design of calculation model of residual stress in austenitic stainless steel during plastic deformation

In the process of processing, the residual stress in the deformed component will play a crucial role in the assembly and future use. This paper analyzes the deformation behavior of martensite phase transition induced by deformation through unidirectional stretching. The martensite phase variables and residual stress distributions after stretching were measured. The calculation model of residual stress distribution is established considering the phase change factors. It provides a theoretical basis for the calculation of phase transition behavior and residual stress distribution in the complex deformation process. A dynamic model based on stress and strain was established under different conditions, and the realization of martensite transformation was added to the finite element software with the help of secondary development subroutine. By comparing with the test data, the results show that the distribution pattern of residual stress can better reflect the residual stress after deformation under the consideration of the phase transformation of martensite.

Experimental and numerical studies of austenitic stainless steel CHS stub columns after exposed to elevated temperatures

This paper presents a thorough testing and finite element modelling study of the material response and structural behaviour of austenitic stainless steel circular hollow section (CHS) stub columns after exposed to elevated temperatures. The testing programme was conducted on two cold-formed austenitic stainless steel circular hollow sections: CHS 73 × 3 and CHS 89 × 3, and involved 16 material tensile coupon tests and 16 stub column tests after exposure to eight levels of elevated temperatures ranging from 30 °C to 1000 °C. The experimental investigation was supplemented by a finite element modelling study, where numerical models were firstly developed and validated against the post-fire stub column experimental results, and afterwards utilised to carry out numerical parametric studies to extend the test data pool over a wider range of cross-section geometric dimensions. The measured post-fire tensile coupon test results indicated that the Young's modulus and the ultimate stress of austenitic stainless steel generally remained unchanged after exposure to elevated temperatures, while the post-fire 0.2% proof (yield) stress and ultimate strain were similar to the corresponding room temperature properties for temperatures up to around 600 °C–800 °C, but experienced a reduction and an increase after exposure to higher elevated temperatures, respectively. Given that there have been no established design standards for stainless steel structures after exposure to fire, the corresponding room temperature design rules, as set out in the European code, American specification and Australia/New Zealand standard, were evaluated for austenitic stainless steel CHS stub columns after exposed to elevated temperatures, revealing safe but conservative cross-section compression resistance predictions, owing principally to the adoption of the 0.2% proof (yield) stress as the design stress without considering strain hardening. The deformation-based continuous strength method accounts for material strain hardening in calculating cross-section capacities at ambient temperature. Its applicability to the design of austenitic stainless steel CHS stub columns after exposed to elevated temperatures was also assessed. The results of the assessment indicated that the continuous strength method leads to both precise and consistent compression resistance predictions of austenitic stainless steel CHS stub columns after exposed to elevated temperatures.

Influence of Work‐Hardening and Lower Temperatures on the Yield Stress of Austenitic Stainless Steel X2CrNi18‐10 under Combined Tension‐Torsion Loading

This study investigates the impact of work‐hardening and lower temperatures on the mechanical properties of austenitic stainless steel X2CrNi18‐10 under multiaxial stress. For this purpose, the yield behavior of hollow tubular specimens under combined torsion‐tension loading is examined using a servo‐hydraulic testing system MTS809.10S. Yield points are determined for different tension‐to‐torsion ratios using 1) solution‐annealed specimens tested at room temperature, 2) pre‐strained specimens with 3.6% α’‐martensite tested at room temperature, and 3) solution‐annealed specimens tested at −20 °C. Experimental results show that the yield stresses of the solution‐annealed material at room temperature and at −20 °C can be reasonably approximated using the von Mises and the isotropic Hosford yield hypothesis. Contrary, the yield stresses of the work‐hardened material cannot be fitted with isotropic yield hypotheses because the required torsional stress component to induce plastic deformation is much higher than suggested in those criteria. This effect is mainly attributed to the strain hardening behavior of the X2CrNi18‐10, showing a comparatively high rate of work‐hardening especially at low strains. A suitable approach for fitting the yield points of the work‐hardened material is found with the anisotropic yield hypothesis according to Hosford.

Influence of Welding Strength Matching Coefficient and Cold Stretching on Welding Residual Stress in Austenitic Stainless Steel

Austenitic stainless steel is widely used in pressure vessels for the storage and transportation of liquid gases such as liquid nitrogen, liquid oxygen, and liquid hydrogen. Cryogenic pressure vessel manufacturing uses cold stretching technology, which relies heavily on welding joint performance, to construct lightweight and thin-walled vessels. Residual stress from welding is a primary factor in cases of austenitic stainless steel pressure vessel failure. In this paper, on the basis of Visual Environment 10.0 finite element simulation technology, the residual stress resulting from different welding strength matching coefficients (0.8, 1, 1.2, 1.4) for two S30408 plates welded with three-pass butt welds is calculated according to thermal elastoplastic theory. In addition, the stress field was calculated under a loading as high as 410 MPa and after the load was released. Path 1 was set to analyze stress along the welding line, and path 2 was set to analyze stress normal to the welding line. The welding strength matching coefficient strongly affected both the longitudinal residual stress (center of path 1) and the transverse residual stress (both ends of path 1) after the welding was completed. However, the coefficient had little effect on the longitudinal and transverse residual stress of path 2. Under the loading of 410 MPa, the longitudinal and transverse stress decreased and the stress distribution, with different welding strength matching coefficients, was less diverse. After the load was released, longitudinal and transverse stress distribution for both path 1 and path 2 decreased to a low level. Cold stretching could reduce the effect of residual stress to various degrees. Transverse strain along the stretching direction was also taken into consideration. The experimental results validated the reliability of the partial simulation.

X-Ray Residual Stress Measurement of Austenitic Stainless Steel Based on Fourier Analysis

In a previous study, the authors introduced a new nondestructive method to measure stress with a two-dimensional X-ray diffraction image. This method was tested on a carbon steel specimen. To apply this method to the structures of nuclear power plants, it is essential to show that the residual stress of austenitic stainless steel can be measured. We report on an experiment in which the method was used to measure the stress in austenitic stainless steel standards. The results were consistent with the specification of the stress standard and the conventional sin2 ψ method. We conclude that the proposed method is promising for residual stress measurement of austenitic stainless steels.

A Method to Estimate Residual Stress in Austenitic Stainless Steel Using a Microindentation Test

This study proposed a method to evaluate the residual stress and plastic strain of an austenitic stainless steel using a microindentation test. The austenitic stainless steel SUS316L obeys the Ludwick’s work hardening law and is subjected to in-plane equi-biaxial residual stress. A numerical experiment with the finite element method (FEM) was carried out to simulate an indentation test for SUS316L having various plastic strains (pre-strains) and residual stresses. It was found that the indentation force increased with increasing pre-strain as well as with compressive residual stress. Next, a parametric FEM study by changing both residual stress σres and pre-strain epre was conducted to deduce the relationship between the indentation curve and the parameters epre and σres (which were employed for the FEM study). This relationship can be expressed by a dimensionless function with simple formulae. Thus, the present method can estimate both epre and σres, when a single indentation test is applied to SUS316L.

An exponential strain dependent Rusinek–Klepaczko model for flow stress prediction in austenitic stainless steel 304 at elevated temperatures

In this paper, to predict flow stress of Austenitic Stainless Steel (ASS) 304 at elevated temperatures the extended Rusinek–Klepaczko (RK) model has been modified using an exponential strain dependent term for dynamic strain aging (DSA) region. Isothermal tensile tests are conducted on ASS 304 for a temperature range of 323–923K with an interval of 50K and at strain rates of 0.0001s−1, 0.001s−1, 0.01s−1 and 0.1s−1. DSA phenomenon is observed from 623 to 923K at 0.0001s−1, 0.001s−1 and 0.01s−1. Material constants are calculated using data obtained from these tensile tests for non-DSA and DSA region separately. The predicted results from the RK model are compared with the experimental data to check the accuracy of the constitutive relation. It is observed that to find out the constants of this model, some initial assumptions are required, and these initial values affect the predicted values. Hence, Genetic Algorithm (GA) is used to optimize the constants for RK model. Statistical measures such as the correlation coefficient, the average absolute error and standard deviation are used to measure the accuracy of the model. The resulting values of the correlation coefficient for ASS 304 for non-DSA and DSA region using modified extended RK model are 0.9828 and 0.9701. This modified, extended RK model is compared with Johnson–Cook (JC), Zerilli–Armstrong (ZA) and Arrhenius models and it is observed that specifically in DSA region, the modified extended RK model gives highly accurate predictions.

A Constitutive Description to Predict High-temperature Flow Stress in Austenitic Stainless Steel 316

The main focus of this paper is to predict the flow stress of Austenitic Stainless Steel-316 (ASS) in both non-Dynamic Strain Aging (non-DSA) and Dynamic Stain Aging (DSA) regime. A comparative study has been made on modified mechanical threshold stress (m-MTS) model and modified Khan–Huang–Liang (m-KHL) model to study the strain, strain rate-dependent & temperature dependant behaviour of materials. For this purpose, m-MTS model is developed in both non-DSA and DSA regime which comprise various material constants. Genetic algorithm optimization technique is used to optimize material constants for the model. Suitability of this model was evaluated by comparing the correlation coefficient, average absolute error and its standard deviation. The proposed m-MTS model could predict the elevated-temperature flow behaviour of ASS validating better agreement with the experimental data in both non-DSA and DSA regime compared to m-KHL model

Constitutive models to predict flow stress in Austenitic Stainless Steel 316 at elevated temperatures

Strain, strain rate and temperature have a significant impact on the flow stress of a material. To study the impact of these factors on flow stress, quite a few empirical, semi-empirical constitutive models have been reported. In this work, four such models are being presented for estimation of flow stress on Austenitic Stainless Steel 316. While the Johnson Cook model, Modified Zerilli–Armstrong model are semi-empirical models, the Arrhenius type equation is a physical based equation. The Artificial Neural Network model on the other hand is trained based on the training data and employed to predict the flow stress on the testing data. The experiments for these data were conducted at various strain rates (0.1–0.0001s−1) and at various temperatures (323–623K). Values of stress were taken at strain intervals of 0.05 (from 0.05 to 0.3) to evaluate the material constants of the constitutive models. A comparative study on the reliability of the four models has also been made at the end. The correlation coefficient values observed were 0.9423 (JC model), 0.9879 (modified ZA model), 0.9852 (modified Arrhenius type equation) and 0.9930 (ANN model).

Prediction of flow stress in dynamic strain aging regime of austenitic stainless steel 316 using artificial neural network

Flow stress during hot deformation depends mainly on the strain, strain rate and temperature, and shows a complex nonlinear relationship with them. A number of semi empirical models were reported by others to predict the flow stress during deformation. In this work, an artificial neural network is used for the estimation of flow stress of austenitic stainless steel 316 particularly in dynamic strain aging regime that occurs at certain strain rates and certain temperatures and varies flow stress behavior of metal being deformed. Based on the input variables strain, strain rate and temperature, this work attempts to develop a back propagation neural network model to predict the flow stress as output. In the first stage, the appearance and terminal of dynamic strain aging are determined with the aid of tensile testing at various temperatures and strain rates and subsequently for the serrated flow domain an artificial neural network is constructed. The whole experimental data is randomly divided in two parts: 90% data as training data and 10% data as testing data. The artificial neural network is successfully trained based on the training data and employed to predict the flow stress values for the testing data, which were compared with the experimental values. It was found that the maximum percentage error between predicted and experimental data is less than 8.67% and the correlation coefficient between them is 0.9955, which shows that predicted flow stress by artificial neural network is in good agreement with experimental results. The comparison between the two sets of results indicates the reliability of the predictions.

Residual Stresses in Austenitic Stainless Steel due to High Strain Rate

Austenitic stainless steel (SUS316L) was used as specimen material, and the plate specimens were deformed plastically with a wide range of strain rates (6.67×10-5~ 6.70×102/s). The residual micro-stress for each lattice plane was measured with hard synchrotron X-rays. The residual macro-stress due to tensile deformation depended on strain rate. The residual micro-stresses varied from tension to compression, depending on the diffraction elastic constant. The soft lattice plane had tensile residual stress, and the hard lattice plane had compressive residual stress. The higher the strain rate, the smaller the difference in residual micro-stresses. The residual micro-stresses of the surfaces peened with the laser-peening or water-jet-peening were examined. Both surfaces had exhibited large compressive residual stress. The residual micro-stress on the peened surfaces showed a tendency opposite to residual micro-stress due to tensile deformation.

Measuring residual stresses in stainless steel—recent experiences within a VAMAS exercise

Residual stresses impact on a wide variety of industrial sectors including the automotive, power generation, industrial plant, construction, aerospace, railway and transport industries, and a range of materials manufacturers and processing companies. The X-ray diffraction (XRD) technique is one of the most popular methods for measuring residual stress (Kandil et al., 2001) used routinely in quality control and materials characterization for validating models and design. The VAMAS TWA20 Project 3 activity on the “Measurement of Residual Stresses by X-ray Diffraction” was initiated by NPL in 2005 to examine various aspects of the XRD test procedure in support of work aimed at developing an international standard in this area. The purpose of this project was to examine and reduce some of the sources of scatter and uncertainty in the measurement of residual stress by X-ray diffraction on metallic materials, through an international intercomparison and validation exercise. One of the major issues the intercomparison highlighted was the problem associated with measuring residual stresses in austenitic stainless steel. The following paper describes this intercomparison, reviews the results of the exercise and details additional work looking at developing best practice for measuring residual stresses in austenitic stainless steel, for which X-ray measurements are somewhat unreliable and problematic.

S78 Thermal Stability of Mechanical Surface Treatment Induced Residual Stress in Austenitic Stainless Steel by Neutron and Synchrotron Diffraction

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OS5-1-1 Measurement of Stress in Austenitic Stainless Steel by an Indentation Method

Compressive residual stress is introduced into various components by surface modification methods in order to mitigate stress corrosion cracking susceptibility and to improve fatigue strength. There are also some nondestructive evaluation methods of residual stress including X-ray diffraction, neutron diffraction and Barkhausen noise. However, these methods are not suitable for small areas of austenitic stainless steel because it has large grain size and nonmagnetic property. A more reliable method is therefore required to evaluate residual stress in austenitic stainless steel. Recently, much research has focused on an indentation method for the metrology of residual stress. The indentation load-depth curve and impression size obtained from the indentation process can be used to determine residual stress and various mechanical properties such as hardness, the Young's modulus, fracture toughness, yield stress and work hardening exponent of a material. However, these values affect each other and it is necessary to clarify the relations between residual stress and each mechanical property. This study investigates the possibility of separating stress and mechanical properties both experimentally and by numerical calculation using the micro indentation method. For the indentation test using a sharp indenter, it was possible to evaluate stress by comparison of the maximum depth hmax or residual depth hf obtained from the load-depth curve. Furthermore, the 2-dimensional X-ray diffraction method is examined comparatively with the indentation method.

Evaluation of Subsurface Distribution of Residual Stress in Austenitic Stainless Steel Using Strain Scanning Method

The strain scanning method was applied to the evaluation of the subsurface distribution of the residual stress beneath the shot-peened surface of an austenitic stainless steel SUS304L which had coarse grains and preferred orientation. The experiment was performed at beam line BL22XU at SPring-8 using monochromatic X-rays of 70.14 keV and a Ge (111) analyzer. The sizes of both incident and receiving slits were 2 × 0.2 mm2. The specimens were annealed or shot-peened and had the dimensions of 20 × 20 × 5 mm3. The grain size was about 37 μm. In order to obtain the diffractions from an enough number of grains, various types of oscillation methods, which were translation, rotation and tilting of the specimen, were examined. The translational oscillation was found to be enough to obtain the accurate strain distribution. By combining the translational oscillation method with the correction to the surface aberration, the subsurface distribution of the residual stress of shot-peened austenitic stainless steel was successfully determined.

オーステナイト系ステンレス鋼における変形応力のひずみ速度依存性に及ぼす窒素添加の影響

Effect of nitrogen addition on strain rate dependence of flow stress in austenitic stainless steel (SUS310S) was investigated. It is made clear that both thermal and athermal flow stress components are increased with increasing of nitrogen concentration. The flow stress was described by using the Kocks-Mecking (KM) model, where the KM parameters were determined from the data obtained by tension tests at low strain rates below RT. The flow stress at a high speed, 2×103 s-1 measured by the Hopkinson split-bar method agrees well with the prediction by the KM model. High nitrogen bearing steel is characterized by higher flow stress with a considerably large work-hardening at high speed deformation, which has never been achieved in ferritic steels. Because microstructure evolution is influenced by deformation temperature, flow stress shows the deformation history dependence in nitrogen bearing steels.

Effect of sintering temperature on the densification, shrinkage and compressive strength of stainless steel 300 series

The design and processing of stainless steel P/M parts are subject to the same basic considerations as for other P/M materials. There are many reasons for choossing stainless steel P/M parts, such as corrosion resistance, wear resistance, magnetic and non-magnetic properties and good mechanical properties. This paper investigates the effect of sintering temperature on the mechanical properties in terms of the compressive stress of austenitic stainless steel (300 series). Compacts were pressed to 80% initial relative density and sintered at four sintering temperatures for one hour. The results show increased densification, shrinkage and strength with increase in the sintering temperature.