Stainless Steel Test Research Articles

Rotating Bending Fatigue of Laser Powder Bed Fused 316L Stainless Steel at Various Stress Levels: Microstructural Evaluation and Predictive Modeling

ABSTRACTThis research studies the effect of variable stress levels on the rotating bending fatigue (RBF) tests of 316L stainless steel fabricated by the laser powder bed fusion (LPBF) method. The mechanical properties and fatigue behavior were evaluated to ascertain the correlation between the applied stress levels and microstructural changes that occurred during the fatigue experiments. These relationships were further investigated using a classical model developed on the Python platform. The microstructural analysis demonstrated that the face‐centered cubic structure was maintained throughout the application of stresses, varying from 255 to 402 MPa along with no phase changes. Nevertheless, the density of shear lines on the surface was substantially influenced by variations in stress levels as demonstrated by high‐stress areas in Kernel average misorientation maps. At lower stress levels, the model analysis exhibited a higher degree of reliability, with R2 values of 96.25% at lower stress rather than 89.30% at higher stress.

Applying Binder Jetting Technology to 316L Stainless Steel Materials and Testing Its Mechanical and Dimensional Properties Depending on the Printing Method.

This article discusses special additive technologies, with a particular focus on the innovative binder jetting technology used to create three-dimensional objects. The theoretical part of this article defines the production process-its shortcomings and benefits. Also, the article describes process parameters and individual steps that must be optimally set for the desired result. Further, the article characterizes the most influential factors that are indispensable in the printing process-metallic powder, binder, printing parameters, and finishing operations after the printing itself. The conclusion of the theoretical part deals with various material possibilities when using binder jetting technology. In the practical part of the article, the properties of the material, the chemical composition, and the resulting accuracy of the printed samples will be verified experimentally. The information obtained will subsequently be used to identify an economically advantageous application of binder jetting technology.

Quaternary nitrate and chloride molten salts for the next concentrating solar power plants: Corrosion considerations for the use of AISI 304L steel

The study addresses the advancement in concentrating solar power (CSP) plants by transitioning from binary nitrate salts to nitrate and chloride quaternary salts, focusing on the corrosivity of four salts as evaluated by immersion testing of AISI 304L stainless steel. Four compositions of salts were evaluated, from solar salt to equimolar NaNO3‐KNO3‐NaCl‐KCl, up to 21 days (500 h) at 500 °C in an open atmosphere, determining the corrosion kinetics using gravimetry. The morphology, chemical composition, and microstructure of the corrosion products were characterized using XRD, FESEM-EDS, and GD-OES. Exposure to the molten salt with 0 mol% Cl‐ (Solar Salt) resulted in negligible corrosion kinetics, consistent with previous studies. The salt with 14 mol% Cl‐ caused a stable corrosion product with a corrosion rate 30 times higher than without chloride. All quaternary salts exhibited a multilayer structure of the top surface with selective chromium (Cr) removal. Specimens exposed to salts with more than 29 mol% Cl‐ displayed a similar structure with Cr and iron (Fe) removal, resulting in more brittle layers and corrosion rates 90 to 250 times higher than the salt without chloride. GD-OES analysis confirmed Cl diffusion into the oxide layer, highlighting the role of pCl2(g) and pO2(g) in driving corrosivity. Based on the results, using in salts with 29 mol% Cl‐ at 500 °C is discouraged, while using 304L with 14 mol% Cl‐ in a cold tank may be considered.

Accelerated corrosion tests of a contact pair austenitic stainless steel – titanium alloy

The results of accelerated corrosion tests of a pair of austenitic stainless steel — titanium alloy under conditions of elevated temperatures and high concentrations of corrosive agents are presented, conditions for the developing the mechanism of local corrosion being the same. The proposed approach included provoking the appearance of microstress concentrators by three-point loading of test samples with subsequent forced development of local types of corrosion through simultaneous exposure to an increased concentration of corrosive agents and temperatures at a fixed exposure in an autoclave. The results of modeling the corrosion process in a titanium alloy being in contact with stainless steel are assessed proceeding from the data obtained from metallographic studies of the state of surface layers of the metal and the morphology of corrosion products on prepared sections of the surface of templates cut from the samples under study. We considred a pair of steel 08Kh18N10T — titanium alloy VT-5. It is shown that the contact of samples under conditions of accelerated corrosion tests does not lead to a significant development of local types of corrosion of the titanium alloy. On the contrary, analysis of the state of the surface layers of the samples of stainless steel revealed the presence of deep corrosion pits, triggering the intergranular corrosion. In this case, corrosion of austenitic steel, both in the presence and in the absence of contact with titanium, proceeded in three stages: the initiation and fusion of pitting; formation of knife corrosion ulcers; propagation of intergranular cracks into the depth of steel. The proposed methodology of corrosion testing can be used to predict the possible nature of the development of metal corrosion under operating conditions that form local zones of static stresses in structural materials, and for prompt response to extend the design life of the equipment at thermal and nuclear power facilities.

Isothermal and thermomechanical fatigue crack growth behavior of 316LN stainless steel under load‐ and strain‐controlled modes

AbstractCrack growth tests of 316LN stainless steel under load‐controlled and strain‐controlled thermo‐mechanical fatigue (TMF) loadings at the temperature range of 350°C ~ 550°C were conducted. Moreover, the isothermal fatigue (IF) crack growth tests at the maximum temperature of 550°C were performed for comparison. The differences in IF and TMF crack propagation behavior under different loading conditions were thoroughly discussed, and the underlying mechanisms were revealed by a comprehensive characterization of the deformation state of the crack tip region using digital image correlation (DIC) and electron backscatter diffraction (EBSD) methods. Furthermore, it was found that the EBSD characterization was simply based on a single plane made it difficult to evaluate the complex crack growth behavior. In addition, the limitations of the classical parameter of stress intensity factor under TMF loading were presented.

A robust nano-indentation modeling method for ion-irradiated FCC single crystals using strain-gradient crystal plasticity theory and particle swarm optimization algorithm

Addressing the challenge of identifying an appropriate set of material and irradiation parameters for accurate simulation models using crystal plasticity finite element method (CPFEM), this study proposes a novel two-stage method for nano-indentation modeling of ion-irradiated face-centered cubic (FCC) materials. It includes implementing the strain-gradient crystal plasticity (SGCP) theory with irradiation effects and the calibration of simulation parameters using the particle swarm optimization (PSO) algorithm with experimental data. The proposed method consists of two stages: establishing CPFEM without irradiation effects in stage 1 and modeling irradiation effects based on CPFEM in stage 2. Modeling the nano-indentation test of ion-irradiated stainless steel 304 (SS304) using real experimental data is conducted to evaluate the efficiency of the proposed method. The accuracy of the calibration method using PSO is verified through comparisons between simulation and experimental results for force-indentation depth and hardness-indentation depth relationships under both unirradiated and irradiated conditions. Moreover, effect of ion-irradiation on the mechanical behavior during the nano-indentation of single crystal SS304 is also examined to demonstrate that the proposed method is a powerful approach for nano-indentation modeling of ion-irradiated FCC single crystals using SGCP theory and the PSO algorithm.

Metal Laser-Based Powder Bed Fusion Process Development Using Optical Tomography.

In this study, a set of 316 L stainless steel test specimens was additively manufactured by laser-based Powder Bed Fusion. The process parameters were varied for each specimen in terms of laser scan speed and laser power. The objective was to use a narrow band of parameters well inside the process window, demonstrating detailed parameter engineering for specialized additive manufacturing cases. The process variation was monitored using Optical Tomography to capture light emissions from the layer surfaces. Process emission values were stored in a statistical form. Micrographs were prepared and analyzed for defects using optical microscopy and image manipulation. The results of two data sources were compared to find correlations between lack of fusion, porosity, and layer-based energy emissions. A data comparison of Optical Tomography data and micrograph analyses shows that Optical Tomography can partially be used independently to develop new process parameters. The data show that the number of critical defects increases when the average Optical Tomography grey value passes a certain threshold. This finding can contribute to accelerating manufacturing parameter development and help meet the industrial need for agile component-specific parameter development.

Experimental and modeling study on liquid film thickness of horizontal gas-liquid annular flow using ultrasonic method

In horizontal annular flow, the high gas velocity leads to the strong randomness, rapidity and complexity of the structure and dynamic characteristics of gas-liquid interfacial wave. As one of the important parameters to describe the interfacial wave, the liquid film thickness is significant to study the hydrodynamic characteristics of the gas-liquid two-phase flow. In this paper, the ultrasonic echo resonance main frequency (UERMF) method and the ultrasonic reference signal elimination (URSE) method are compared by numerical simulation method. The minimum liquid film thickness measured by these two methods are 0.3 mm and 0.01 mm, respectively. Experiments are conducted on a stainless steel test section with an inner diameter of 50.0 mm to study annular air-water flow under five different pressure conditions (from 0.1 MPa to 0.7 MPa). The experimental data is processed using the URSE method to analyze the characteristics of the liquid film under different operating conditions. In addition, based on the aforementioned experimental dataset, a comparison is made regarding the performance of existing predictive correlations. It is found that the existing correlations have certain limitations in capturing the variations of liquid film thickness in annular flow, particularly at high pressures and for different pipe diameters. Therefore, a new liquid film thickness correlation suitable for different system pressures and pipe diameters is proposed. By expanding the range of fitted data points (220 data points), the 95 % relative error of improved model is within ±25 % error band for all available data (authors' and literature). The mean absolute percentage error (MAPE) is 11.08 %. The improved correlation has the applicability and extrapolation for different system pressure and pipe diameter conditions.

Efficacy of argon cold atmospheric pressure plasma jet on hospital surface decontamination and its impact on the surface property

The emergence of antimicrobial resistance has become a major contributor to healthcare-associated infections. Recently, the cold atmospheric pressure plasma jet (CAPJ) discharges have garnered attention of the researchers globally for their novel antimicrobial property. This research evaluated the effectiveness of an in-house developed CAPJ on the inactivation of multidrug-resistant (MDR) E. coli and S. aureus artificially inoculated over stainless steel and aluminium test surfaces. A greater than ∼5 log10 reduction of E. coli, whereas reduction of ∼3.4–4.6 log10 for S. aureus on the test surfaces was achieved on 180 s CAPJ exposure. Extremely low D- values (in the range of ∼27–63 s) were recorded for both isolates. In addition, this study assessed the impact of repeated CAPJ exposure on surface property, by replicating the process of hospital surface decontamination. Surface properties such as wettability, roughness, and elemental composition varied non-linearly on repetitive Ar CAPJ exposure on test surfaces. It was observed that the identified gas-phase species such as excited atoms (Ar I, and O I), positive ions (NO+, O2 +, OH+, O+, N2 +, Ar+, etc), negative ions (N2O2 −, N2O3 −, NO3 −, NO2 −, etc), radical RONS (OH•), and non-radical RONS (O I, NO+, OH+, N2O3 −, NO3 −, NO2 −, etc) would contribute to bacterial load reduction on the test surface along with any alteration in surface characteristic. There may be chemical and physical processes involved in the above activity. This investigation into understanding the effects of CAPJ surface decontamination on surface properties would aid in determining its potential applications in healthcare settings.

Research on the influence mechanism of titanium on corrosion resistance of ferritic stainless steel in aluminum solution

The immersion corrosion test of ferritic stainless steel with different titanium content was carried out in molten aluminum at 750 °C. Scanning electron microscope (SEM) and energy dispersive spectrometer (EDS) were used to observe the microstructure and basic morphology of ferritic stainless steel with different titanium content, and the corrosion cross-section morphology after corrosion was observed. The types and contents of carbides in steel were analyzed by electrolytic extraction device and X-ray diffractometer (XRD), and the influence mechanism of Ti element on the corrosion resistance of experimental steel was studied. The test results show that the corrosion resistance of the test steel is significantly improved by adding 0.8%Ti. At this time, the microstructure of the test steel is ferrite matrix and carbides dispersed in the grain, including TiC, TiN and Cr23C6. Carbide dispersed in the grain and Fe–Ti intermetallic compound formed at the intersection of two intermetallic compound layers (IMC1 and IMC2) during the corrosion of molten aluminum simultaneously hindered the diffusion of Al atoms and improved the corrosion resistance of the tested steel.

Detection of Anomalies in Additively Manufactured Metal Parts Using CNN and LSTM Networks

The process of metal additive manufacturing (AM) involves creating strong, complex components by using fine metal powders. Extensive use of AM methods is expected in near future for the production of small and medium-sized batches of end-use products and tools. The ability to detect loads and defects would enable AM components to be used in critical applications and improve their value. In this study, the Surface Response to Excitation (SuRE) method was used to investigate wave propagation characteristics and load detection on AM metallic specimens. With completely solid infills and the same geometry, three stainless steel test bars are produced: one conventionally and two additively. To investigate the effect of infills, four bars with the same geometries are 3D printed with triangular and gyroid infills with either 0.5 mm or 1 mm skin thickness. Two piezoelectric disks are attached to each end of the test specimens to excite the parts with guided waves from one end and monitor the dynamic response to excitation at the other end. The response to excitation was recorded when bars were in a relaxed condition and when compressive loads were applied at five levels in the middle of them. For converting time-domain signals into 2D time-frequency images, the Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were implemented. To distinguish the data based on fabrication characteristics and level of loading, two deep learning models (Long Short-term Memory algorithm (LSTM) and Convolutional Neural Networks (2D CNN)) were utilized. Time-frequency images were used to train 2D CNN, while raw signal data was used to train LSTM. It was found that both LSTM and 2D CNN could estimate solid parts' loading level with an accuracy of more than 90%. In parts with infills, CNN outperformed LSTM for the classification of over five classes (internal geometry and loading level simultaneously).

Prediction of characteristic points of single-peak flow curve through statistical technique and artificial neural network

ABSTRACT Artificial neural network (ANN) is an input–output modeling technique that has been successfully implemented in the field of material science to predict material behavior in terms of constitutive models. The material behavior considered here is the stress–strain behavior of the material at elevated temperatures that is characterized by a single-peak flow curve consisting of characteristic points viz. critical stress, peak stress and steady-state stress. The present work focuses on predicting these stress values along with the corresponding strain values through statistical regression analysis (SRA), ANN, and multi-layer complex neural network (MLCNN) with reasonable accuracy. The flow curves obtained from the axisymmetric compression test of 304LN austenitic stainless steel, performed at constant temperatures and constant strain rates, are used to train the models. The MLCNN has performed best while SRA has performed relatively worst with the current dataset due to rigorous and thorough computation. Both MLCNN and ANN are observed to perform better than SRA because of their non-parametric nature of handling data.

Predicting the creep-rupture lifetime of a cast austenitic stainless steel using Larson-Miller and Wilshire parametric approaches

An experimental dataset of just over 100 creep tests of a cast austenitic stainless steel, CF8C-Plus, was analyzed by two temperature-compensated parametric models (Larson-Miller, Wilshire et al.) to predict long-term lifetimes as functions of temperature and stress. The dataset and associated regression analyses showed greater scatter than typically found in recent similar studies of wrought Ni-based alloys by the same two models and was attributed to the microstructural inhomogeneity of the cast stainless steel. Qualitatively, the Larson-Miller formalism showed greater lifetime prediction accuracy than the Wilshire approach, with the latter model's predictive ability being particularly degraded by the presence of two very significant outlier results. This observation suggests that the Larson-Miller approach is more robust when treating rupture-time datasets that show particularly wide experimental scatter. Despite the differences in the overall predictive ability, both models yielded similar predictions of the applied stress at which CF8C-Plus would have a creep-limited lifetime of 100,000 h when loaded below the yield point.

A deep learning framework for defect prediction based on thermographic in-situ monitoring in laser powder bed fusion

AbstractThe prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers. Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. The model is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) µm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity.

Corrosion Behavior of Incoloy®800H, Hastelloy®G35® and 316L Stainless Steel in the Molten Eutectic Fluoride Mixture FLiNaK and Its Vapors

This paper discusses the findings of a corrosion experiment which investigates a transition area which is between the lower area and upper area of the sample. In this experiment, the lower half of the sample surface is exposed to molten salt and the upper half of the sample surface is exposed to the vapors generated by molten FLiNak salt. Incoloy®800H and Hastelloy®G35® alloys and 316L stainless steel were selected for the corrosion experiment on the basis of their Cr content. The experiment was conducted at 600 °C for a period of 100 h. The results of the experiment show that, in the transition area, no abrupt change in corrosion mechanism takes place; the experiments also give us information on how the degree of degradation varies across the three areas of the samples. The experiment also showed two distinct corrosion mechanisms operating in the test samples: intergranular corrosion in the SS316L stainless steel test sample and continuous corrosion in Hastelloy®G35®. The results also show a progressive reduction in the concentrations of F, K and Na in the upper areas compared to the lower areas for Hastelloy©G35®. Cr is shown to have a critical role in the corrosion process, even when the sample surface is not in direct contact with the molten salt and is only in contact with the vapors generated by the molten salt.

Damage analysis of concrete-filled square stainless steel columns based on acoustic emission and Markov chain methods

A test of concrete-filled square stainless steel (CFSST) columns with different eccentricities under compression was conducted to evaluate their failure and damage characteristics. Acoustic emission (AE) analysis was used to assess the damage process, and a damage prediction model based on the Markov chain was established. The monitoring results showed that the outward buckling was the dominant damage mode. As the eccentricity increased, the damage location changed from both sides of the column to the compressed side. The AE characteristic energy and b-value accurately described the damage process, which was divided into three stages: initial stability, cracking and buckling, and approaching failure. An analysis of the rise time/peak amplitude ratio (RA) and AE count/duration ratio (AF) indicated that tensile cracking was dominant in the three stages. A prediction model based on Markov chain was proposed to reflect state of CFSST columns, which could provide effective guidance in practical engineering.

Chilldown of cryogenic feed lines- An insight into the influence of feed line orientation and mass flux

Chilldown of cryogenic feed lines is the most indispensable part of any process that handles cryogenic fluids. Prediction of chilldown time ‘apriori’, requires a complete understanding of the influencing factors and the phenomenon involved. In the present study, the effect of feed line orientation and mass flux on chilldown performance is experimentally evaluated using liquid nitrogen. Experiments are carried out with a foam insulated stainless steel test section. Tests are performed for two different mass flux conditions with test section held in horizontal position as well as varying upward and downward inclinations. In tests with upward orientation, a significant jump in heat flux at top region is seen when the wall superheat reduces below a critical value and this is attributed to onset of liquid wetting at top region. In tests with downward inclination, a minor increase in the heat flux at top region is seen beyond 30° downward orientation attributable to the flow structure prevailing in the test section. It is noted that heat flux pattern at bottom region is not influenced much by test section orientation. Leidenfrost temperature, minimum wall heat flux, and critical heat flux values obtained from the present study are compared with the predictions made using correlations available in published literature. The findings of the present study would help to understand better the influence of feed line orientation and mass flux on wall heat flux pattern and thereby improve the prediction capability.

Shearographic Detection of Internal Defects in Austenitic Stainless Steels

Despite the formation of a passive layer, corrosion also occurs in austenitic stainless steels. One of the most common and most dangerous types is pitting corrosion, in which the material beneath the surface is completely dissolved. Since this type of corrosion only produces small holes on the surface, it is difficult to detect without nondestructive testing. This paper presents induction-excited shearography as an optical, nondestructive testing method for quick inspection of inner defects and corrosion. The investigations were carried out on test specimens with blind holes made of austenitic stainless steel 1.4301 (AISI 304). The detection limits of different defect sizes were determined objectively based on the signal-to-noise ratio (SNR). The results show that inner defects with a size of Ø 2 mm can be detected via SNR in a depth of up to 1.5 mm. Larger defects can be detected in greater depth. The data obtained were validated on real test specimens. The measurement of a more realistic defect geometry showed higher SNR values compared to the idealized test specimens. From the measured SNR values of realistic defect geometry, it can be concluded that real irregularities have higher SNR values than defects with flat bottoms. The results show that induction-excited shearography is suitable for the nondestructive testing of austenitic stainless steels.

Two-steps electrochemical polishing of laser powder bed fusion 316l stainless steel

Laser Powder Bed Fusion fabricated 316L stainless steel test components were electrochemically polished in a non-aqueous electrolytic solution consisting of 1M sodium chloride, ethylene glycol, and 10% ethanol, and in an aqueous commercial electrolyte A2. The influence of the high current densities ranging between 250 and 2000 mA/cm2 on the surface roughness (Psa, Wsa and Ssa), materials removal weight and thickness reduction with various morphological characteristics were investigated. It is confirmed that polishing at the tranpassive region was feasible in non-aqueous electrolytes where little pitting occurred. A two-step electrochemical process was proposed based on the characterisations to enhance the polishing effect, which consisted of two processes with different electrolytes and current densities. The experimental results indicated that the surface roughness of two-step polished steels with 1500 and 250 mA/cm2 current densities was reduced by 11.25% than the optimum result of the one-step EP with the non-aqueous electrolyte solution. The weight and thickness reduction were reduced by 3.39% and 9.02%, respectively, more than the optimum results of the one-step EP with the aqueous commercial electrolyte.

A Comparative Study on Representativeness and Stochastic Efficacy of Miniature Tensile Specimen Testing

Abstract In this article, a miniature dog bone tensile coupon design was tested against the existing ASTM standard specimen design. Specimens were prepared from commercially sourced austenitic stainless steel 304 alloy, and a defect-ridden additively manufactured 304L alloy was studied. By utilizing a tensile specimen design that is 1/230th volume of the smallest ASTM E8-04(2016), Standard Test Methods for Tension Testing of Metallic Materials, dog bone specimen, coupled to a digital image correlation (DIC) setup, case studies were performed to compare tensile property measurements and strain field evolution. Whereas yield strength measurements were observed to be similar, post-yield, the ultimate strength measurements and ductility measurements from the miniature specimens were observed to be higher than the ASTM specimen design. Although the strength measurements were comparable, the strain evolution was found to differ in the miniature specimens. Studies to assess effects of varying thickness and defect population were also pursued on the miniature tensile specimen. From the DIC strain field estimations, the peak local strain values at ultimate tensile strength were observed to be increasing with reducing specimen thickness. Testing of defect ridden stainless steel revealed the sensitivity to failure through strain localization and the influence of defect size was captured in the strength measurements.