Evaluation Of Residual Stresses Research Articles

Residual Stress Evaluation in L-PBF-Produced SS 316L Specimens.

The identification of residual stresses (RS) in components made by selective laser melting (SLM) is necessary for subsequent technological optimization. The presented research is devoted to evaluating the influence of the combination of laser power (P), scanning velocity (v) and the rarely considered number of layers (nL) on surface residual stresses in SLM stainless steel SS 316L. Experimental parameters were set based on the Design of Experiment (DoE) method, with follow-up X-ray diffraction (XRD) measurements and data processing using analysis of variance (ANOVA) and regression analysis. The obtained data are a valuable stepping-stone for the subsequent design of research focused on the application of sustainable eco-friendly Abrasive Water Jet (AWJ) peening for RS modification in the evaluated material.

Multiscale residual stress and mechanical behavior analysis in machining of Ti-6Al-4V alloy with advanced microscopic characterization

Machining of polycrystalline metal materials results in metallurgical modifications in the machined surface, and it can introduce multiscale residual stresses (macroscale and microscale residual stress), which may be detrimental to the mechanical properties thereby interfering with the service performance of machined parts. However, most of the current studies only focus on the macroscale residual stress, the microscale residual stresses have always been neglected since the limitations of the commonly used techniques for residual stress measurement in machined surfaces. In this study, the recently developed Focused Ion Beam-digital image correlation (FIB-DIC) ring core method was employed to evaluate the time-resolved strain relaxation and multiscale residual stress of the machined surface under different cutting conditions. The quantitative evaluation of residual stress and microstructural effects on hardness of the machined surface was conducted by combining with FIB incremental milling and nanoindentation technique. It has been observed that although the global tensile strain relaxation was captured in the ring-core region, local microscale compressive strain relaxation also occurred. The hardness of the machined surface was determined by the combined effects of microstructure and residual stress induced by machining. Specifically, the grain refinement and compressive residual stress contributed to hardening, while tensile residual stress resulted in softening of the machined surface. Furthermore, it was also observed the hardening/softening effects were enhanced with increasing intensity of tension and compression.

Microhardness Profile and Residual Stresses Evaluation in a Shot Peened SAE 5160H Steel

Shot peening (SP) is a surface cold hardening process used on metals to enhance life under cyclic stress. In this case, SP was applied to SAE5160H samples of steel quenched tempered in oil at 460 °C used for leaf springs. This study shows the residual surface stresses measured through X-ray diffraction (XRD) and the microhardness variation through the perpendicular-to-the-peened surface using a combination of metallographic preparation and Vickers microhardness (HVN). This combination of techniques makes possible measuring the SP effect in perpendicular-to-the-treated surface. A residual stress of -365.8 ± 78 MPa measured by XRD and a maximum microhardness of 525± at 92.7 HVN on the surface were obtained for the SP material. Alternatively, an average of 54.2 ± 54.3 MPa residual stress measured by XRD and 433 ± 39.5 HVN were obtained for the As-it-is samples. In addition, corrosion electrochemical potential tests showed that SP increases the corrosion potential, which makes this process undesirable if the SP component is exposed to aggressive environments. Moreover, the As-it-is samples presented not statistically significant HVN difference in the measured points. The combination of experimental techniques allows estimating hardness change in perpendicular-to-the treated surface separated by as little as 10 µm but with a simpler specimen preparation than other techniques such as XRD or strain gauges. Such a combination can be an alternative for estimating residual stresses through depth.

Evaluation of Residual Stresses and Distortion of Ti-5Al-2.5Sn and SS 304 Gas Tungsten Arc Welded Joints under Hybrid Thermal Tensioning Technique: Transient Thermal Tensioning and Trailing Intensive Cooling

Evaluation of Residual Stresses and Distortion of Ti-5Al-2.5Sn and SS 304 Gas Tungsten Arc Welded Joints under Hybrid Thermal Tensioning Technique: Transient Thermal Tensioning and Trailing Intensive Cooling

Quantitative evaluation of residual stress and microstructural effects on the surface hardness of machined Ti-6Al-4 V alloy with microscopic characterization techniques

The surface hardness of machined Ti-6Al-4 V components has a significant influence on the service performance and fatigue life. It’s acknowledged that the hardness value of machined surface is dependent on microstructure and residual stress, however, the quantification of their influence levels on surface hardness is still an unresolved issue, which limits the potential for further enhancing the mechanical property of machined parts. Furthermore, it’s difficult for current studies to fully reveal the mechanism of hardness fluctuation due to release of residual stress. To address these gaps, this study presents a novel method for quantitatively evaluating residual stress and microstructural effects on the machined surface hardness using microscopic characterization techniques. The microstructures of machined Ti-6Al-4 V workpiece were characterized by electron backscatter diffraction (EBSD), and the microstructural hardening effects were analyzed. The residual stresses release and evaluation were performed by the Focused Ion Beam-Digital Image Correlation (FIB-DIC) ring core method, then the surface hardening originated from residual stress and microstructural effects can be assessed by nano-indentation measurements. The results show that the surface hardness increase induced by the combined effects of compressive residual stress and microstructure, where the microstructural hardening can be strengthening by obtaining smaller grain size and high proportion low grain boundary, and the higher magnitudes of compressive residual stress can also enhance the hardness increase. The surface softening induced by compressive residual stress relaxation with no obvious microstructural alterations was observed and the maximum softening reached 14.54% in this study. The findings are beneficial for understanding the mechanical property strengthening mechanism of the machined parts.

Evaluation of plastic properties and equi-biaxial residual stress via indentation and ANN

This study presents a method for evaluating plastic properties and equi-biaxial residual stress (RS) simultaneously using the images of generated imprints in indentation tests and an artificial neural network (ANN). The ANN model is trained with data from finite element analysis (FEA) for establishing the relationship between material properties and the radial (ur) and vertical (uz) displacements resulting from indentation tests. To deal with the inherent noise in experimental data, an artificial random noise was added to the FEA data used for training the ANN model. By inputting parameters with artificial errors into the ANN model, the robustness of predicted values against potential testing errors was examined. Also, the accuracy of the predicted residual stresses and material properties from ANN are validated using tensile tests and indentation tests on the stress-induced specimens.

Residual Stresses in a Wire and Arc-Directed Energy-Deposited Al–6Cu–Mn (ER2319) Alloy Determined by Energy-Dispersive High-Energy X-ray Diffraction

In order to enable and promote the adoption of novel material processing technologies, a comprehensive understanding of the residual stresses present in structural components is required. The intrinsically high energy input and complex thermal cycle during arc-based additive manufacturing typically translate into non-negligible residual stresses. This study focuses on the quantitative evaluation of residual stresses in an Al–6Cu–Mn alloy fabricated by wire and arc-directed energy deposition. Thin, single-track aluminum specimens that differ in their respective height are investigated by means of energy-dispersive high-energy X-ray diffraction. The aim is to assess the build-up of stresses upon consecutive layer deposition. Stresses are evaluated along the specimen build direction as well as with respect to the lateral position within the component. The residual stress evolution suggests that the most critical region of the specimen is close to the substrate, where high tensile stresses close to the material’s yield strength prevail. The presence of these stresses is due to the most pronounced thermal gradients and mechanical constraints in this region.

Experimental and numerical investigation of a gouging heat source model and gouging/welding residual stress and deformation

Gouging is generally used for groove preparation of multi-pass welding and repair welding. Arc gouging is one of the most efficient gouging methods which utilizes an arc heat source and a trailing air blower. To date, the heat effects from the arc gouging are not clear especially when it is coupled with subsequent welding. In this study, a new heat source model considering the blown molten metal as well as the increased heat transfer by the blowing air was proposed. A 3D finite element model for gouging and welding was employed to analyze the thermal behavior, welding distortion and residual stresses. The transient temperature predicted by the proposed heat source model has good agreement with thermocouple measurements, both featuring multiple peaks in the thermal profile. The predicted distortion and residual stress due to gouging show similar trends as those in experiment. The proposed analysis approach can provide necessary insights, with high accuracy, in the evaluation of welding distortion and residual stresses in practical manufacturing.

Rapid evaluation and prediction of cure-induced residual stress of composites based on cGAN deep learning model

In this piece of present work, we propose a deep learning model driven by conditional generative adversarial network (cGAN) for rapid evaluation and prediction of cure-induced residual stress (CRS) of composites. The CRS is evaluated by solving for the material behaviors of the cure kinetics, viscoelasticity, thermal expansion, and cure shrinkage under heat transfer condition using finite element (FE) method. The geometry and CRS fields are extracted from numerous simulations and subsequently utilized in the proposed cGAN training process. With this end-to-end unsupervised learning, the cGAN model can predict the CRS with high fidelity based on the fiber random distributed microstructure and capture the variation of fiber volume fraction. In qualitative measures, peak signal to noise ratio (PSNR) and structure similarity index (SSIM) are employed for accuracy verification. Moreover, the cGAN model can also significantly reduce the computational cost and provide some insights for optimizing the manufacturing process of composites.

Non-equibiaxial residual stress evaluation methodology using simulated indentation behavior and machine learning

Measuring the residual stress in the components in nuclear power plants is crucial to their safety evaluation. The instrumented indentation technique is a minimally invasive approach that can be conveniently used to determine the residual stress in structural materials in service. Because the indentation behavior of a structure with residual stresses is closely related to the elastic-plastic behavior of the indented material, an accurate understanding of the elastic-plastic behavior of the material is essential for evaluation of the residual stresses in the structures. However, due to the analytical problems associated with solving the elastic-plastic behavior, empirical equations with limited applicability have been used. In the present study, the impact of the non-equibiaxial residual stress state on indentation behavior was investigated using finite element analysis. In addition, a new nonequibiaxial residual-stress prediction methodology is proposed using a convolutional neural network, and the performance was validated. A more accurate residual-stress measurement will be possible by applying the proposed residual-stress prediction methodology in the future.

Residual Stresses in Surgical Growing Rods.

The treatment of early-onset scoliosis using surgical growing rods suffers from high failure rate. Fatigue resistance can be improved by inducing compressive residual stresses within the near surface region. An in-depth investigation of the residual stresses profile evolution is performed through the sequence of material processing steps followed by surgeons handling operations, in connection to material properties. The final goal is to guide further improvements of growing rod lifetime. Residual stress evaluation was carried out on Ti6Al4V rods using digital image correlation applied to micro-beam ring-core milling by focused ion beam. This provided experimental stress profiles in shot-peened rods before and after bending and demonstrated that compressive residual stresses are maintained at both concave and convex rod sides. A finite element model using different core and skin conditions was validated by comparison to experiments. The combination of an initial shot peening profile associated to a significant level of backstress was found to primarily control the generation of compressive stresses at the rod surface after bending. Guidelines to promote larger compressive stresses at the surface were formulated based on a parametric analysis. The analysis revealed the first order impact of the initial yield strength, kinematic hardening parameters and intensity of the shot peening operation, while the bending angle and the depth of shot peening stresses were found to be of minor importance. Materials exhibiting large kinematic hardening and low yield strength should be selected in order to induce compressive residual stresses at key fatigue initiation location.

7075 aluminum alloy induced by ultrasonic surface rolling: grain refinement and residual stress model analysis and evaluation

7075 aluminum alloy induced by ultrasonic surface rolling: grain refinement and residual stress model analysis and evaluation

Residual Stress Evaluation on Surface Grinding of Maraging 350 Steel with Conventional Sol-Gel Grinding Wheel

Residual Stress Evaluation on Surface Grinding of Maraging 350 Steel with Conventional Sol-Gel Grinding Wheel

Design Optimization of Components for Additive Manufacturing-Repair: An Exploration of Artificial Neural Network Requirements and Application

The integration of artificial intelligence (AI) in additive manufacturing (AM) technology is currently a promising and leading area of research for component repair and restoration. The Issues of high cost and time consumption for AM repair have been a subject of discussion among researchers in this field of study. Moreover, the potential challenges in dealing with complex components for repair and restoration in the (AM) domain require the establishment of a critical technical platform based on hybrid (AI). At this point, the proposed optimization method must cover all important parameters for the complex configuration of structural components under restoration. For the purpose of this study, a design optimization framework was developed using a MATLAB-SIMULINK mathematical model for AM solution purposes by improving the functionality and integration of monitoring. This improvement is based on facilitating the real-time identification of failures with accuracy and giving a clear monitoring vision according to the intended targets like geometric distortions, residual stresses evaluation, and defect characterization. The improvement involves overcoming a number of challenges such as the pre-fabrication stage by expanding the data repository besides offering a theoretical set of algorithmic with some options that improve the current procedure. Also, this study will conclude and suggest a further framework and new knowledge for restoration and product life cycle extension. This developed ANN can be used at the real pace of modeling the MATLAB-Simulink system and merged with another suitable algorithm to form a hybrid ANN. This model development using a neural network has attained a good manipulation of AM. The predicted data from ANN model that was determined and achieved in this study can be used to facilitate and enhance any further study as base knowledge in merging the ANN with another AI to form a hybrid algorithm.

Evaluation of Flexural Toughness of Concrete Reinforced with High-Performance Steel Fiber.

In this study, a flexural test and residual stress evaluation using the aspect ratio (65 and 80) and steel fiber content (20, 30, and 40 kg/m3) as variables were conducted according to the EN 14651 standard to investigate the flexural toughness of concrete reinforced with high-performance arched steel fibers. The result of the flexural test show that the residual stress was 114.5% higher in the test specimen with high curvature and high content of arched steel fibers than that in the other conditions. In addition, the energy absorption capacity of arched steel fiber-reinforced concrete increased by 138.88% compared to concrete.

Evaluation of residual stresses in Cu/Mo, Cu/Nb nanomultilayers with a strong fiber texture

Residual stresses are one of the key factors for tuning the properties, microstructure, and reliability of thin films and nanomultilayers, but their measurement and evaluation are challenging. Residual stresses in nanomultilayers with {110} out-of-plane texture exhibit a dependence on the in-plane crystal orientation, which complicates their evaluation using X-ray diffraction. The texture and residual stresses were investigated for two representative nanomultilayers of immiscible materials with fcc/bcc structure: Cu/Mo and Cu/Nb grown on Si substrate with an amorphous silicon nitride layer. Both multilayered structures exhibited Cu {111} // Mo, respectively Nb {110} out-of-plane fiber texture, and showed compressive stress. A modified crystallite group method for {110}, {111} fiber texture was used to determine residual stresses in the nanomultilayers. The method was proven to be a good tool to extract the residual stress in nanomultilayers with a strong fiber texture.

A Discrete-Finite Element Analysis Model Based on Almen Intensity Test for Evaluation of Real Shot Peening Residual Stress.

In this study, a combined discrete-finite element model based on the Almen intensity measurement test was proposed to evaluate the real shot peening residual stress. The discrete element analysis was utilized to simulate the random behavior of numerous shot balls, while the finite element analysis was employed to quantitatively predict the residual stress induced by shot peening. Moreover, the Almen intensity, an essential factor in the actual shot peening process, was taken into account. Initially, an Almen strip analysis model was established, and the multi-random impact analysis was performed to validate the good agreement between the analytical Almen curve and experimental Almen curve. Subsequently, the unit cell discrete-finite element analysis model was expanded for predicting the peening residual stress, incorporating the Almen intensity. The analysis results showed a significant correlation between the predicted peening residual stress and the XRD (X-ray diffraction) experimental residual stress. Therefore, it was confirmed that the proposed discrete-finite element random impact analysis model in this study could serve as an effective analytical technique capable of substituting for the actual shot peening process.

Nondestructive evaluation of residual stresses in welded joints on the base of a combination of ultrasonic testing and speckle-interferometry

Nondestructive evaluation of residual stresses in welded joints on the base of a combination of ultrasonic testing and speckle-interferometry

Evaluation of Residual Stress in MEMS Micromirror Die Surface Mounting Process and Shock Destructive Reliability Test

Light Detection and Ranging (LiDAR) devices based on Micro-Electro-Mechanical Systems (MEMS) micromirrors have been promising sensors for automatic driving owing to the advantages of MEMS devices. The packaging plays a vital role in the reliability of MEMS micromirror but introduces the residual stress from the surface mounting process. The shock reliability of micromirrors is influenced by residual stress. In this work, the residual stress from the surface mounting process are investigated through resonant frequency tests. The frequency drift can be 718 Hz and -281 Hz under the residual stress of 107.0 MPa and -25.0 MPa respectively. As the difference of CTE rises, the residual stress proportionally increases but decreases with the thickness of the substrate. In addition, a higher curing temperature can introduce a more significant stress level. The dynamic response in shock environment under different residual stress are analyzed. Drop fall experiments verify the theoretical conclusions that the stiffness hardening effect can improve the shock level tolerance. The tensile stress levels of 41.7 MPa and 107.0 MPa are corresponding to 100 g and 200 g respectively. The influences of residual stress on frequency drift and reliability need to be considered seriously and simultaneously during the packaging design for MEMS micromirrors. This work provides a deep insight into commercial applications of MEMS-based LiDAR in autonomous driving.

Residual Stress Analysis of Additive Manufactured A356.2 Aluminum Alloys using X-Ray Diffraction Methods

The physical properties of metal-based structural materials, such as hardness, strength and toughness, are directly or indirectly affected by residual stress inside or on the surface of the given part. Repeated rapid heating and cooling during the additive manufacturing process causes thermal gradients and expansion and contraction in the material, which causes residual stress. Tensile residual stresses are known to exist on the surface of additive manufactured products and should be kept to a minimum as they affect the mechanical properties and lead to product deformation and product failure. Therefore, it is important to evaluate the residual stress after making the product and to control it under the desired conditions. There are limitations to using the destructive method commonly used for residual stress evaluation with additive manufacturing products, due to difficulties in repeated measurements, product size, and cost issues. Therefore, it is necessary to apply a non-destructive evaluation method and verify the validity of the method. In this study, A356.2 aluminum alloy powders were used for additive manufacturing using the powder bed fusion process, and the surface residual stress generated during the process was measured. X-ray diffraction (XRD) methods were used to observe the surface residual stress. After XRD measurement, analyses were performed using the Williamson-Hall plot, sin2ψ, and cosα methods. The residual stress measurement results of samples manufactured through the LPBF process and the characteristics and limitations of each method were discussed.