Prediction Of Residual Stresses Research Articles

A computationally efficient thermo-mechanical model with temporal acceleration for prediction of residual stresses and deformations in WAAM

ABSTRACT The present paper introduces a novel temporal acceleration strategy for computationally efficient prediction of residual stresses and deformations in wire arc additive manufacturing (WAAM) components. It employs a semi-analytical approach, dividing temperature into the analytical and the complementary fields. The analytical field is obtained by a closed-form solution, and the complementary field is employed to solve the boundary conditions. A temporal acceleration factor for the heating period is applied. Meanwhile, the diffusion time in the analytical field is manipulated to guarantee accurate temperature prediction. Validation via WAAM experiments, including both a thin-wall structure and a practical engineering component with characteristic dimensions on the order of metres, indicates that the predicted stresses is 50 MPa lower than the experimental values. Moreover, the discrepancy of between the predicted deformations and the experimental measurements is less than 10%, demonstrating reasonable accuracy can be achieved with attractive computational efficiency.

Deep learning-based prediction of thermal residual stress and melt pool characteristics in laser-irradiated carbon steel

Residual stress is a crucial factor that must be managed in laser manufacturing processes. Neglecting it can result in product failure or unsuitability for the intended use. In this article, we present the first surrogate deep learning model based on generative adversarial network that predicts thermal residual stress and melt pool characteristics on the cross-section of laser-irradiated carbon steel. The model was trained using simulation data obtained from the unified momentum equation approach-based numerical model. Our model was designed to predict various aspects of the laser melting process only from three scalar inputs of laser power, beam diameter, and irradiation time at two different points in time (immediately after the laser heating is finished and when the steel is completely cooled down) and successfully predicted the distributions of 12 variables, including residual stress and temperature. Prediction results were not only qualitatively very similar to their ground truth, but also quantitatively accurate with the R2 values ranging from 0.975 to 0.999 and the mean absolute errors between 0.0015 and 0.0126. We demonstrated that complex experiments can be quickly performed using the surrogate model because a single deep learning prediction took only 0.13 s, compared to the 2.5–8 h numerical simulation times.

Hybrid modeling prediction of residual stresses in turned Ti6Al4V considering frictional contact

Residual stress plays a pivotal role in assessing the surface quality of machined components, influencing characteristics such as fatigue performance, corrosion resistance, and mechanical strength. Ti6Al4V, a critical material in the nuclear sector, the generation of residual stresses during machining has attracted significant attention. A model for variable friction coefficient model is proposed to accurately characterize the complex plastic deformation behavior of the material. This model takes into account variables such as the relative sliding velocity, contact pressure, and temperature. A subroutine has been developed using the VFRIC interface to enhance precise cutting simulation and modeling. Using the friction model, a new hybrid prediction model for surface residual stresses is developed by integrating finite element analysis and analytical methods. The reliability of both the hybrid and friction models is confirmed through the evaluation of cutting forces, chip morphology, and residual stress. The findings suggest that the novel friction model produces simulation results with higher accuracy compared to traditional Coulomb friction models. The variations in cutting force amount to 5.8% and 13.7%, the error in chip serration is 12.4%, and the deviation in predicting the maximum residual compressive stress is 10.3%. This study contributes to the advancement of friction models and methodologies used in predicting residual stress.

Gradient residual stress and fatigue life prediction of induction hardened carbon steel S38C axles: Experiment and simulation

Gradient distribution of triaxial residual stresses to a depth of several millimeters is retained in middle carbon steel S38C axles after high-frequency induction hardening, which has become a critical concern for fatigue structural integrity. To address this, the axial, hoop, and radial gradient residual strains inside the axles were measured for the first time by advanced neutron diffraction. The SIGINI Fortran subroutine was then adopted to reconstruct the global initial residual stress field from the measured data. Experimental and simulation results show that residual stresses of about −520 MPa (axial), −710 MPa (hoop), and −40 MPa (radial) residual stress were retained below the axle surface. Subsequently, the fatigue crack propagation behavior of S38C axles was numerically investigated in the framework of fracture mechanics. The calculated results clearly show that the compressive residual stresses at a depth of 0–3 mm from the axle surface lead to a low crack growth driving force, and that fatigue cracks do not propagate as long as the crack depth is less than 3.7 mm for hollow S38C axles. These results further indicate that the maximum defect size allowed in routine inspections is acceptable from a safety and economic point of view. Accurate measurement and characterization of the global gradient residual stress field through experiments and simulations can provide an important reference for optimizing the mileage intervals of nondestructive testing (NDT) of surface defects in these surface-strengthened railway axles.

Artificial neural network and ANFIS approaches for mechanical properties prediction and optimization of a turbine blade treated by laser shock peening

This research article covers a comprehensive investigation on the prediction and optimization of residual stresses, plastic deformations and damage induced by laser shock peening (LSP) on a thin Ti-6Al-4 V leading edge sample of a turbine blade. The study is based on a two-part approach: the first part involves a numerical simulation of the LSP process and the characterization of its effects on the material. The finite element analysis (FEA) is used to simulate the complex interactions between laser parameters, material properties and mechanical response. This numerical analysis generates valuable insights into the residual stress distribution, the plastic deformation and the potential damage within the sample being treated. Based on the numerical results, the second part highlights the novel application of the artificial neural network (ANN) and the adaptive neuro-fuzzy inference system (ANFIS) methods. By taking advantage of the strength of ANN and ANFIS, the models are able to learn accurately from all the data generated by numerical simulations, allowing them to precisely predict and optimize the effects induced by LSP: residual stresses, plastic strains and damage. This paper provides a significant contribution to the field of materials science and engineering, by offering an in-depth comprehension of the LSP process impact on Ti-6Al-4 V leading-edge turbine blade specimens. The combined utilization of numerical analysis and artificial intelligence AI-driven techniques offers a comprehensive approach to optimize the LSP process, leading to the fatigue resistance improvement and service life extension of turbine blades and other critical aerospace components. The proven capability of ANN and ANFIS enables innovative solutions in aerospace engineering and surface treatment technologies, promoting the AI implementation in materials processing and design.

Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review.

In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions in predicting residual stresses in laser additive manufacturing.

The prediction of residual stress of welding process based on deep neural network

The welding process has been an efficient method for producing essential and complex manufacturing parts in various industrial design fields. The post-weld residual stress can have detrimental effects on welded components. Therefore, systematic studies of residual stress are essential for evaluating welding behaviors and mechanisms in welded structures. They can provide a valuable reference and optimization for addressing residual stress relief. Numerical finite element analyses based on thermal-mechanical models offer a comprehensive approach to simulate real welding, providing a reliable means to determine and quantify the distribution of residual stress based on welding parameters and material properties. Furthermore, the finite element analysis is capable of generating adequate and dependable datasets in relation to the classical experiment. However, the finite element simulation is not considered an efficient method for predicting the magnitude and distortion of residual stress due to its high computational cost. A deep learning framework with powerful automatic learning abilities could potentially be used as an alternative method to efficiently predict residual stress. The purpose of the current study is to propose an innovative modeling approach for accurately and effectively predicting residual stress. A deep network model with Convolutional Neural Network using Adam optimization is integrated with numerical finite element analyses of a single-pass beam weld in SUS304 stainless steel. Finite element analysis is used to generate extensive residual stress datasets, which are partly used to train the deep network model and partly used for model validation. The deep network model aligns closely with the finite element analysis results, with a root-mean-square error (RMSE) of less than 12, an absolute fraction of variation (R2) of greater than 0.95, a mean absolute error (MAE) of less than 6.8 and a mean absolute percentage error (MAPE) of less than 1.1. Furthermore, this study highlights the potential advantage of using a deep network model with strong memory capabilities to directly predict residual stress for identical structural components and welding processes.

Hardening of Cylindrical Bars with Water Impinging Jet Quenching Technique

Hardening of carbon steel products by austenitization and immersion in a quenching medium is a widely used heat treatment to obtain a hard and strong martensitic structure. To avoid the undesired consequences, such as residual stresses or insufficient hardening depth, the cooling rates must be accurately measured and controlled. This can be achieved using the impinging water jet quenching technique. The aim of this work is to perform hardening of four low‐alloyed 70 mm cylindrical carbon steel bars, using impinging water jet quenching technique with different jet flow rates, and to analyze its effect on thermal evolution and residual stresses. The temperature evolution during quenching experiments is recorded and used as input to a comprehensive quenching model to predict phase transformations, final hardness, and residual stresses of cylindrical bars. All four quenching experiments result in a fully hardened martensitic state. Furthermore, a decrease in jets’ flow rate, within a certain interval, results in different thermal histories and in lower compressive residual stresses on the surface. The results from quenching simulations show promising hardness, microstructure, and residual stress predictions that are validated by hardness measurements, optical microscopy, and residual stress analysis using X‐Ray diffraction method.

Residual Stress Prediction during TIG and MIG Welding Process of Steel - A Review

In this study, a forensic review of residual stress predictions and its impact on welded joints especially the steel pipes during the Tungsten inert gas welding and metal inert gas welding processes were reviewed. It was established that in the case of MIG welding, the effect of residual stress can be reduced after treatment. In the case of TIG welding process, increase in current will lead to deformation and residual stresses. Estimating the residual stress is a challenging task. It is common to predict residual stress using a notch stress intensity factor, a numerical simulation approach coupled with a thermal-mechanical finite element model. Thus, adequate design in welding parameters will help in the optimization of the process to achieve a safe and reliable welded joint during TIG and MIG welding process.

Machining-induced residual stress and deformation during Mg–Li alloy thin-walled rib-web parts milling

The deformation of thin-walled parts based on the lightweight Mg–Li alloy materials caused by machining-induced stress is an important factor hindering the further development of lightweight in the aerospace field. Prediction of the machining-induced residual stress is the key to understand the deformation and derive low-stress machining scheme. In this study, the equivalent cutting deformation loads were applied to substitute the strongly coupled thermal-mechanical behavior between the tool and part to realize the prediction of the machining-induced stress and deformation of thin-walled rib-web part under different milling conditions. The local regular and complex rib-web parts share a same deformation mode, both behave as in-plane bending deformation of the machined surface. The low-stress milling scheme of up milling combined with cryogenic cooling obtained based on the local regular four rid-web thin-walled part was applicable to the prediction of the complex Mg-Li alloy disk rib-web part. Compared with the initial scheme, the maximum stress and deformation displacement based on the low-stress scheme were reduced from 80 to 36 MPa and from 0.04060 to 0.00814 mm, respectively. Finally, the effectiveness of low-stress milling scheme were verified based on the milling and measurement experiments of the complex thin-walled disk rib-web part. These results indicate the effectiveness of the modeling method for thin-walled rib-web parts and prove the results obtained based on the local model can be extended and applied to large complex parts with the same structural type.

Residual Stress and Distortion Prediction for Laser Directed Energy Deposition Based on Cyclic Heat Transfer Model

Residual Stress and Distortion Prediction for Laser Directed Energy Deposition Based on Cyclic Heat Transfer Model

Prediction of Residual Stresses in 316 Stainless Steel Pipes Welded Joint

Due to the extremely complicated thermal cycle for the welding process, the fusion zone and heat-affected zone (HAZ) produce irreversible elastic-plastic deformation and residual stresses. The differential heating of the pipes caused by the weld heat source causes residual stress as a result of the welding process. However, the strength and lifetime of the component are also decreased as a result of residual stresses in and around the weld zone. The objective of this research is to analyze the residual stresses created during the welding process and select the best welding parameters that give the lowest residual stresses in 316SS pipes with 50 mm diameter and 4 mm thickness that were manually welded by used (316) welding wire and using shielded metal arc welding (SMAW) in a single-pass butt joint with the various values for each of current (58, 68, 78, 88) amperes and voltage (22, 23, 24, 25, 26) volts. The shielded metal arc welding process involves heating, melting, and solidifying the parent metals and filler material in a localized fusion zone by a transient heat source to create a junction between the parent metals. The welding process free from preheating and heat treatment will be obtained. ANSYS Finite Element methods are used to calculate the welding residual stress distribution. The mechanical and thermal models were used to carry out the theoretical analysis. In general, the numerical study found that the residual stress distribution at the weld zone’s center is continuous, rising, and has a value of about (1738 MPa). Additionally, the residual stress at the boundary between the heat-affected zone and the weld zone climbs to a maximum value of around (3799.6 MPa). On the other hand, the magnitude of the residual stress in the heat-affected zone of the weld reduces significantly and achieves a minimum value at a position of (20 mm) with a value near zero.

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.

A novel genetic-based residual stress and deformation prediction method for the coupled machining process of connecting rod

A novel genetic-based residual stress and deformation prediction method for the coupled machining process of connecting rod

Process-induced residual stress in a single carbon fiber semicrystalline polypropylene thin film

During the processing of semicrystalline thermoplastic composites, various models for the prediction of residual stress have been proposed but experimental validation is limited. In this study, a modeling framework is developed to predict the evolution of residual stress in a single carbon fiber embedded in a semicrystalline polypropylene thin film subjected to non-isothermal cooling. Validation is based on in-situ measurement of axial fiber strain after processing using micro-Raman Spectroscopy. A material model for polypropylene (50% equilibrium crystallinity) is presented that incorporates the effects of non-isothermal cooling on crystallinity-dependent resin shrinkage and crystallinity-dependent resin modulus from the amorphous polymer melt to room temperature. The evolution of residual stress is calculated using a finite element (FE) model with a user-developed subroutine incorporating the resin material models. Single-fiber polypropylene films are fabricated using a range of fiber pretension levels to prevent fiber waviness during cooling and to induce a wide range of axial strain levels in the fiber for model validation. The fiber axial strain was measured over the length of the fiber, from the free edge into the bulk, capturing the ineffective length region where strain builds up and plateaus. A good correlation has been obtained between the model results and the in-situ measurements of axial compression strain in the fiber. A key finding was the importance of including temperature-dependent resin modulus and that the contribution of crystallinity shrinkage to residual strain in the carbon fiber is relatively low. The model developed in this study is also compared to residual stress models in the literature which shows the importance of including the effects of crystallization and cooling rate on temperature and crystallinity-dependent modulus and resin shrinkage for accurate predictions.

Energy-based efficient residual stress prediction of titanium alloys with lack-of-fusion porosity in powder bed fusion

Residual stress and porosity exert adverse effects on the mechanical properties of metal parts fabricated by powder bed fusion. However, the modeling of the correlation between process parameters, process-induced porosity, and residual stress remains challenging. In this paper, a thermo-mechanical model of titanium alloy with porosity is established, where the relation between process parameters, porosity, and effective material properties is identified. The porosity is first predicted based on the power conservation principle in the process. The effective material properties of as-built titanium alloy with porosity are estimated using a homogenization method. Subsequently, the stress and distortion resulting from temperature variation are predicted using the shear deformation theory. The predicted results exhibit excellent agreement with the predictions and experimental results in the literature. Furthermore, the effect of the main process parameters on the porosity, residual stress, and distortion is investigated. The results show that excessive laser power, small laser radii, and extensive scan tracks contribute to a significant lack-of-fusion porosity. Additionally, the normal stress along the scan direction and vertical deflection are the major residual stress and distortion of titanium alloy with porosity, respectively. The proposed model can serve as a guideline for the design and fabrication of PBF-built titanium alloys.

A Novel Method for Predicting Residual Stress in GH4169 Machined Surfaces through Micro-Hardness Measurement

The presence of residual stress seriously affects the mechanical performance and reliability of engineering components. Here, the authors propose a novel method to determine corresponding residual stress through micro-hardness measurements of machined surfaces. In this study, a mathematical model with equal biaxial stress indentation is established. Then, the correlation of micro-hardness with indentation and residual stress is used to determine the prediction equation of residual stress. The material applied in this study is the nickel-based Superalloy GH4169. The residual stress prediction formula for Superalloy GH4169 is ultimately determined through the finite element method by subjecting the indentation to residual stress and fitting the experimental test data. The relationship between the indentation modulus and indentation depth is given quantitatively. The relationship between residual stress and hardness is given quantitatively. The prediction results show that the compressive residual stress can enhance the material hardness and make the contact deformation only require a low indentation depth to achieve complete plastic deformation. Conversely, the tensile residual stress can result in a deeper depth and less hardness at the initial stage of the fully plastic state. For the materials that yield more easily (small ratio of elastic modulus to yield strength), the effect is more evident. The model presented in this paper can accurately forecast corresponding residual stress through measurements of the micro-hardness of machined surfaces.

Development of Maximum Residual Stress Prediction Technique for Shot-Peened Specimen Using Rayleigh Wave Dispersion Data Based on Convolutional Neural Network

Shot peening is a surface treatment process that improves the fatigue life of a material and suppresses cracks by generating residual stress on the surface. The injected small shots create a compressive residual stress layer on the material’s surface. Maximum compressive residual stress occurs at a certain depth, and tensile residual stress gradually occurs as the depth increases. This process is primarily used for nickel-based superalloy steel materials in certain environments, such as the aerospace industry and nuclear power fields. To prevent such a severe accident due to the high-temperature and high-pressure environment, evaluating the residual stress of shot-peened materials is essential in evaluating the soundness of the material. Representative methods for evaluating residual stress include perforation strain gauge analysis, X-ray diffraction (XRD), and ultrasonic testing. Among them, ultrasonic testing is a representative, non-destructive evaluation method, and residual stress can be estimated using a Rayleigh wave. Therefore, in this study, the maximum compressive residual stress value of the peened Inconel 718 specimen was predicted using a prediction convolutional neural network (CNN) based on the relationship between Rayleigh wave dispersion and stress distribution on the specimen. By analyzing the residual stress distribution in the depth direction generated in the model from various studies in the literature, 173 residual stress distributions were generated using the Gaussian function and factorial design approach. The distribution generated using the relationship was converted into 173 Rayleigh wave dispersion data to be used as a database for the CNN model. The CNN model was learned through this database, and performance was verified using validation data. The adopted Rayleigh wave dispersion and convolutional neural network procedures demonstrate the ability to predict the maximum compressive residual stress in the peened specimen.

Efficient prediction of residual stress in additive manufacturing based on semi-analytical solution

Efficient prediction of residual stress in additive manufacturing based on semi-analytical solution

Study on modelling simulation and distribution characteristics of residual stress during robotic weld end grinding of high strength modulated steel

Welding technology is widely used in the aerospace, construction, machinery, and rail transportation industries. After the welding process is completed, the weld seam is typically ground to improve the assembly accuracy and surface integrity of the structural part. However, residual stresses on the surface after grinding often affect the product performance and service life of structural components. Therefore, the formation mechanism and distribution of residual stresses after the robotic weld end grinding of high–strength modulated steel welds were investigated. First, a local contact geometric model of the grinding wheel and weld was established, and a grinding -force contact model was built based on this model. Next, a model of the heat-flow distribution in robotic end grinding was analysed, and the magnitudes of the heat flow in the contact and weld-width directions were calculated for the contact area. The results of these calculations were similar to the actual heat-flow distribution. Then, based on a theoretical analysis, the finite element method was used with a birth–death element to establish a force-thermal coupled residual stress prediction model, which could simulate the material removal process during actual grinding. Finally, the accuracies and reliabilities of the theoretical and simulation models were experimentally verified. The maximum error between the maximum simulated temperature and experimentally measured value was 10 %. The minimum error was 3 % when using the same process parameters, and the residual stress error was approximately 15 %. Based on the simulations, the variations in the temperature and residual stresses with time and position were explored. The highest weld-contact surface temperature of 417.75 °C was found when grinding using process parameters ap = 0.2 mm, vs = 23.55 m/s, vw = 20 mm/s. The residual tensile stresses could be up to +553 MPa in the longitudinal direction and −161 MPa in the transverse direction. The temperature in the zone of demarcation between the weld and base material was 212.8 °C, with maximum residual tensile stresses in both the longitudinal and transverse directions, of up to 352 MPa. The analysis revealed that the magnitude of the residual stresses was consistent with the temperature variation pattern, and that the grinding depth had the greatest influence on the residual stress distribution.