Prediction Of Residual Stresses Research Articles

Early age bond-slip behavior of reinforcing steel bars in concrete subjected to uniaxial lateral pressure: An experimental study

The bond between reinforcing bars and concrete is a crucial factor concerning the serviceability of reinforced concrete structures, particularly for those during construction in some developing areas where plain bars were still used as reinforcements. Despite numerous studies on early age bond behavior between steel bars and concrete, little research has been conducted to quantify the effect of uniaxial lateral pressures on this bond. This study involved testing a total of 378 pullout specimens to evaluate the early age bond-slip behavior of plain bars in concrete under uniaxial lateral pressure. The main test parameters included the concrete ages (1, 3, 7, 14 and 28 days), lateral pressures (0, 0.1, 0.2, 0.4 and 0.6 times the compressive strengths of concrete), concrete strength grades (C20, C35 and C45), and bar diameters (12, 16 and 20 mm). Based on the experimental results, a series of analytical expressions were developed for bond strength, maximum bond slip and residual bond stress predictions considering the effects of early age and lateral pressure. The results demonstrate that degradation of bond strength at early ages can be effectively mitigated by uniaxial lateral pressures. As the age decreases from 28 to 1 days, the bond strength exhibits a significant decrease of up to 51.59 %, while this adverse effect can be reduced by around 10 % in the presence of lateral pressures. Increasing the pressure level from 0 to 0.4 results in a significant increased early age bond strength by 112.6 % to 199.0 % for different bar diameters. However, the residual-to-maximum bond stress ratio remains nearly constant at 0.49 regardless of age and lateral pressure, as the pressure primarily contributes to frictional forces at the bar-concrete interface. An age-dependent bond-slip constitutive model considering the effect of uniaxial lateral pressures was finally proposed and verified with the test results. The paper concludes that given the concrete compressive strength at 28 days, the proposed bond strength and bond stress-slip models can predict the early age bond responses of plain bars in concrete under uniaxial lateral pressures with reasonable accuracy.

Prediction of residual stresses in additively manufactured parts using lumped capacitance and classical lamination theory

Prediction of residual stresses in additively manufactured parts using lumped capacitance and classical lamination theory

Residual stress prediction in Ti17 linear friction welds considering inhomogeneous mechanical properties of joints

Microstructural variations of joints occur in linear friction welding, resulting in inhomogeneous mechanical properties that affect the development of residual stress. The mathematical models for elastic moduli and yield strength of Ti17 were firstly developed to obtain the inhomogeneous mechanical properties of joints. Then, the influence of inhomogeneous mechanical properties on residual stress prediction was investigated. The results show that the finite element model considering inhomogeneous mechanical properties accurately captures the bimodal distribution of residual stress and the location of peak stress in joints. Numerically, the peak values of stress are ∼662 and ∼194 MPa along the direction of oscillation and forging, respectively, which is consistent with the result of the contour method.

The Generation, Measurement, Prediction, and Prevention of Residual Stress in Nickel-Based Superalloys: A Review

As a crucial high-performance material, nickel-based superalloys inevitably generate residual stresses during processing, manufacturing, and usage. The mechanical properties of nickel-based superalloys are significantly reduced by residual stress, which becomes one of the important factors restricting material reliability. The systematic analysis of residual stresses in nickel-based superalloys throughout the entire manufacturing and usage processes is insufficient. The residual stress generation factors, measurement methods, prediction models, and control methods in nickel-based superalloys in recent years are summarized in this paper. The current challenge and future development trends in the research process of nickel-based superalloy residual stress are also presented. A theoretical reference for further research on residual stresses in nickel-based superalloys can be provided in this review.

Prediction of residual stress in precision injection molding of plastic optical lenses based on an improved solidification layer model

Precision injection molding technology (PIM) is the primary technique used for large scale manufacture of plastic optical lenses. However, the injection molding of plastic optical lenses results in a problem of residual stress, which affects the imaging quality of the optical system. The conventional injection molding process uses an orthogonal approach for adjusting the process parameters, requiring a large number of experiments. Therefore, this article proposes an improved residual stress model for precision injection molding of plastic optical lenses based on the solidification layer theory. The residual stress of precision injection molded aspherical plastic optical lens used for imaging optical systems was analyzed through simulation and experimentation. The results demonstrate that the proposed method achieves residual stress prediction for plastic optical lenses after injection molding.

Development of a Novel Beam-Based Finite-Element Approach for the Computationally Efficient Prediction of Residual Stresses and Displacements in Large 3D-Printed Polymer Parts

Recently, 3D printing of large, structural polymer parts has received increasing interest, especially for the creation of recyclable structural parts and tooling. However, the complexity of large-scale 3D polymeric printing often dictates resource-intensive trial and error processes to achieve acceptable parts. Existing computational models used to assess the impact of fabrication conditions typically treat the 3D-printed part as a continuum, incorporate oversimplified boundary conditions and take hours to days to run, making design space exploration infeasible. The purpose of this study is to create a structural model that is computationally efficient compared with traditional continuum models yet retains sufficient accuracy to enable exploration of the design space and prediction of part residual stresses and deformations. To this end, a beam-based finite element methodology was created where beads are represented as beams, vertical springs represent inter-bead transverse force transfer and multi-point, linear constraints enforce strain compatibility between adjacent beads. To test this framework, the fabrication of a large Polyethylene terephthalate glycol (PETG) wall was simulated. The PETG was modeled as linearly elastic with an experimentally derived temperature-dependent coefficient of thermal expansion and elastic modulus using temperature history imported from an ABAQUS thermal model. The results of the simulation were compared to those from a continuum model with an identical material definition, showing reasonable agreement of stresses and displacements. Further, the beam-based model required an order of magnitude less run time. Subsequently, the beam-based model was extended to allow separation of the part from the printing bed and the inclusion of part self-weight during fabrication to assess the significance of these effects that pose challenges for existing continuum models.

Prediction of residual stress after cold rolling and coiling processes

The objective of this manuscript is to present an analytical-numerical methodology for predicting residual stresses after cold rolling of steel sheets, with or without coiling/uncoiling steps. This proposal considers the contribution of hardening after each pass, the increase in temperature arising from forming, with the consequent appearance of a thermal gradient between the center of the sheet and its surface, and the eventual plastic deformation/relief after coiling/uncoiling. Predicted results were compared to experimental data collected from a steel plant, as well as to results available in the literature. The coincidence of values was quite satisfactory, especially in the central third of the residual stress distribution curves. Experimental results from more diverse processes, with greater variation in the number of passes and strain rates, are desirable for definitive validation of the proposed prediction model.

Residual Stress Analysis in Linear Friction Welded Ti17.

Residual stresses with a complex distribution are generated after linear friction welding, which affects the service performance of the weldment. In this work, a numerical model for linear-friction-welded Ti17 (Ti-5Al-2Sn-2Zr-4Mo-4Cr) was developed to investigate the evolution of residual stresses and the effect of welding parameters on residual stresses. Additionally, a method for predicting internal residual stresses was constructed. The results indicate that the residual stresses near the contact interface are largest in the oscillatory direction, peaking at ~661 MPa at 2 mm away from the contact interface. The evolution of stresses is not only related to the inhomogeneous thermal gradient, but also to the forging force. And the stress distribution essentially stabilizes within the duration of the forging force applied. Increasing the amplitude and frequency results in higher peaks of tensile residual stresses and a more concentrated distribution. Conversely, increasing the forging force only reduces the magnitude of the residual stresses. The developed prediction method, based on the similarity of internal residual stress distributions, facilitates the prediction of internal residual stresses using measured surface residual stresses.

Prediction of residual stresses in welded structures based on neural network: a review

Prediction of residual stresses in welded structures based on neural network: a review

Towards an automated framework for numerical prediction of residual stresses and deformations in metal additive manufacturing

Metal additive manufacturing (AM) revolutionizes design with complex structures and customizable material properties. However, high-energy heat source utilized in metal AM creates non-uniform temperature gradients, which usually leads to significant residual stresses and deformations. The present study proposes a line source based semi-analytical thermo-mechanical approach. The accuracy of the proposed line source based model is thoroughly examined by comparing with corresponding experiments in the literature and other numerical models. Besides, an automated framework for fast construction of a thermo-mechanical FE model for the metal AM process is introduced. A numerical tool AM-builder is developed to bridge the gap between AM process parameters and the FE model. The proposed automated framework is followed by utilization of the AM-builder and the line source based model. A purely numerical example is conducted to demonstrate the effectiveness of the proposed automated framework in rapidly constructing AM thermo-mechanical models for various geometries.

Analytical modelling for residual stress prediction in multi-step side milling of GH4169 thin-wall parts based on deformation

GH4169 thin-walled parts are widely used in aerospace due to their high-temperature, corrosion resistance, and fatigue strength. However, they often deform from machining-induced residual stress, which is a significant unresolved manufacturing challenge. Additionally, initial residual stress from previous steps critically impacts subsequent processes. This study found that the residual stress of side milling of thin-walled parts under smaller cutting parameters is mainly caused by mechanical effects, and the influence of milling heat can be ignored. In this study, an analytical prediction model for residual stress of multi-step side milling thin-walled parts based on the deformation of thin-walled parts is proposed for the first time, which is suitable for smaller cutting parameters. Then, the proposed model is examined the mechanisms through which residual stresses develop during side milling and defined the applicability of model based on specific assumptions. To validate these assumptions, an experimental setup was devloped to simulate the movement of the heat source during milling. Subsequent two-step side milling experiments on GH4169 confirmed the accuracy of the analytical prediction model in predicting the residual stresses of multi-step side milling in thin-walled parts. It was observed that higher equivalent bending stiffness in thin-walled parts correlates with reduced machining deformation. The analytical model also provides a strategic approach to control machining deformation by managing the equivalent bending stiffness of the parts.

Hybrid Physical–Virtual Digital Twin System for Additive Manufacturing

This study presents a hybrid physical–virtual digital twin system to enhance additive manufacturing (AM). The system aims to achieve real-time monitoring, predictive modeling, and improved 3D printing control. It integrates sensing-driven and simulation-driven digital twins into a comprehensive hybrid digital twin environment (HDTE) tailored for the Creality Ender-5 Plus 3D printer within the Unity 3D virtual environment. The AM process simulation employs a three-dimensional transient finite element model, enabling precise predictions of thermodynamics, residual stress, final distortion, and solidification parameters. To enhance real-time insights, external sensors like thermocouples, thermal cameras, and LIDAR ranging sensors continuously collect temperature and location data during 3D printing. Experimental results confirm the HDTE’s accuracy. Position assessment experiments show their ability to replicate component positions under varying conditions, while temperature assessment experiments demonstrate their capacity to mimic temperature variations promptly and accurately. The study also provides a detailed analysis of nodal temperature distribution within a 3D printed plate, indicating potential for predictive defect detection and print parameter optimization. Additionally, the study integrates a closed-loop control mechanism, ensuring rigorous quality control, although a detailed exposition of this system is reserved for future publications. This paper bridges the gap between virtual design and physical AM production, offering real-time monitoring, predictive capabilities, and automated control. These advancements enhance efficiency, reduce material waste, and promote sustainability. As this system evolves, it promises to revolutionize additive manufacturing, ushering in a data-driven era of intelligent 3D printing across industries.

Residual stresses prediction in transition metal nitrides sputtered coatings using artificial neural network and experimental evaluation of surface morphology

In the production of transition metal nitride hard coatings under specific deposition conditions, the resulting residual stresses play a critical role in determining the mechanical properties, adhesion, and overall performance of the coatings. In this work, an artificial neural network (ANN) that utilizes feed-forward architecture is used to predict the average residual stress in titanium nitride (TiN) coatings with different coating thickness. These coatings are manufactured under varying bias voltages and temperature on substrate. The impact of variations in coating thickness, bias voltage, and temperature on development of internal residual stresses and changes in microstructure within sputtered coatings have been gathered from existing literature. The data collected is within the context of fibrous (zone I) and mix columnar and fibrous structure (combined zone) as defined by an existing structure zone model. The model is trained with experimental data from literature. The model is trained with experimental data from literature. The validation of the ANN model is carried out by comparing the predictions with experimental results. X-ray diffraction is used to analyze the residual stresses, preferred orientation, and the structure (zone I and combined zone) of produced coatings under different sputtering parameters. The regression and performance curves are used to assess the efficacy of ANN model. The coefficient of determination of trained model and mean square error are 0.9918 and 0.35, respectively. The predicted results show good agreement with experimental and subsequently AFM grain morphology analysis captures the growth mechanisms involved in the residual stresses evolution.

Thermoviscoelastic modelling of highly reactive thermoset resins for liquid moulding applications

This paper presents a methodology for developing thermo-mechanical properties model for highly reactive resins and its use for Resin Transfer Moulding process (RTM) modelling and prediction of residual stresses. Cure Hardening Instantaneously Linear Elastic (CHILE), and Thermoviscoelastic (TVE) models, were implemented to analyze the mechanical behaviour of the resin. Simulation of the RTM process was developed and applied to a representative curved plate geometry. An integral approach was considered where the degree of cure gradient, generated during the filling stage due to the reactivity of the resin, was implemented as initial condition of the stress-deformation process simulation. The validation process included fabricating experimental parts with the representative geometry. The degree of cure variation of highly reactive thermosets during injection caused a significant effect on the final shape of the parts. These effects were captured by the simulations, where the TVE model showed a more accurate prediction of the part distortion.

Residual Stress Analysis of Re-autofrettage of a Swage-Autofrettaged Tube by Computer Modeling Incorporating Accurate Material Representation

Autofrettage processes allow engineers to reduce the thickness of thick-walled cylinders or components in high-pressure applications without sacrificing strength, life, or safety. However, during the autofrettage process, residual stresses will be generated due to plastic deformation. The complex tube material behavior is dominated by the Bauschinger effect. A better understanding and accurate prediction of the residual stress field is critical, which will enable better piping design strategies to minimize deformation and stresses under operating conditions. This study aims to predict and analyze residual stresses resulting from hydraulic re-autofrettage of a swage-autofrettaged thick-walled cylinder by computer modeling. A case study was performed on a thick-walled cylinder of A723 alloy with a radial interference of 2.5%. In order to investigate the effect of the chosen material constitutive representation, results based on the true material constitutive model were compared with the simplified prevalent material model of bi-linear kinematic strain hardening. Computer implementation for the true material was via a user-developed subroutine that incorporates the complex Bauschinger effect. The results indicate that an accurate material constitutive representation is crucial for better and more accurate prediction and understanding of residual stresses induced by autofrettage processes. Computer modeling based on the true material constitutive representation will likely prove to be a powerful tool for the design of autofrettage processes in general and thick-walled cylinders in particular.

Effect of magnetic needle magnetic particle grinding process on the performance of metal aluminum plates

Magnetic needle grinding processing technology is one of the magnetic grinding processing techniques. It possesses the characteristics of micro-cutting removal, small increase in processing temperature, flexible processing, high-quality, and high-precision processing. It is mainly utilized to remove burrs at the edge of the workpiece and the edge of the hole, as well as to finish the surface of the workpiece. It is frequently employed in civil, aerospace, navigation, and other fields. Due to the randomness and complexity of magnetic needle movement in magnetic abrasive finishing, it is difficult to quantify the processing parameters and predict processing effects. Therefore, this paper establishes a simulation model of magnetic needle in magnetic abrasive finishing by the coupling numerical simulation method of fluid dynamics discrete element method (CFD-DEM) to analyze the working state parameters of the magnetic needle. Through the simulation of actual working conditions, the machining process and parameters of magnetic abrasive finishing are quantified and analyzed, and the motion trend of magnetic needles during the machining process is studied. Then, the residual stress of single magnetic needle impact is analyzed with ABAQUS, and the performance enhancement of the workpiece is predicted. Finally, observations of surface morphology and validation of residual stress prediction were conducted through experiments on an aluminum plate. The results show that the residual stress of the aluminum plate is positively correlated with the number of strikes of the magnetic needle. The residual stress changes from tensile stress (+0.1 MPa) to compressive stress (-16.5 MPa). The comparison between simulation results and experimental results is good, indicating that the simulation model can comprehensively consider multiple factors such as magnetic field, particle motion, and fluid flow, and establish a magnetic needle magnetic grinding process model that is suitable for actual working conditions.

Machine learning in prediction of residual stress in laser shock peening for maximizing residual compressive stress formation

Laser Shock Peening (LSP) is an advanced technique for enhancing surface properties, drawing significant interest for its ability to induce beneficial residual stresses in materials. Traditional LSP design processes, reliant on manual parameter selection, often result in imprecise control over the stress distribution, necessitating multiple iterations and high costs. This study introduces a machine learning (ML)-based approach, utilizing the Random Forest (RF) algorithm, to automate and optimize the design of LSP parameters for nickel-aluminium bronze surfaces. Our findings demonstrate the RF model’s capability to accurately predict and optimize residual stress distributions, achieving compressive stresses up to 472 MPa with a notable reduction in design iterations. The model forecasts both uniform and non-uniform stress patterns, particularly identifying areas susceptible to Residual Stress Holes (RSH) with improved precision. With an Absolute Percentage Error (APE) of only 6.2 %, our approach significantly outperforms traditional ML algorithms, offering a novel method for efficiently designing complex residual stress fields in LSP applications.

Residual stress prediction of micro-milling Inconel 718 thin-walled parts

Residual stress prediction of micro-milling Inconel 718 thin-walled parts

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.