Search Results (49)

Taking the shield construction of Xiamen Metro Line 2 tunnel side-crossing the Tianzhushan overpass and under-crossing the Shen-Hai Expressway as the engineering background, FLAC3D 6.0 software was used to examine the deformation of adjacent structures based on shield construction parameters in upper-soft and lower-hard strata. The reliability of the numerical simulation results was verified by comparing measured and predicted deformations. The study results indicate that deformation of the pile will occur during the construction of the tunnel shield next to the pile foundation. The shape of the pile deformation curve in the horizontal direction is significantly influenced by the distance from the pile foundation to the adjacent tunnel’s centerline, as well as by soil bin pressure, grouting layer thickness, and stress release coefficient. During the tunnel shield construction beneath the expressway, increasing the soil bin pressure, the grouting layer thickness, and reducing the stress release coefficient can effectively minimize surface deformation and differential settlement on both sides of the deformation joints between the bridge and the roadbed. The practice shows that, by optimizing shield construction parameters in upper-soft and lower-hard strata, the deformation of nearby bridges and pavements can be kept within allowable limits. This is significant for reducing construction time and costs. The findings offer useful references for similar projects. Full article

This investigation explores the application of supervised machine learning regression approaches to predict various responses, including penetration, bead width, bead height, hardness, ultimate tensile strength, and percentage elongation in autogenous TIG-, A-TIG-, and TIG-welded joints of SS304H, which is considered as an advanced high-temperature resistant material. The machine learning (ML) models were constructed based on the data gathered from 50 experimental runs, considering eight key input variables: gas flow rate, torch angle, filler material, welding pass, flux application, root gap, arc gap, and heat input. A total of 80% of the collected dataset was used for training the models, while the remaining 20% was reserved for testing their performance. Six ML algorithms—Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost)—were implemented to assess their predictive accuracy. Among these, the XGBoost model has demonstrated the highest predictive capability, achieving R² scores of 0.886 for penetration, 0.926 for width, 0.915 for weld bead height, 0.868 for hardness, 0.906 for ultimate tensile strength, and 0.926 for percentage elongation, along with the lowest values of RMSE, MAE, and MSE across all responses. The outcomes establish that machine learning models, particularly XGBoost, can accurately predict welding characteristics, marking a significant advancement in the optimization of TIG welding parameters. Consequently, integrating such predictive models can substantially enhance the precision, reliability, and overall efficiency of welding processes. Full article

In the context of the rapid advancement of reconstruction and expansion projects, there has been a significant increase in the demand for the inspection and evaluation of existing asphalt pavements. In order to enhance the efficiency and effectiveness of joint detection using 3D ground-penetrating radar and falling weight deflectometers, this study investigates non-destructive testing methods for existing asphalt pavements based on geostatistical correlation principles. The relationship between crack rate and deflection is analyzed using group average values. The characteristic sections division method based on the crack rate guideline was realized. Research on the prediction method for deflection using Kriging interpolation has been conducted. Research has revealed that there is a positive correlation between the crack rate and the deflection index. The principle of the singularity index can be employed to divide characteristic sections. The falling weight deflectometer is capable of conducting targeted testing in accordance with characteristic sections. Furthermore, the superior performance of Kriging interpolation in predicting deflection compared with linear interpolation has been demonstrated. According to the Kriging interpolation principle, the detection interval of slow lane deflection should not be more than 100 m. Kriging interpolation on one way lane of matrix data has the best effect, and it can predict deflection using a limited amount of slow lane and hard shoulder data. This facilitates analysis of the changing trend of the deflection index in cases where detection conditions are constrained. This method is of great significance for grasping the true performance status of the existing asphalt pavement structure. Full article

X80 pipeline steel is widely used in oil and gas pipelines because of its excellent strength, toughness, and corrosion resistance. It is welded via gas metal arc welding (GMAW), risking high cold crack sensitivities. There is a certain relationship between the joint hardness and cold crack sensitivity of welded joints; thus, predicting the joint hardness is necessary. Considering the inefficiency of welding experiments and the complexity of welding parameters, we designed a set of processes from temperature field analysis to microstructure prediction and finally hardness prediction. Firstly, we calculated the thermal cycle curve during welding through multi-layer welding numerical simulation using the finite element method (FEM). Afterwards, BP neural networks were used to predict the cooling rates in the temperature interval that ferrite nuclears and grows. Introducing the cooling rates to the Leblond function, the ferrite fraction of the joint was given. Based on the predicted ferrite fraction, mapping relationships between joint hardness and the joint ferrite fraction were built using BP neural networks. The results shows that the error during phase fraction prediction is less than 8%, and during joint hardness prediction, it is less than 5%. Full article

Studying multiple properties of a material concurrently is essential for obtaining a comprehensive understanding of its behavior and performance. However, this approach presents certain challenges. For instance, simultaneous examination of various properties often necessitates extensive experimental resources, thereby increasing the overall cost and time required for research. Furthermore, the pursuit of desirable properties for one application may conflict with those needed for another, leading to trade-off scenarios. In this study, we focused on investigating adhesive joint strength and elastic modulus, both crucial properties directly impacting adhesive behavior. To determine elastic modulus, we employed a non-destructive indentation method for converting hardness measurements. Additionally, we introduced a specimen apparatus preparation method to ensure the fabrication of smooth surfaces and homogeneous polymeric specimens, free from voids and bubbles. Our experiments utilized a commercially available bisphenol A-based epoxy resin in combination with a Poly(propylene glycol) curing agent. We generated an initial dataset comprising experimental results from 32 conditions, which served as input for training a machine learning model. Subsequently, we used this model to predict outcomes for a total of 256 conditions. To address the high deviation in prediction results, we implemented active learning approaches, achieving a 50% reduction in deviation while maintaining model accuracy. Through our analysis, we observed a trade-off boundary (Pareto frontier line) between adhesive joint strength and elastic modulus. Leveraging Bayesian optimization, we successfully identified experimental conditions that surpassed this boundary, yielding an adhesive joint strength of 25.2 MPa and an elastic modulus of 182.5 MPa. Full article

Maintaining high-quality welded connections is crucial in many industries. One of the challenges is assessing the mechanical properties of a joint during its production phase. Currently, in industrial practice, this occurs through NDT (non-destructive testing) conducted after the production process. This article proposes the use of a virtual sensor, which, based on temperature distributions observed on the joint surface during the welding process, allows for the determination of hardness distribution across the cross-section of a joint. Welding trials were conducted with temperature recording, hardness measurements were taken, and then, neural networks with different hyperparameters were tested and evaluated. As a basis for developing a virtual sensor, LSTM networks were utilized, which can be applied to time series prediction, as in the analyzed case of hardness value sequences across the cross-section of a welded joint. Through the analysis of the obtained results, it was determined that the developed virtual sensor can be applied to predict global temperature changes in the weld area, in terms of both its value and geometry changes, with the mean average error being less than 20 HV (mean for model ~35 HV). However, in its current form, predicting local hardness disturbances resulting from process instabilities and defects is not feasible. Full article

The automobile industry relies primarily on spot welding operations, particularly resistance spot welding (RSW). The performance and durability of the resistance spot-welded joints are significantly impacted by the welding quality outputs, such as the shear force, nugget diameter, failure mode, and the hardness of the welded joints. In light of this, the present study sought to determine how the aforementioned welding quality outputs of 0.5 and 1 mm thick austenitic stainless steel AISI 304 were affected by RSW parameters, such as welding current, welding time, pressure, holding time, squeezing time, and pulse welding. In order to guarantee precise evaluation and experimental analysis, it is essential that they are supported by a numerical model using an intelligent model. The primary objective of this research is to develop and enhance an intelligent model employing artificial neural network (ANN) models. This model aims to provide deeper knowledge of how the RSW parameters affect the quality of optimum joint behavior. The proposed neural network (NN) models were executed using different ANN structures with various training and transfer functions based on the feedforward backpropagation approach to find the optimal model. The performance of the ANN models was evaluated in accordance with validation metrics, like the mean squared error (MSE) and correlation coefficient (R²). Assessing the experimental findings revealed the maximum shear force and nugget diameter emerged to be 8.6 kN and 5.4 mm for the case of 1–1 mm, 3.298 kN and 4.1 mm for the case of 0.5–0.5 mm, and 4.031 kN and 4.9 mm for the case of 0.5–1 mm. Based on the results of the Pareto charts generated by the Minitab program, the most important parameter for the 1–1 mm case was the welding current; for the 0.5–0.5 mm case, it was pulse welding; and for the 0.5–1 mm case, it was holding time. When looking at the hardness results, it is clear that the nugget zone is much higher than the heat-affected zone (HZ) and base metal (BM) in all three cases. The ANN models showed that the one-output shear force model gave the best prediction, relating to the highest R and the lowest MSE compared to the one-output nugget diameter model and two-output structure. However, the Levenberg–Marquardt backpropagation (Trainlm) training function with the log sigmoid transfer function recorded the best prediction results of both ANN structures. Full article

High-precision tapered threads are widely used in hard-loaded mechanical joints, especially in the aggressive environment of the drilling of oil and gas wells. Therefore, they must be made of workable materials often difficult to machine. This requires the use of high-performance cutting tools, which means the application of non-zero geometric parameters: rake and edge inclination angles. This study is based on analytical geometry methodology and describes the theoretical function of the thread profile as convoluted surfaces dependent on the tool’s geometric angles. The experiments were conducted using a visual algorithm grounded on the obtained function and prove the practical use of the scientific result. They predict the required accuracy of thread made using a lathe tool with a rake angle of up to 12°. Full article

This study presents a new gas metal arc welding (GMAW) technique that achieves both high efficiency and low heat input using a hybridization of the hot-wire method. The optimal combination of welding speed and welding current conditions was investigated using a fixed hot-wire feeding speed of 10 m/min on a butt joint with a V-shaped groove using 19 mm thick steel plates. Molten pool stability and defect formation were observed using high-speed imaging and cross-sectional observations. The power consumption and heat input were predicted prior to welding and measured in the experiments. The results indicate that a combination of a welding current of 350–500 A and welding speed of 0.3–0.7 m/min is optimal to avoid defect formation and molten metal precedence using three or four passes. The higher efficiency and lower heat input achieved by hot-wire GMAW results in a weld metal of adequate hardness, narrower heat-affected zone, smaller grain size at the fusion boundary, and lower power consumption than those obtained using tandem GMAW and high-current GMAW. Based on the experimental results, a single bevel groove, which is widely used in construction machinery welding joints, was welded using hot-wire GMAW, and we confirmed that the welding part could be welded in six passes, whereas eight passes were required with GMAW only. Full article

The resistance spot welding (RSW) process is still widely used to weld panels and bodies, particularly in the automotive, railroad, and aerospace industries. The purpose of this research is to examine how RSW factors such as welding current, welding pressure, welding time, holding time, squeezing time, and pulse welding affect the shear force, micro-hardness, and failure mode of spot welded titanium sheets (grade 2). Resistance spot welded joints of titanium sheets with similar and dissimilar thicknesses of 1–1 mm, 0.5–0.5 mm, and 1–0.5 mm were evaluated. The experimental conditions were arranged using the design of experiments (DOE). Moreover, artificial neural network (ANN) models were used. Different training and transfer functions were tested using the feed-forward backpropagation approach to find the optimal ANN model. According to the experimental results, the maximum shear force was 5.106, 4.234, and 4.421 kN for the 1–1, 0.5–0.5, and 1–0.5 mm cases, respectively. The hardness measurements showed noticeable improvement for the welded joints compared to the base metal. The findings revealed that the 0.5–0.5 mm case gives the highest nugget and heat-affected zone (HAZ) hardness compared to other cases. Moreover, different failure modes like pull-out nugget, interfacial, and partial failure between the pull-out nugget and interfacial failure were noticed. The ANN outcomes based on the mean squared error (MSE) and coefficient of determination (R²) as validation metrics demonstrated that using the Levenberg–Marquardt (Trainlm) training function with the log sigmoid transfer function (Logsig) gives the best prediction, where R² and MSE values were 0.98433 and 0.01821, respectively. Full article

The confinement boundaries of spent nuclear fuel (SNF) canisters are typically fusion welded. Welded microstructures, strain hardening, and residual stresses combined with a chemically aggressive, chloride-rich environment led to concerns that the welded canister may be susceptible to chloride-induced stress corrosion cracking (CISCC). A comprehensive understanding of the modification of stainless steel (SS) metallurgical and mechanical properties by fusion welding could accelerate the predictive analysis of CISCC susceptibility. This paper describes a submerged arc welding (SAW) procedure that was developed and qualified on 12.7 mm (0.5 in.) thick AISI 304/304L SS to produce joints in a way similar to actual SNF canister manufacturing. This procedure has the potential to reduce the production cost and weld CISCC susceptibility by using fewer welding passes and lower heat input than current industrial applications. Global and local mechanical behaviors and properties, as well as residual stress distributions on the welded joint, were studied. The results indicate that hardness values in the fusion zone (FZ) and heat-affected zone (HAZ) are slightly higher than that of the base metal. Strain localization was presented in the HAZ before the tensile stress reached its maximum value, and then it shifted to the FZ. The specimen finally broke in the FZ. High tensile residual stresses exhibited in the FZ and the nearby HAZ suggest the highest CISCC-susceptible spots. The maximum tensile residual stresses were along the welding direction, indicating that if cracks occur, they would be perpendicular to the welding direction. This study involved developing and qualifying a SAW procedure for SNF canister production. The new procedure yielded cost savings (SAW working efficiency increased by about 80%), improved mechanical properties, and presented moderate residual stresses. Analysis revealed that the welded joint’s low-stress and high-stress damage assessments may be affected by shifts in the strain localization spot under loading. Full article

Rotary friction welding (RFW) has no electric arc and the energy consumption during welding can be reduced as compared with conventional arc welding since it is a solid-phase welding process. The RFW is a sustainable manufacturing process because it provides low environmental pollution and energy consumption. However, few works focus on the reliability of dissimilar polymer rods fabricated via RFW. The reliability of the frictionally welded components is also related to the ambient temperatures. This work aims to investigate the effects of ambient temperature on the mechanical properties of frictionally welded components of polycarbonate (PC) and acrylonitrile butadiene styrene (ABS) dissimilar polymer rods. It was found that the heat-affected zone width increases with increasing rotational speeds due to peak welding temperature. The Shore A surface hardness of ABS/PC weld joint does not change with the increased rotational speeds. The Shore A surface hardness in the weld joint of RFW of the ABS/PC is about Shore A 70. The bending strength was increased by about 53% when the welded parts were placed at 60–70 °C compared with bending strength at room temperature. The remarkable finding is that the bending fracture position of the weldment occurs on the ABS side. It should be pointed out that the bending strength can be determined by the placed ambient temperature according to the proposed prediction equation. The impact energy was decreased by about 33% when the welded parts were placed at 65–70 °C compared with the impact energy at room temperature. The impact energy (y) can be determined by the placed ambient temperature according to the proposed prediction equation. The peak temperature in the weld interface can be predicted by the rotational speed based on the proposed equation. Full article

The current study uses three different pin eccentricities (e) and six different welding speeds to investigate the impact of pin eccentricity on friction stir welding (FSW) of AA5754-H24. To simulate and forecast the impact of (e) and welding speed on the mechanical properties of friction stir welded joints for (FSWed) AA5754-H24, an artificial neural network (ANN) model was developed. The input parameters for the model in this work are welding speed (WS) and tool pin eccentricity (e). The outputs of the developed ANN model include the mechanical properties of FSW AA5754-H24 (ultimate tensile strength, elongation, hardness of the thermomechanically affected zone (TMAZ), and hardness of the weld nugget zone (NG)). The ANN model yielded a satisfactory performance. The model has been used to predict the mechanical properties of the FSW AA5754 aluminum alloy as a function of TPE and WS with excellent reliability. Experimentally, the tensile strength is increased by increasing both the (e) and the speed, which was already captured from the ANN predictions. The R² values are higher than 0.97 for all the predictions, reflecting the output quality. Full article

Rotary friction welding (RFW) could result in lower welding temperature, energy consumption, or environmental effects as compared with fusion welding processes. RFW is a green manufacturing technology with little environmental pollution in the field of joining methods. Thus, RFW is widely employed to manufacture green products. In general, the welding quality of welded parts, such as tensile strength, bending strength, and surface hardness is affected by the peak temperature in the weld joint during the RFW of dissimilar plastic rods. However, hitherto little is known about the domain knowledge of RFW of acrylonitrile butadiene styrene (ABS) and polycarbonate (PC) polymer rods. To prevent random efforts and energy consumption, a green method to predict the peak temperature in the weld joint of dissimilar RFW of ABS and PC rods was proposed. The main objective of this work is to investigate the peak temperature in the weld joint during the RFW using COMSOL multiphysics software for establishing an empirical technical database of RFW of dissimilar polymer rods under different rotational speeds. The main findings include that the peak temperature affecting the mechanical properties of RFW of PC and ABS can be determined by the simulation model proposed in this work. The average error of predicting the peak temperature using COMSOL software for five different rotational speeds is about 15 °C. The mesh element count of 875,688 is the optimal number of meshes for predicting peak temperature in the weld joint. The bending strength of the welded part (y) using peak welding temperature (x) can be predicted by the equation of y = −0.019 x² + 5.081x − 200.75 with a correlation coefficient of 0.8857. The average shore A surface hardness, impact energy, and bending strength of the welded parts were found to be increased with increasing the rotational speed of RFW. Full article

Rotary friction welding (RFW) is a green manufacturing technology with environmental pollution in the field of joining methods. In practice, the welding quality of the friction-welded parts was affected by the peak temperature in the weld joint during the RFW of dissimilar plastic rods. In industry, polycarbonate (PC) and acrylonitrile butadiene styrene (ABS) are two commonly used plastics in consumer products. In this study, the COMSOL multiphysics software was employed to estimate the peak temperature in the weld joint during the RFW of PC and ABS rods. After RFW, the mechanical performance and microstructural evolution of friction-welded parts were investigated experimentally. The average Shore A surface hardness, flexural strength, and impact energy are directly proportional to the rotation speed of the RFW. The quality of RFW is excellent, since the welding strength in the weld joint is better than that of the ABS base materials. The fracture occurs in the ABS rods since their brittleness is higher than that of the PC rods. The average percentage error of predicting the peak temperature using COMSOL software using a mesh element count of 875,688 for five different rotation speeds is about 16.6%. The differential scanning calorimetry curve for the friction-welded parts welded at a rotation speed of 1350 rpm shows an endothermic peak between 400 to 440 °C and an exothermic peak between 600 to 700 °C, showing that the friction-welded parts have better mechanical properties. Full article