Weld Defects Research Articles

Fatigue experiment and assessment of Q420qFNH weathering steel in the unequal-thickness butt-welded joint

The use of unequal-thickness butt-welded joints is widespread in bridge engineering, material savings mean a decrease in cost, which can lead to lower overall costs and significant benefits Because of their unique structures and propensity for stress concentration and weld defects, it is crucial to investigate their fatigue performance. In this study, 15 sets of unequal-thickness butt-welded joints based on Q420qFNH were subjected to fatigue tests. ABAQUS modelling and a stress-extraction process utilising distance iterations were employed in this study. The key findings demonstrated that the fatigue performance of these weathering steel butt joints was superior, and the stress variation at these joints exhibited greater complexity and resulted in a more pronounced multi-crack fusion effect. Additionally, an attempt was made to evaluate the fatigue behaviour of other welded joints and unequal-thickness butt-welded joints using the same approach as that presented herein. The fatigue evaluation method based on nominal stress yielded a significant scatter of 1:Ts = 4.21. Iterations were conducted based on the scattering at various critical distances. The results indicate that based on the theory of critical distance, the scatter of 1:Ts = 2.304 is lower than the scatter of 1:Ts = 2.502 associated with the notch stress method. These findings suggest that employing this method for fatigue assessment yields favourable outcomes across diverse welded joints.

The Effect of High-Temperature Deformation on the Mechanical Properties and Corrosion Resistance of the 2024 Aluminum Alloy Joint after Friction Stir Welding.

The 2024 aluminum alloy is one of the high-quality lightweight materials. Friction stir welding (FSW) has shown advantages in reducing welding defects and improving welding quality in 2024 aluminum alloys. Currently, the research regarding FSW joint corrosion performance is mainly about the joint without plastic deformation. However, FSW joints often need to be formed into complex shapes by plastic deformation. The influence of plastic deformation on the corrosion performance of FSW joints is the focus of scientific research. To address this problem, the effect of high-temperature deformation on the mechanical properties and corrosion behavior of 2024 aluminum alloy joints was researched. The exfoliation corrosion test, scanning electron microscopy, energy-dispersive spectroscopy, and transmission electron microscopy were employed to analyze the corrosion mechanism and microstructure. The results show that high-temperature deformation of the weld nugget zone greatly affects the mechanical properties and corrosion behavior of the FSW joint. Compared with the 0% deformation specimen, the hardness and tensile strength of the 20% deformation FSW joint increased by 32% and 21%, respectively. The FSW joint with 20% deformation shows the best mechanical properties and corrosion resistance. The number of precipitated S' phases of the FSW joint increases when the deformation increases to 20%, and the shape of the S' phase is a regular round particle shape. The dislocation density of the FSW joint increases continuously during deformation, which provides a favorable nucleation location for the S' phase.

Automated weld defect segmentation from phased array ultrasonic data based on U-net architecture

Ultrasonic inspection is an environmentally friendly and easily deployable nondestructive testing (NDT) method widely used for defect detection of critical components in the industry. Phased array ultrasonic testing (PAUT) is one of the most advanced ultrasonic inspection methods, which gives volume inspection with increased resolution and coverage, improving inspection efficiency. Because of the weld structure echoes and the abstract nature of ultrasound images, especially facing meters and feature-changing weld joints with different thicknesses and welding methods in shipbuilding, the analysis of the PAUT weld data still relies on experienced rater random inspections. Rating complex welded products process is lengthy, costly, and prone to introduce human error during the manual rating but challenges automatic detection. To automatically segment defects in PAUT data, this work shows a combination of PAUT data of ship weld and three-dimensional (3D) U-net architecture. Combining PAUT imaging principles with welding and scan processes, a PAUT volumetric image dataset, including different thicknesses and scan angles, is established. We pioneered the application of 3D U-net architecture to segment defects in PAUT volume data. We found that U-net architecture with two encoding stages will perform better in segmenting defects in PAUT data, and region-based loss mainly improves the accuracy. Furthermore, a lightweight U-net architecture containing skip-connection and residual blocks is proposed with precision and efficiency improvement. The validation results show that the proposed U-net architecture offers a feasible solution to the problem of segmenting defects from PAUT data with a Dice accuracy of 90.9 %. Segmentation results help to locate and measure defects. This method makes locating and sizing defects in PAUT weld data possible within a fraction of a second.

Interference study of double cracks on the inner surface of pipe bends with axial misalignment weld defects

In engineering practice, erosion wear cracks in pipelines occasionally exhibit a phenomenon of multiple crack aggregation. To investigate the effect of the interaction between double cracks in natural gas pipelines with axial misalignment welds, this paper, based on fracture mechanics, firstly establishes a model of an external axial misalignment weld bend. By analyzing the double-crack model of the main crack and subsidiary crack, the interference effect of the subsidiary crack on the leading edge of the main crack is studied. The results show that when both the main crack and the accessory crack are annular cracks, all the points on the leading edge of the main crack are within the shielding zone of the accessory crack under the condition of co-linear neighboring position. Meanwhile, under the condition of coaxial neighboring position, all the points on the leading edge of the main crack are partly within the shielding zone of the accessory crack and partly within the reinforcing zone. Conversely, when the main crack is an annular crack and the accessory crack is an axial crack, the conclusion is opposite to the former. This study can provide a theoretical basis for the safety evaluation of multi-cracked gas pipelines.

Study on microstructure, mechanical and corrosion behavior of Ti-6Al-4V titanium alloy by Keyhole gas tungsten arc welding

In this paper, 8 mm thick Ti6Al4V titanium alloy was successfully welded by keyhole gas tungsten electrode arc welding (K-TIG) technology without special edge preparation. The microstructure, mechanical properties and corrosion resistance of the joint were studied. The experimental results show that the overall weld is nail-shaped, and there are no welding defects such as porosity, cracks, and unfused sidewalls. The weld area is mainly composed of many fine acicular -like α, coarse acicular -like α and massive α intertwined with each other, and the distribution ratio of high-angle grain boundaries in the heat-affected zone and weld metal area is higher than that of the base metal. The microhardness fluctuation of each region in the welded joints is not large, and the tensile strength of the welded joints reaches 96.7 % of the base metal, and the post-weld mechanical properties are good. The K-TIG welded joints and the base metal were electrochemically analyzed, and the welded joints showed good corrosion resistance.

Laser welding by focusing multi-laser beams

This paper presents a multi-laser beam focusing on laser welding based on a reflective splitting mirror, aiming to improve the stability of the laser welding process and reduce welding defects. By validating the laser welding process on Q235 mild steel using a newly developed multi-focused laser beam, the results show that by increasing the number of laser focal points, the porosity and spattering are effectively reduced, thus achieving higher process stability. Additionally, the rotation of the laser focal point further reduces the occurrence of welding defects such as porosity and spattering.

Influences of preheating parameters on the quality of weld by thermite rail welding

The major goal of this study is to enhance the mechanical and metallurgical characteristics of rail steel grade R260 joined by thermite welding under various preheating conditions, including preheating time and gas pressure. Mainly two conditions, referred to as the Normal Condition and Improved Condition, are carried out for experiments. Prior to welding, the Normal Condition was preheated using liquefied petroleum gas (LPG) and oxygen gas pressures of 1 bar and 4.5 bar for 3 min, and the Improved Condition was preheated using liquefied petroleum gas and oxygen gas pressures of 1.2 bar and 4.5 bar for 6 min and 30 s. To investigate the mechanical and physical properties, micro-Vickers hardness tests, tensile tests and slow bending tests were also carried out. Welded metal in Normal Condition has many defects, including gas holes and shrinkage cavities. When comparing the Normal Condition to the Improved Condition, the Improved Condition demonstrates significantly more bending load and deflection. Specifically, the thermite welded rail sample of Improved Condition demonstrated a remarkable ability to endure bending loads of 108 tonnes and a deflection of 16 mm, and this sample remained unbroken until it exceeded 50% of the standardized deflection limit (10 mm). In addition, the average hardness values for the Improved Condition of the weld metal zone and the heat-affected zone were 331 HV and 289 HV, respectively. The Normal Condition produced an unsatisfactory fracture surface after slow bending test. This was caused by weld defects at the thermite weld due to inappropriate preheating.

Research on intelligent classification of weld defects based on improved ResNeXt network

Welding technology is widely used in the connection of metal workpieces, and then due to improper operation and other reasons, small defects may occur in the welding, which will lead to welding failure and hazardous accidents. Therefore, it is very important to accurately identify welding defects. The automatic defect identification method based on deep learning learns features directly from the full-focus mapping of weld defects and automatically classifies them; the ResNeXt network is selected for improvement, and the SK convolution unit is introduced into the original ResNeXt-50 network model to enhance the adaptive adjustment of sensory field; in order to alleviate the problem of the point-by-point convolution in the ResNeXt-50 which consumes a large amount of In order to alleviate the problem that point-by-point convolution in ResNeXt-50 consumes a large amount of computation, 1×1 group convolution is used instead of the original point-by-point convolution, and channel rearrangement is introduced to alleviate the problem of weakened information exchange of groups caused by group convolution. After improvement, the ResNeXt-50 network model can achieve a classification accuracy of 98.2%, which is 5.5% more than that of the original ResNeXt-50 on this paper's dataset, and the classification accuracy after T-sne visualization also has obvious separation boundary and clustering effects.

Weld Defect Downscaling Recognition for Phased Array Ultrasonic Data Based on Semantic Segmentation

Phased array ultrasonic testing (PAUT) is one of the most advanced ultrasonic inspection methods, providing inspection data in image sequences with increased resolution and coverage, consequently enhancing inspection efficiency. Due to the inherently abstract nature of ultrasound images, the analysis of PAUT weld data continues to rely on the expertise of experienced inspectors, particularly in the context of shipbuilding welds, which exhibit characteristics such as arbitrary lengths, varying thicknesses, and various weld methods, particularly when associated with hybrid laser-arc welding involving higher caps and narrower fill widths. This paper applies a semantic segmentation architecture to PAUT data for hybrid laser-arc welding to segment defects automatically. A downscaling PAUT image dataset incorporating PAUT imaging principles with scan processes is established. The proposed architecture adopts an encoder-decoder architecture and segments defects downscaling, enabling direct localization and sizing defects in long PAUT data. Meanwhile, it handles PAUT image sequences of arbitrary length, eliminating the need for horizontal scale normalization while preserving the integrity of defect shapes. The proposed architecture was validated in the dataset to demonstrate the superiority of the proposed method in locating and sizing defects in PAUT weld data, offering a promising automated solution to a challenge in ultrasonic inspection methodologies.

An X-Ray-Based Automatic Welding Defect Detection Method for Special Equipment System

An X-Ray-Based Automatic Welding Defect Detection Method for Special Equipment System

Advanced ANN Techniques for Precise Detection and Classification of Welding Defects

Advanced ANN Techniques for Precise Detection and Classification of Welding Defects

Defect identification method for stainless steel welded pipes based on eddy current testing

Abstract A defect identification and classification method for 316 stainless steel welded pipes is proposed based on eddy current testing technology. Initially, this approach acquires eddy current signals from steel pipes, applies Empirical Mode Decomposition (EMD) to derive Intrinsic Mode Functions (IMFs), chooses the principal IMF based on interrelationships, and extracts time-frequency domain characteristic parameters from the selected IMF. To enhance model recognition efficiency, Principal Component Analysis (PCA) is employed to reduce the dimensionality of the feature vector set. Ultimately, a Support Vector Machine (SVM) is utilized to identify and classify weld defects. The results indicate that this method is highly accurate in identifying defects in 316 stainless steel welded pipes.

Progressive effect of dual-hybridization in friction stir welding by ultrasonic vibration and resistive heating for joining dissimilar material Al6063 aluminium alloy and C26000 copper alloy

In the world of disruptive technology, Al–Cu joints are quite in demand for electric vehicle batteries and other ion battery applications. Fusion welding is the less preferred method due to its many disadvantages; friction stir welding has the potential to become a sustainable process for producing a sound dissimilar Al–Cu joint. A study of multiple hybridization to the Friction Stir Welding (FSW)was made by utilizing two different energy sources of ultrasonic energy and electric current. The improvement is the property by different combination of the hybridization studied for utilizing the multiple hybridization technique to the FSW process for improving the weld efficacy and defects free weld even in case of dissimilar joints. Mechanical properties obtained by varying the process parameters have been studied and compared. Three process parameters have been selected including ultrasonic energy (10 KHz), electric current (75–125 Amps), rpm (400–600 rpm) and transverse tool rate (30–50 mm/min). A remarkable improvement in the mechanical property has been monitored by adding electric current to the UAFSW. Similar optimistic results in the improvement of the property have been found by adding Ultrasonic energy to RHFSW. The comparative study in the mechanical property had been presented to explain the improvement in the weld efficacy.

A data-driven time-sequence feature-based composite network of time-distributed CNN-LSTM for detecting pore defects in laser penetration welding

A data-driven time-sequence feature-based composite network of time-distributed CNN-LSTM for detecting pore defects in laser penetration welding

Detection and classification of invisible weld defects by magneto-optical imaging under alternating magnetic field excitation

Aiming at the difficult to detect invisible weld defects, a magneto-optical (MO) imaging non-destructive testing (NDT) system excited by an alternating magnetic field is proposed for feature extraction and detection classification of invisible weld defects. The relationship between the MO image features and the magnetic field intensity is analyzed based on the Faraday MO effect. A magnetic dipole model is proposed to study the magnetic field distribution on weld defect. A three-dimensional finite element model of invisible weld defects (with a width of 0.01 mm) is established, and the distribution of leakage magnetic field for different types of invisible defects is analyzed. In order to detect different invisible weld defects under the excitation of alternating magnetic field, MO imaging NDT experiments are carried out. The effectiveness of the finite element model is verified by the experiment. The gray value of the MO image can match the corresponding leakage magnetic field intensity. The Tamura method and gray-level co-occurrence matrix (GLCM) method are used to extract the texture features of natural invisible weld defect’s MO image, and these texture features of MO images are used as input data for the defect classification model established using back propagation (BP) neural network. Experimental results show that the classification accuracy of the GLCM + Tamura-BP model is higher than that of the GLCM-BP model, and its overall classification accuracy reaches 91.1%, indicating that this model can effectively and accurately classify natural invisible weld defects.

Comparison of fatigue crack growth design curves on GMAW and EBW joints of high strength steels

There is a growing demand in the industrial sector for the use of high-strength structural steels (HSSSs), which can achieve a significant weight reduction in structures. These structural steels are usually produced by quenching and tempering (Q + T) or thermomechanical treatment (TM), and their applications in welded structures pose several challenges for the users. In industrial practice, gas metal arc welding (GMAW) is basically the most commonly used fusion welding process, which has a relatively high heat input. However, at HSSSs, there is a need for low heat input but, at the same time, productive welding processes. High-energy density welding processes, e.g., electron beam welding (EBW), offer a unique opportunity to weld these steels. The widespread use of HSSSs is also hampered by the fact that the benefits of high strength can be exploited primarily under static loading. At the same time, different welded structures made of HSSSs are often subjected to cyclic loading, and possible weld defects and material discontinuities are major risks in this case. During our experiments, GMAW and autogenous EBW processes were applied to make welded joints from S960 Q + T and TM structural steels. The fatigue resistance of the welded joints was characterized by fatigue crack growth (FCG) tests, considering the increased crack sensitivity of HSSSs. A statistical approach was followed both in the design of the experiments and in the evaluation of their results. Based on the test results fatigue crack propagation design curves were determined for the investigated GMAW and EBW welded joints. The design curves were compared with each other, with design curves of lower strength material (S690QL) and with the recommended fatigue crack growth laws of BS 7910.

Understanding the mechanisms behind ultrasonic vibration in resistance spot welding of aluminum and steel

An ultrasonic-assisted resistance spot welding process, which can improve the quality of aluminum/steel spot-welded joints, has been developed. However, the mechanism of the thermal-electrical-mechanical behavior caused by the ultrasonic longitudinal vibration to welding process has not been clarified. In this study, the role and impact mechanisms of ultrasonic longitudinal vibration during the welding process to form joints were confirmed by combining the microstructural characterization of the formed joints with the signal analysis of the process, as well as the analysis of the independent finite element numerical calculation results. Additionally, the interface metallurgical reaction behavior, welding defects, fracture behavior, and joint strength characteristics of ultrasonic-assisted aluminum/steel resistance spot welding joints were comprehensively analyzed, and the results indicated that the sizes of the aluminum/steel nuggets decreased. It could be attributed to the competition among multiple ultrasonic effects. Specifically, ultrasonic vibration decreased the contact resistance between the aluminum/steel sheets, whereas the acoustic cavitation and acoustic streaming effects increased the electrical conductivity of molten steel and thermal conductivity of molten aluminum, respectively. Moreover, ultrasonic vibration promoted the radial creep of molten and near-molten aluminum, which expanded in the effective bonding area between the two workpieces. Furthermore, ultrasonic vibration effectively reduced the thickness of the intermetallic compound layer (<3.0 µm), concurrently inhibited the formation of interfacial welding defects. Finally, the peak tensile-shear load of aluminum/steel joints is significantly increased under multiple ultrasonic optimization mechanisms.

Fatigue life evaluation of laser welded lap joints of dissimilar aluminum alloys

Abstract Constant amplitude fatigue tests were conducted on 6061/7075 dissimilar aluminum alloy laser welded lap specimens, as well as weld line cross-section hardness measurements. The fatigue test results show that the specimens exhibit multiple fracture modes that exit near the weld seam. The microhardness data on weld line cross-section from 7075 side to 6061 side display a sharp change and the softening phenomenon is serious. The hardness variation in heat affected zone of laser welding is very shallow, and its hardness is close to that of the base material. It was found that there are slag inclusions and pores in the weld seam when observing the fatigue fracture surface using SEM, and a small amount of secondary cracks were generated. However, stress concentration plays a dominant role in causing specimen fracture under fatigue loading, rather than welding defects. Defective specimens are found to have higher fatigue strength. The fatigue life prediction results obtained by the notch stress method and the hot spot stress method are both conservative and fall within two factor lines. The hot spot stress method has relatively higher accuracy for life prediction. The accuracy of both methods in predicting life is influenced by the location of the fracture.

Investigation of the influence of defect volume on weld strength during FSW: A simulation and experimental study

Internal defects such as voids/cavities significantly hamper the weld quality, resulting in lower weld efficiency and mechanical properties. Therefore, there is a scope for developing a relationship between quantum of defects and weld efficiency. This research uses a thermomechanical model based on coupled Eulerian-Lagrangian (CEL) to predict defect formation during friction stir welding (FSW) of a 3 mm thick AA2024. A square pin tool is used in current research. A novel concept of defect volume is developed using the Eulerian volume fraction within the weld domain, and a mathematical equation is developed to calculate weld efficiency as a function of defect volume, stir zone temperature, and axial force. The developed model predicted weld efficiency with less than 5 % error with R-squared coefficients of 0.9. The model established effective material flow behavior having an intercalated zig-zag path, resulting in defect-free weld with 86 % weld efficiency at 1500 rpm and 150 mm/min. It has a defect volume of 0.45 mm3. EBSD analysis shows an average grain size of 4.9 and 6.1 µm for defect-free and defective welds, respectively. Material flow, mechanical, and metallurgical characterization of lowest and highest weld efficiency is carried out to understand the underlying weld failure mechanism.

Resistance spot welding defect detection based on vectorized dynamic resistance signal and LightGBM classifier

The problem of real-time detection of welding defects is a difficult problem in resistance spot welding. It is found that the dynamic resistance has a strong connection with the growth of the nugget. The dynamic resistance signals with welding defects are significantly different from those of normal welding, and the dynamic resistance signals between different welding defects show different characteristics, and the dynamic resistance signals of the same kind of welding defects may also differ from each other. The most common practice today to realize the detection of resistive defects is by extracting the time-domain features of the signal waveforms. However, this approach is highly subjective, so this article proposes a double-size mesh division method to process the dynamic resistance signal. Experiments prove that the method can retain the characteristics of the signal curve well, and it is also improves the training speed and accuracy compared with the mesh division method. Finally, the processed signals are classified using the light gradient boosting machine classifier with an accuracy of 98.55%.