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This study investigates the effect of welding parameters on the microstructural evolution and mechanical properties, such as tensile strength and hardness, in mild steel plates welded using gas metal arc welding (GMAW). The welding parameters examined include welding current, arc voltage, and gas flow rate, with ER K-71 T filler wire as the consumable material. Results indicate that a higher welding current and reduced gas flow rate result in a maximum tensile strength of approximately 533 MPa. However, discrepancies were noted in the microhardness data across different zones, with the heat-affected zone (HAZ) exhibiting a hardness of 115.8 HV, while the weld zone and base metal showed values of 76.7 HV and 112.9 HV, respectively. Microstructural analysis revealed the presence of bainite and ferrite with a tempered martensitic structure in the welded area, demonstrating that the combination of welding parameters plays a critical role in controlling phase transformations and mechanical behavior. It is essential to reconcile the observed hardness values with the conclusions drawn to ensure accurate interpretation of the data. Highlights: The study investigates how different welding process parameters, including current, voltage, and gas flow, affect the internal structure and mechanical characteristics of weld metal. It was found that the highest tensile strength is achieved with a combination of high welding current and low gas flow rate. The welded joint shows greater hardness in the heat-affected zone compared to the original metal, with the fusion zone being less hard. Analysis of the weld area reveals the presence of bainite, ferrite, and tempered martensite, which are responsible for the mechanical properties observed. [ABSTRACT FROM AUTHOR]

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In weld-based manufacturing processes such as welding and metal deposition additive manufacturing (AM), the weld bead is a direct indicator of manufacturing quality. For example, the geometry of the weld bead was optimized to a net shape which outperformed conventional geometries. Automatic monitoring of weld bead is thus of prime importance for welding process control and quality assurance. This paper develops a general-purpose convolutional neural network (CNN) for pixel-level detection and monitoring of beads, regardless of welding materials, machine, manufacturing conditions, etc. To achieve the generality, we collected a great variety of welding images containing 2677 single-line beads from 231 research articles, followed by pixel-wise hand-annotation. Consequently, the trained CNN can recognize different beads from various backgrounds at a pixel level. Case studies show that compared to the image-level classification in prior research, its pixel-level labeling permits real-time, complete characterization of weld beads (e.g., detailed morphology, discontinuity, spatter, and uniformity) for more informed process control. This research represents a significant step towards developing a truly human-like monitoring system with low-level scene understanding ability and general applicability. [ABSTRACT FROM AUTHOR]

Weld bead segmentation is important for the automation of quality control and inspection of welded joints. Due to the irregularity and the lack of topological information of the point cloud, it is hard to effectively segment the bead from the weld in 3D point cloud. This research investigates a novel method by using the 3D texture features and global features of point cloud subregions to achieve submillimeter precision segmentation of 3D weld point clouds captured by a RealSense depth camera. First, a preprocessing algorithm that combines a 2D image processing method with a segmentation method based on the global bead profile is proposed to improve the segmentation performance of the algorithm. Then, based on different feature selection algorithms and machine learning (ML) classifiers, the classification performance of the algorithm is effectively optimized. The experimental results show that choosing appropriate subregion sizes and features can significantly improve the segmentation performance of the algorithm. The average error in the bead width is less than 1 mm. Compared to other methods based on laser profile and ML, it has much better accuracy. [ABSTRACT FROM AUTHOR]

This paper presents an automated Non-Destructive Testing (NDT) system for the in-service inspection of orbital welds on tubular components operating at temperatures as high as 200 °C. The combination of two different NDT methods and respective inspection systems is here proposed to cover the detection of all potential defective weld conditions. The proposed NDT system combines ultrasounds and Eddy current techniques with dedicated approaches for dealing with high temperature conditions. Phased array ultrasound was employed, searching for volumetric defects within the weld bead volume while Eddy currents were used to look for surface and sub-surface cracks. The results from the phased array ultrasound results showed the effectiveness of the cooling mechanisms and that temperature effects on sound attenuation can be easily compensated for up to 200 °C. The Eddy current results showed almost no influence when temperatures were raised up to 300 °C. [ABSTRACT FROM AUTHOR]

With the elimination of solidification defects, friction stir welding becomes the most effective process for joining flat and curved surfaces. Tool geometry (tool pin profile, shoulder diameter) and tool rotation speed mainly contribute to the overall weld quality. The study evaluates the simultaneous effect of tool geometry, rotational speed, base offset and double pass Friction Stir Welding (DP-FSW) on the defect formation and the mechanical properties. Micro-structure and macro-structure have been analysed for weld strength and hardness characterisation, and found, at high tool rotation speed maximum hardness is achieved in the Stir Zone (SZ). A small tunnel present near the middle proved to be more detrimental than the large one at the bottom. Grain refinement in the SZ was 87.2% while augmentation in hardness was 21.3 HV. Maximum tensile strength of approximately 103% with decent bead profile is obtained, using taper cylindrical tool at 710 rpm with 14 mm shoulder diameter. Fractography of the fractured surface revealed a pure ductile failure. It has been discovered that with base offset the strength of the weld is increased. However, reduction of 4.8% was obtained with DP-FSW. Better weld profile gives better strength. With DP-FSW, a significant improvement in bead surface is obtained. [ABSTRACT FROM AUTHOR]

In the present study, the laser melting deposition (LMD) technology was adopted to fabricate FeCoCrNiMn and FeCoCrNiMnAl0.75 high entropy alloys (HEAs). Then, laser welding method was used to join the HEAs in forms of similar (FeCoCrNiMnAl/FeCoCrNiMnAl and FeCoCrNiMnAl0.75/FeCoCrNiMnAl0.75) and dissimilar (FeCoCrNiMnAl0/FeCoCrNiMnAl0.75) combinations, respectively. Ultra-depth field microscope and electron backscatter diffraction detection were used to observe the macro-morphology and microstructure, respectively. It was found that the width of the weld bead and heat-affected zone with higher aluminium content was larger. In both base metals and welded joints, aluminium promoted the face-centered cubic (FCC) phase transfer into body-centered cubic (BCC), significantly refined the grain and the dislocation density of HEA is also increased, which increased strength and hardness, and decreased ductility. Highlights The FeCoCrNiMn/FeCoCrNiMn, FeCoCrNiMn/FeCoCrNiMnAl0.75, FeCoCrNiMnAl0.75/FeCoCrNiMnAl0.75 joints were produced by laser welding. The widths of the weld bead and heat-affected zone increased with higher Al content. The weld bead with higher content of Al exhibited higher strength and hardness, but lower ductility. [ABSTRACT FROM AUTHOR]

An investigation has been done to identify the heat source model suitable for numerical modeling of the Gas Metal arc Welding (GMAW) welding process. In the present investigation, a numerical model was constructed to simulate the GMAW welding process through transient thermal analysis for different thermal heat source models for weld deposition of Mild steel considering semi-cylindrical weld bead geometry. The Gaussian heat source with radial coordinate system and double ellipsoidal thermal heat source have been considered to develop a Finite Element Analysis (FEA) model. Validation of numerical analysis results has been confirmed through experimental investigation. Numerical modeling confirms the utilization of both heat sources, i.e. Gaussian and Double ellipsoidal for the prediction of the weld bead deposition profile. However, the numerical modeling developed using a double ellipsoidal heat source has more resembles experimental investigation due to the utilization of bead profile data. However, the Gaussian heat source is found to be suitable in the case of the unavailability of experimental data. The experimental studies show a variation in the weld bead geometry, along its welding direction due to multiple environmental factors. The microstructural investigation also shows the development of a fine columnar grain structure at the welding bead & weld penetration zone, and the grain structure became coarser at the Heat Affected Zone (HAZ). [ABSTRACT FROM AUTHOR]

Understanding the flow characteristics within the molten pool and the creation of the weld shape holds significant theoretical and engineering importance. Numerical simulation is the best method for understanding the flow behaviour in the molten pool due to its greater flexibility and cost competitiveness, which enable efficient exploration of such intricate phenomena. The dimensions of the geometry are generally crucial in determining the flow pattern in the molten pool. Marangoni convection may occur in thin plates due to changes in surface tension, while thicker plates may experience buoyancy forces from natural convection. It would be intriguing to understand the flow pattern when thin plates are treated with a smaller laser beam diameter. Three numerical models have been created in the present paper using COMSOL Multiphysics to analyse the flow characteristics within the molten pool. The first model assumes laminar flow in the molten pool, while the other two models assume turbulent flow (yPlus and k-ω model). The models incorporate a pulsed heat source that is simulated using COMSOL's events interface, which adjusts the time steps depending on the speed of the solution changes. The three models consider the temperature dependent thermo-physical properties, track the solid-liquid phase interface with keyhole, account for Marangoni convection and buoyancy forces. The melt pool shapes calculated by the models are compared with experimental results and found to be comparable. The intensity of the flow in the molten pool is determined by using dimensionless numbers like the Marangoni number and the ratio of the Marangoni number to the Grashof number. [ABSTRACT FROM AUTHOR]

In this study, research on the use of the External Magnetic Field method – Tungsten Inert Gas was done to determine the effect of welding arc compression on the quality of AISI 304 thin plate weld. The welding process was performed using autogenous welds. In this study, an external magnetic field was generated by placing a magnetic solenoid around the TIG welding torch. Enabling this electromagnetic field is done dynamically using a microcontroller. Welding parameters used are welding current 100; 105; 110 A and welding speed 1.6; 1.8; 2.05 mm/s. The results of this study showed that EMF-TIG welding can produce a more uniform bead width along the weld line with a standard deviation of 0.08 compared with conventional TIG welding of 0.12. Increased welding speed of 2.05 mm/s causes no effect on the addition of an external magnetic field to the width of the weld bead. The current parameters are 105 A with a speed of 1.6; 1.8; 2.05 mm/s resulted in compression of the top bead width by 0.87; 0.61; 0.1 mm. The welding parameters with a current of 105 A and welding speed of 1.6 mm/s have a larger upper bead compression effect of 0.84 mm compared to 110 A currents of 0.38 mm. Moreover, the D/W ratio obtained under an external magnetic field was higher than without magnetic.

Laser welding of A516 steel was performed using the filler metal to serve as the interlayer. The effects of laser welding parameters on the melt pool microstructure, weld bead shape, dimensions of the melt pool, and temperature field around the melt pool were investigated. The results indicated that the nozzle distance had the most significant influence on the temperature around the molten pool. As the maximum energy of the laser beam was irradiated on the center of the filler, the highest value of the temperature near the fusion zone was about 360°C. The welding speed, determining the overlap factor, had a considerable effect on the weld bead shape. The laser power, therefore, could determine laser beam line energy to melt the filler volume effectively. Enhancing the laser power up to 450 W increased the temperature to about 300 °C; also, the melting rate of the filler used was almost completed and the weld bead appearance was clearly improved; therefore, the width of the melt pool was increased by about 10%. Increasing the nozzle distance from 3 to 7 mm also reduced the width of the molten pool by about 75% and 40% with and without the filler, respectively. By using the filler metal, the geometry of the welding bead changed from a concave one to a convex one. The major fusion zone microstructure of the fusion zone was an acicular ferrite characterized by needle-shaped ferrite crystallites. [ABSTRACT FROM AUTHOR]

Ti-6Al-4V is a material that is extensively used in the gas turbine application. The fabrication of the assemblies of the gas turbine made up of Ti-6Al-4V involves Tungsten Inert Gas (TIG) welding. It is preferred to have a minimal bead width for minimal distortion with full penetration welds meeting the required weld quality as per aerospace weld standards. The primary purging gas which is usually employed during TIG welding is inert argon gas. In this present study argon and helium gas mixture was adopted as shielding gas. Taguchi design of experiment has been employed involving L9 orthogonal array with 3 parameters and 3 levels. The aim of the study was to understand the effect of the weld parameters and Ar-He gas mix ratio on the bead width of the welds. The obtained results were analyzed using ANOVA and found the welding current having significant effect on bead width followed by welding speed and Ar-He gas mix. A linear regression mathematical model is generated of the response variable. In order to ascertain the optimized parameters confirmation tests are conducted and obtained results are correlated. Macroscopic images do not show any crack or porosity and thereby confirm that defect free welds can be produced within the range of process parameters. [ABSTRACT FROM AUTHOR]

Metals created by melting basic metal and welding rods in welding operations are referred to as weld beads. The weld bead shape allows the observation of pores and defects such as cracks in the weld zone. Radiographic testing images are used to determine the quality of the weld zone. The extraction of only the weld bead to determine the generative pattern of the bead can help efficiently locate defects in the weld zone. However, manual extraction of the weld bead from weld images is not time and cost-effective. Efficient and rapid welding quality inspection can be conducted by automating weld bead extraction through deep learning. As a result, objectivity can be secured in the quality inspection and determination of the weld zone in the shipbuilding and offshore plant industry. This study presents a method for detecting the weld bead shape and location from the weld zone image using image preprocessing and deep learning models, and extracting the weld bead through image post-processing. In addition, to diversify the data and improve the deep learning performance, data augmentation was performed to artificially expand the image data. Contrast limited adaptive histogram equalization (CLAHE) is used as an image preprocessing method, and the bead is extracted using U-Net, a pixel-based deep learning model. Consequently, the mean intersection over union (mIoU) values are found to be 90.58% and 85.44% in the train and test experiments, respectively. Successful extraction of the bead from the radiographic testing image through post-processing is achieved. [ABSTRACT FROM AUTHOR]

The weld bead formation process is closely related to welding materials because of the thermophysical properties involved. In this paper, a 3D numerical calculation model to explore the weld bead formation process in fiber laser welding of dissimilar materials is presented. The formed welded joint during the fiber laser welding of dissimilar materials shows characteristics that are different from that during the welding of the same material. The differences in the thermophysical properties between the materials are taken into consideration in the developed model. By means of comparison with the laser welding of the same material, the corresponding formation characteristics of a dissimilar weld bead are analyzed in terms of temperature distribution, molten metal flow, and thermophysical properties based on numerical simulation results. Due to the differences in the thermophysical properties, the weld bead, the temperature field, and the flow field are asymmetric in the laser welding of dissimilar materials. The moving processes of the two sides of the molten pool are extracted and discussed. The influence of an asymmetrical flow field on both sides of the molten pool is the critical factor accounting for the generation of asymmetric behaviors in the dissimilar material fiber laser welding. The proposed method in this paper can be utilized to provide guidance for revealing the weld formation process in dissimilar laser welding and to obtain weldment with high quality in practical welding. [ABSTRACT FROM AUTHOR]

This paper focuses on selecting optimal process parameters for uniform single-layer weld bead deposition and the characterization of structures manufactured by GMAW-based wire arc additive manufacturing (WAAM) process. The results reveal that the obtained grain structures vary due to the local thermal cycle. Near to the base of the fabricated part, ferrite with pearlite structures is observed. At the same time, the observed grains become coarser along the deposition direction. Again adjacent to the final layer, finer grains are observed with ferrite and thin strips of bainite, which has been confirmed through XRD analysis. Also, the formation of different chemical compounds such as ferrite, cementite, bornite, and martensite has been identified at different layers. Investigations of surface defects using dye penetration test and corrosion behavior of the component using the weight loss method have also been conducted. The observed surface defects like cracks and porosity are primarily present in the interface of the upper layers. The measured Vickers micro-hardness is found different in different wall structures. The micro-hardness values in the flat and circular wall are recorded as 162.806 HV and 172.191 HV, respectively. [ABSTRACT FROM AUTHOR]

Welding quality detection is a critical link in modern manufacturing, and the weld bead location is a prerequisite for the high-precision assessment of welding quality. It is generally necessary for weld bead detection to be accomplished in the context of complex industrial environments. However, conventional detection and location methods based on specific detection conditions or prior knowledge lack accuracy and adaptability. To precisely detect and locate the weld beads in real industrial environments, a novel weld bead detection and location algorithm is proposed based on deep convolutional neural networks. Because there is no open data set of weld beads and the samples in real industrial applications are insufficient for effective model training of the deep convolutional neural network, a novel data augmentation method based on a deep semantic segmentation network is proposed to increase the sample diversity and enlarge the data set. Then, a dynamic sample updating strategy is put forward to cover more welding situations. Finally, faced with the weak-feature and weak-texture characteristics of weld beads, a simplified YOLOV3 model is proposed to realize end-to-end weld bead location. Experiments demonstrate that the proposed method could effectively satisfy the robustness and precision requirements for weld bead detection and location combined with a deep semantic segmentation network and simplified YOLOV3 model. [ABSTRACT FROM AUTHOR]

The lightweight titanium alloys possess good resistance to corrosion and temperature. They are used in turbine engines and aircraft structures. The strength of weld joint is dependent on thermal history in the weld zone and the weld bead geometry. The quality of weld can be improved by specifying the optimal welding parameters. Trial-and-error experimental methods are time-consuming and expensive. This paper deals with Computational Fluid Dynamics (CFD) models to carry out three-dimensional thermo-fluid analysis. Buoyancy and Marnangoni stress are incorporated. Temperature dependent properties of Ti-6Al-4V alloy and the process conditions are specified for generating the weld bead profile. The CFD model is validated initially through comparison of existing test data. Further studies are made by conducting tests on the pulsating laser welding of Ti-6Al-4V alloy. The effects of welding speed, pulse width and pulse frequency on the weld bead geometry are examined. This study confirms the adequacy of modeling and simulations of weld bead geometry with test results. [ABSTRACT FROM AUTHOR]

In this paper, the effects of the joint type on the driven-out bead of the roll-formed pipes, welded by high-frequency induction welding process are studied. The main goal is to predict and reduce the volume of the bead driven out in the weld seam. Moreover, it aims to move the semi-solid bead during welding to the outer diameter of the pipe. This study has two prior aims: to produce a defect-free joint and to improve the mechanical and metallurgical properties. In order to optimize the weld joint, various joint types have been investigated by experimental tests and simulation. Lastly, destructive tests were used to determine if the desired mechanical properties of the weld joint were obtained. The metallurgical properties and the derivation of the semi-solid material in the weld zone have both been investigated in terms of microstructure. According to the results, the proper joint type improves the mechanical properties by 5% and reduces the volume of the weld bead about 45%. [ABSTRACT FROM AUTHOR]

Weld bead detection is essential for automated welding inspection processes. The non-invasive passive techniques, such as photogrammetry, are quickly evolving to provide a 3D point cloud with submillimeter precision and spatial resolution. However, its application in weld visual inspection has not been extensively studied. The derived 3D point clouds, despite the lack of topological information, store significant information for the weld-plaque segmentation. Although the weld bead detection is being carried out over images or based on laser profiles, its characterization by means of 3D geometrical features has not been assessed. Moreover, it is possible to combine machine learning approaches and the 3D features in order to realize the full potential of the weld bead segmentation of 3D submillimeter point clouds. In this paper, the novelty is focused on the study of 3D features on real cases to identify the most relevant ones for weld bead detection on the basis of the information gain. For this novel contribution, the influence of neighborhood size for covariance matrix computation, decision tree algorithms, and split criteria are analyzed to assess the optimal results. The classification accuracy is evaluated by the degree of agreement of the classified data by the kappa index and the area under the receiver operating characteristic (ROC) curve. The experimental results show that the proposed novel methodology performs better than 0.85 for the kappa index and better than 0.95 for ROC area.

Manufacturing industries intend superior quality production maintaining economy with improved productivity. Components used in engineering applications are desired to have high strength and corrosion resistance for long run reliable performance. Weld cladding is commonly employed on a corrosion prone material with a goal to achieve longer service life of the same. For doing weld cladding, obtaining favourable bead geometry is important, and heat input is expected to play a vital role. In this work, bead-on-plate experiments are first done on low carbon steel substrate using 316 austenitic stainless steel filler employing pulsed GTAW or TIG process. Three-variable Box-Behnken unblocked design of experiment of Response Surface Methodology is resorted to for setting experimental runs. Evaluating appropriate process parameters from this set of experiments, experimental conditions of cladding are set. Corrosion test shows that at 150A welding current, 33Hz welding pulse frequency and 150 mm/min torch travel speed with 0.594 kJ/mm heat input, corrosion rate becomes minimum, and therefore, this parametric combination may be recommended to adopt. [ABSTRACT FROM AUTHOR]

Metals created by melting basic metal and welding rods in welding operations are referred to as weld beads. The weld bead shape allows the observation of pores and defects such as cracks in the weld zone. Radiographic testing images are used to determine the quality of the weld zone. The extraction of only the weld bead to determine the generative pattern of the bead can help efficiently locate defects in the weld zone. However, manual extraction of the weld bead from weld images is not time and cost-effective. Efficient and rapid welding quality inspection can be conducted by automating weld bead extraction through deep learning. As a result, objectivity can be secured in the quality inspection and determination of the weld zone in the shipbuilding and offshore plant industry. This study presents a method for detecting the weld bead shape and location from the weld zone image using image preprocessing and deep learning models, and extracting the weld bead through image post-processing. In addition, to diversify the data and improve the deep learning performance, data augmentation was performed to artificially expand the image data. Contrast limited adaptive histogram equalization (CLAHE) is used as an image preprocessing method, and the bead is extracted using U-Net, a pixel-based deep learning model. Consequently, the mean intersection over union (mIoU) values are found to be 90.58% and 85.44% in the train and test experiments, respectively. Successful extraction of the bead from the radiographic testing image through post-processing is achieved.

Abstract China Low Activation Martensitic (CLAM) steel was one of the Reduced-Activation Ferritic/Martensitic (RAFM) steels which were widely considered as the candidate structural metal for the new generation nuclear facilities. Flowing Pb-Bi corrosion tests of CLAM steel weld bead with Tungsten Inert Gas (TIG) welding were conducted in oxygen-saturated liquid Pb-Bi eutectic alloy at 550 °C for 500 h, 1000 h and 1500 h to study the effect of different flow velocities (1.70 m/s, 2.31 m/s and 2.98 m/s) and corrosion time on corrosion behavior. The results indicate that the double-layer oxide films consist of Fe 3 O 4 outer layer and (Fe,Cr) 3 O 4 inner layer on the surface of all samples. With the increase of relative flow velocity, the dissolution and migration of elements are faster, which leads the degree of corrosion to become more severe. In the corrosion process of CLAM steel weld bead in flowing liquid Pb-Bi, the oxidation-corrosion and erosion have happened. Meanwhile, the degree of erosion on the surface of weld bead samples was more serious than that of the base metal samples. The experiment has obtained the function between corrosion time and oxide layer thickness of CLAM base metal and weld bead in Pb-Bi with 2.98 m/s. [ABSTRACT FROM AUTHOR]