Showing papers on "Welding published in 2021"

Effect of welding thermal treatment on the microstructure and mechanical properties of nickel-based superalloy fabricated by selective laser melting

Abstract: Strengthening the heat-affected zone in actual weld joint of Inconel 625 alloys was crucial for engineering applications in aerospace, petroleum, nuclear energy, and marine industries. In this study, Gleeble thermal simulations were employed to investigate the influence of welding heat on grain morphology, carbide evolution, and mechanical properties of Inconel 625 alloys manufactured by selective laser melting (SLM). Typical elongated columnar grains along the building direction and parallel columnar array with obvious layer boundaries along the scanning direction were observed in SLM-processed Inconel 625 alloys with hot isostatic processing treatment. After Gleeble thermal simulations, the prominent equiaxed grains appeared in the entire gauge region that was directly exposed to the simulated welding heat owing to the recrystallization process. Meanwhile, the size of equiaxed grains significantly increased with the increasing peak temperature. Particularly, only MC-type carbides were observed in the γ-Ni matrix before and after the thermal simulation owing to the fairly rapid heating (100 °C/s) and cooling (30 °C/s) process. Simulated welding heat produced a negative effect on the mechanical properties of the Inconel 625 alloys, especially for the peak temperature of 1350 °C. Then, the respective contributions of the four conventional strengthening mechanisms were successfully quantified for understanding the effect of the simulated peak temperature on the yield strength of the Inconel 625 alloys. Finally, the observations of microstructural evolution indicated that the cross-slip of dislocations was the dominant deformation mechanism to account for the large strain-hardening rate of the Inconel 625 alloys.

Digital Twin for Human-Robot Interactive Welding and Welder Behavior Analysis

Abstract: This paper presents an innovative investigation on prototyping a digital twin (DT) as the platform for human-robot interactive welding and welder behavior analysis. This human-robot interaction (HRI) working style helps to enhance human users' operational productivity and comfort; while data-driven welder behavior analysis benefits to further novice welder training. This HRI system includes three modules: 1) a human user who demonstrates the welding operations offsite with her/his operations recorded by the motion-tracked handles; 2) a robot that executes the demonstrated welding operations to complete the physical welding tasks onsite; 3) a DT system that is developed based on virtual reality (VR) as a digital replica of the physical human-robot interactive welding environment. The DT system bridges a human user and robot through a bi-directional information flow: a) transmitting demonstrated welding operations in VR to the robot in the physical environment; b) displaying the physical welding scenes to human users in VR. Compared to existing DT systems reported in the literatures, the developed one provides better capability in engaging human users in interacting with welding scenes, through an augmented VR. To verify the effectiveness, six welders, skilled with certain manual welding training and unskilled without any training, tested the system by completing the same welding job; three skilled welders produce satisfied welded workpieces, while the other three unskilled do not. A data-driven approach as a combination of fast Fourier transform (FFT), principal component analysis (PCA), and support vector machine (SVM) is developed to analyze their behaviors. Given an operation sequence, i.e., motion speed sequence of the welding torch, frequency features are firstly extracted by FFT and then reduced in dimension through PCA, which are finally routed into SVM for classification. The trained model demonstrates a 94.44% classification accuracy in the testing dataset. The successful pattern recognition in skilled welder operations should benefit to accelerate novice welder training.

Modeling and numerical study of keyhole-induced porosity formation in laser beam oscillating welding of 5A06 aluminum alloy

Abstract: This paper presents a numerical framework of keyhole-induced porosity formation and methods to suppress porosity in laser beam oscillating welding. Circular and infinity oscillating paths with amplitude of 2 mm and frequencies of 100 Hz and 200 Hz were used. A numerical model for multiple phases, including solid metal, liquid metal and shielding gas is presented using the commercial software FLUENT. An adaptive rotated Gaussian volumetric heat source was developed for analysis of the heat input and temperature distribution during laser oscillating welding. The mechanism of porosity formation caused by keyhole collapse is studied by means of numerical analysis and experiments, and compared to conventional laser welding without oscillation. The numerical simulations were in good agreement with the experimental results. It can be concluded that upon the use of oscillation during welding, porosity decreased and was fully inhibited when using infinity-oscillating path with a frequency of 200 Hz. The developed multi-physics model aids in understanding the dynamics characteristics and keyhole-induced porosity formation during laser beam oscillating welding of 5A06 aluminum alloy.

Impact of process parameters of resistance spot welding on mechanical properties and micro hardness of stainless steel 304 weldments

Abstract: Resistance spot welding (RSW) is an essential process in the automobile sector to join the components. The steel is the principal material utilized in car generation because of its high obstruction against erosion, toughness, ease of support and its recuperation potential. Due to this, it was planned to study the mechanical properties, hardness and microstructure characteristics of RSW of Stainless steel 304.,In the present research, RSW of 304 stainless steel plates with 1 mm thickness and effect of current intensity, welding time, electrode pressure and holding time on nugget diameter, tensile strength microhardness and microstructure of the joints was investigated. The specimens were prepared according to the dimensions of 30 × 100 mm with 30 mm overlaps joint through the RSW machine. The tensile test of the specimen was carried out on a universal testing machine and microhardness of specimens measured using Vickers’s hardness tester. Taguchi L16 orthogonal array was used to scrutinize the significant parameters for each output.,It has been observed that the tensile strength of the specimen is affected by the current intensity and nugget diameter, and the weld time has a significant effect on the tensile strength. Microhardness is highly influenced by electrode pressure and holding time, as the increase in both these parameters resulted in the increase of microhardness. This is due to rapid cooling, which is done by the cooling water flowing through the copper electrodes.,This study was carried out using a copper electrode with a flat face with selected parameters and response factors. The study can be useful for researchers working on optimization of welding parameters on stainless steel.

A new fine-tuned random vector functional link model using Hunger games search optimizer for modeling friction stir welding process of polymeric materials

Abstract: Modeling of manufacturing processes using artificial intelligence-based techniques has recently received considerable attention. The current investigation introduces a new artificial intelligence-based predictive model for friction stir welding of dissimilar polymeric materials. The welded joint is made of acrylonitrile butadiene styrene (ABS) and polycarbonate (PC) sheets. The proposed model is used to correlate the joint characteristics (tensile strength, joint efficiency and extensibility) with the welding variables (rotational tool speed, welding speed and tool tilt angle). The model consists of a random vector functional link (RVFL) model optimized by Hunger games search (HGS) optimizer. HGS optimizer is utilized to find out the optimal internal parameters of RVFL that boost the model accuracy. The proposed model is compared with two other optimized models in which RVFL is integrated with the sine cosine algorithm (SCA) or ecosystem-based optimizer (AEO). RVFL-HGS outperformed RVFL-SCA and RVFL-AEO based on different statistical measures. For all investigated joint characteristics, the determination coefficient ranges between 0.907 and 0.993 for RVFL-HGS, 0.869 and 0.972 for RVFL-AEO, 0.829 and 0.983 for RVFL-SCA.

Improvement on Mechanical Properties of Submerged Friction Stir Joining of Dissimilar Tailor Welded Aluminum Blanks

Abstract: Friction stir welding is a solid-state welding method that produces joints with superior mechanical and metallurgical properties. However, the negative effects of the thermal cycle during welding dent the mechanical performance of the weld joint. Hence, in this research study, the joining of aluminum tailor welded blanks by friction stir welding is carried out in underwater conditions by varying the welding parameters. The tensile tests revealed that the underwater welded samples showed better results when compared to the air welded samples. Maximum tensile strength of 229.83 MPa was obtained at 1000 rpm, 36 mm/min. The improved tensile strength of the underwater welded samples was credited to the suppression of the precipitation of the secondary precipitates due to the cooling action provided by the water. The lowest hardness of 72 HV was obtained at the edge of the stir zone which indicated the weakest region in the weld zone.

Comparison of the Weldability of AA6061-T6 Joint under Different Friction Stir Welding Conditions

Abstract: In this work, similar butt joints of AA6061-T6 alloy prepared by underwater friction stir welding (UWFSW) and friction stir vibration welding (FSVW) processes were examined. The characteristics of joints were compared with the joints obtained by conventional friction stir welding (CFSW). The different kinds of microstructural modifications that occurred during CFSW, FSVW, and UWFSW processes were analyzed. The results are employed to analyze the different behaviors in strength, ductility, weldability, and hardness of the joints in different processes at different traverse speeds, rotational speeds, and vibration frequency. It was found that mechanical vibration decreases the grain size in the weld zone and hinders the coalescence and regrowth of the precipitates during FSVW. On the other hand, water significantly decreased the joint temperature during UWFSW and led to a refined microstructure in the stir zone, which substantially improved the mechanical properties of the welded joint. The synergetic effect of refined grains and lower dissolution of β″ precipitates (higher evolution of β″ to β′ and β-Mg2Si) due to fast cooling rate (intensified local deformation) led to higher hardness in UWFSW (FSVW) joint compared to CFSW joint. The small size with the uniform distribution of dimples indicated the combination of strength and ductility in FSVW-ed and UWFSW-ed joints. The simple and highly effective welding procedure, FSVW, can be readily scaled up for industrial welding applications.

An automatic welding defect location algorithm based on deep learning

Abstract: Welding production has a pivotal role in the modern manufacturing industry. However, welding defects are frequently generated during the complex welding production process which will bring a certain effect to the welding quality. Therefore, the issue of welding defect detection has received considerable critical attention. However, traditional methods, based on handcrafted features or shallow-learning techniques could only detect welding defects under specific detection conditions or priori knowledge. In this paper, to serve the evaluation of the harmfulness of welding defects to different objects, based on the strong feature expression ability of deep learning, an automatic welding defect location method is proposed based on the improved U-net network from digital X-ray images which includes data augmentation and welding defect location. To acquire better location performance, the data augmentation is realized to enlarge the data set of welding defects to serve the network training. On the basis, a defect location method based on the improved U-net network is proposed to realize automatic and high-precision welding defect location. Experiments show that the proposed method could acquire the detection precision up to 88.4 % on the public data set (GDXray Set) which shows a remarkable location performance compared with other related detection methods.

Modeling ultrasonic welding of polymers using an optimized artificial intelligence model using a gradient-based optimizer

Abstract: In this study, a new hybrid artificial intelligence approach is proposed to model the ultrasonic welding of a polymeric material blend. The proposed approach is composed of an ensemble random vector functional link model (ERVFL) integrated with a gradient-based optimizer (GBO). First, welding experiments were conducted on acrylonitrile butadiene styrene (ABS) and polycarbonate (PC) blends produced by the injection molding method. The experiments were designed according to the L27 orthogonal array considering three process factors (applied pressure, welding time, and vibration amplitude) and two responses (average temperature and joint strength). Then, the obtained experimental data were used to train the developed model. To verify the accuracy of the model, it was compared with standalone ERVFL in addition to two fine-tuned ERVFL models (ERVFL-SCA and ERVFL-MRFO) in which ERVFL is incorporated with sine cosine algorithm (SCA) or Manta ray foraging optimization (MRFO). The four models were evaluated using five statistical tools. ERVFL-GBO has the highest coefficient of determination and the lowest root mean square error, mean relative error mean absolute error, and coefficient of variance compared with other models which indicate its high accuracy over other tested models.

Welding and processing of metallic materials by using friction stir technique: A review

Abstract: Friction stir technique played a vital role in recent industries as it has been utilized in welding and processing of metallic materials. Friction stir welding (FSW) is applied for joining the poorly weldable materials and enhancing the microstructure and the mechanical properties of the welded joints. Friction stir spot welding (FSSW), underwater friction stir welding (UFSW) and vertical compensation friction stir welding (VCFSW) are variants of FSW process. On the other hand, friction stir processing (FSP) is another method, whose basic principal originated from friction stirring technique, which can be utilized for manipulating the base materials by performing dynamic recrystallization on grains resulting in superior properties of the processed material. Friction stir alloying (FSA) is analogous to FSP with implanted reinforcement particles, producing surface composites. Like other fusion welding techniques, FSW process has its own defects which especially characterize the friction stir welded joints. Tunnels, voids, flash, lack of penetration, kissing bond and surface grooving are the common defects of FSW method. Since friction stirring action generates both thermal and mechanical loads beside the higher plastic deformation, finite element modeling (FEM) has been used in model and simulation this of the process. A few research gaps are pin pointed and some research recommendations are included.