Specific Welding Research Articles

Enhancing resistance welding strength of carbon fiber/polycaprolactam thermoplastic composites through coupling agent‐modified metal heating elements

AbstractThis study investigates the impact of coupling agent‐modified resistance welding heating elements on the welding strength of carbon fiber‐reinforced polyamide 6 (PA6) thermoplastic composites. Silane coupling agent KH550 and titanate coupling agent UP‐201 were used to modify heating elements made of 304 stainless steel mesh. Optimal modification parameters for KH550‐modified stainless steel mesh (SSM) were determined using orthogonal experiments, resulting in maximum single lap shear strength (LSS) of 17.05 MPa under specific welding conditions. Comparative tests demonstrated the enhancement effect of UP‐201‐modified SSM on LSS. Additionally, two experimental schemes were used to study the synergistic enhancement effect of KH550 and UP‐201 on the LSS of resistance welded joints. The findings indicate that both coupling agents improve the welding strength, with significant differences in their individual and combined effects.Highlights This study explores carbon fiber reinforced Nylon 6, an innovative thermoplastic. Analyzed effects of modified heating elements on joint shear strength. Examined silane and titanate coupling agents' effects on weld joint shear strength. Investigated synergistic effects of silane and titanate agents on weld joint strength. Investigated the synergistic enhancement mechanism of silane and titanate agents.

Determination of the load acting on the probe by separating force and torque during FSW of AA 6060 T66

AbstractThe development of suitable welding processes is required to meet the ever-increasing demands on joining processes, particularly for lightweight construction and increasing environmental awareness. Friction stir welding (FSW) represents a promising alternative to conventional fusion welding processes, particularly for the joining of low-melting-point materials such as aluminium and magnesium alloys, which present a number of challenges, including the formation of pores and the occurrence of hot cracks. The central element of the process is the friction stir welding tool, which consists of a shoulder and a probe. The rotation and the simultaneous application of pressure during the joining process create a friction-based heat input through the tool. The excellent mechanical properties resulting from dynamic recrystallisation during the welding process are a major advantage of the process. As a result, strengths comparable to those of the base material can be achieved. However, FSW is subject to process-specific challenges, including high process forces, which result in the fabrication of complex and robust devices. Additionally, high dynamic loads on the friction stir welding tools must be considered. In many cases, the design of friction stir welding tools is based on empirical data. However, these empirical values are machine-, component- and material-specific, which often results in under- or overmatching of friction stir welding tools. Sudden probe failure, component scrap, and low process reliability are the direct consequences of undermatching. Overmatching results in enlarged tools with limited accessibility, high heat input, and high process forces, leading to component deformation. The aim of this study is to determine the load on the probe by separating the forces and torque of the shoulder and the probe in order to be able to make statements about the load acting on the probe and the resulting stress state. The knowledge of the stress state can be employed to design friction stir welding tools, both statically and dynamically, for a specific welding task. A strategy was devised to distribute the load exerted on the shoulder and probe. To this end, the length of the probe was gradually reduced between the welding tests. The investigations were carried out with a force-controlled robotized welding setup in which AA 6060 T66 sheets with a thickness of 5 mm were welded. A Kistler multicomponent dynamometer type 9139AA allows to measure the Cartesian forces to be recorded in the x-, y-, and z-directions with a sampling rate of 80 kHz. The weld seam properties were determined by visual and metallographic inspections as well as tensile and bending tests in accordance with DIN EN ISO 25239–5.

Effect of Different Welding Modes on Morphology and Property of SS316L Stainless Steel Deposition by Robotic Metal-Inert Gas Welding.

The widespread adoption of arc additive manufacturing techniques across various industries has advanced the field of SS316L stainless steel manufacturing. It is crucial to acknowledge that different welding modes exert distinct influences on the forming and mechanical performance. This study analyzed the thermal input associated with four specific welding modes in LORCH MIG welding, clarifying the transition dynamics of molten droplets through waveform analysis and examining the resultant effects on microstructure and performance characteristics. The Pulse, Speed-Pulse-XT, and Twin-Pulse modes were found to induce spatter during the manufacturing process, consequently reducing molding efficiency in comparison to the SA-XT mode. Notably, the Twin-Pulse mode, characterized by double-pulse agitation, generated fish scale patterns along the lateral surfaces of the fabricated parts, promoting anisotropic grain growth. This microstructural refinement, compared to single-pulse samples with equivalent thermal input, resulted in enhanced mechanical properties. Nevertheless, the horizontal tensile strength of the three pulse modes was lower than the industrial standard for SA-XT mode and forging. In contrast, the SA-XT mode with an average hardness of 168.1 ± 6.9 HV and a tensile strength of 443.58 ± 5.7 MPa. Therefore, while three pulse modes offer certain microstructural advantages, the SA-XT mode demonstrates superior overall performance.

Research Progress on the Microstructure Evolution Mechanisms of Al-Mg Alloys by Severe Plastic Deformation.

Al-Mg alloys are widely used as important engineering structural materials in aerospace engineering, transportation systems, and structural constructions due to their low density, high specific strength, corrosion resistance, welding capability, fatigue strength, and cost-effectiveness. However, the conventional Al-Mg alloys can no longer fully satisfy the demands of practical production due to difficulties caused by many defects. The high strength of Al-Mg alloys as non-heat treatment precipitation-strengthened alloys is achieved primarily by solid solution strengthening along with work hardening rather than precipitation strengthening. Therefore, severe plastic deformation (SPD) techniques can be often used to produce ultrafine-grained structures to fabricate ultra-high strength aluminum alloys. However, this approach often achieves the strengthening of material at the cost of reduced ductility. This paper comprehensively summarizes the various approaches of ultrafine/nanocrystalline materials for enhancing their plasticity, elaborates on the creation of a bimodal microstructure within the alloy, and discusses the formation of a nanotwin microstructure within the alloy and the incorporation of dispersed nanoparticles. The mechanisms underlying both the strengthening and toughening during large plastic deformation in aluminum alloys are summarized, and the future research direction of high-performance ultrafine crystalline and nanocrystalline Al-Mg aluminum alloys is prospected.

Influence of welding parameters on the formation of U-rib fillet welds in single-sided welding of steel bridge panels

It is difficult to reach the requirement of 80% U-rib weld penetration in the practical application of single-sided welding of steel bridge panels. Meanwhile, full penetration has to be avoided. In this study, efforts are made in order to achieve fillet welds with 80% and above penetration consistently. The effect of welding parameters on the formation of single-sided U-rib fillet welds is investigated. This work has revealed that U-rib fillet weld penetration is presenting an upward trend as the welding current increases under specific welding conditions, and full penetration was observed at the current of 325 A. With the increasing distance between the filler wire and the root of the weld, the U-rib penetration reduces. As the torch angle increases during ship position welding, the U-rib penetration will first increase and then decrease. The weld formation of flat position welding is more stable and the U-rib weld penetration has been significantly improved. The optimum U-rib weld with penetration of 84.8% is achieved under the conditions of flat position welding with 285 A welding current, 44∘ torch angle, and 2[Formula: see text]mm distance between the wire and the root.

Development of a fuzzy logic model for predicting the quality of micro friction stir spot welding (µFSSW) using particle swarm optimization

Micro Friction Stir Spot Welding (µFSSW) is crucial in microelectronics and precision manufacturing. It requires a comprehensive understanding of the complex connections between various parameters to achieve the highest quality welds. This study aims to improve the prediction of µFSSW weld quality by incorporating advanced optimization techniques. Fuzzy Logic Optimization is used to model uncertainties, and Particle Swarm Optimization (PSO) is employed to fine-tune parameters for improved accuracy. The fuzzy logic system utilizes Gaussian functions as membership functions, organized with nine rule bases. The results clearly demonstrate that the fuzzy logic model greatly enhances accuracy when combined with Particle Swarm Optimization. The refined model improves precision for pin diameter, shoulder diameter, Thermo-Mechanically Affected Zone (TMAZ) area, and cross-tensile strength. The PSO-optimized model shows lower accuracy in predicting plunge depth and shear tensile strength. The ongoing decline in Root Mean Square Error (RMSE) values highlights the complexity of the results. The optimization significantly improves the model’s ability to predict specific weld quality metrics, as demonstrated by the pin diameter’s reduced RMSE value of 0.07. The collective results showcase an optimized Fuzzy Logic System (FLS) model adept at accurately predicting µFSSW weld quality, demonstrating adaptability across diverse conditions. The discernible increase in accuracy, reaching up to 76 % following the optimization of the fuzzy logic model with PSO, serves as a testament to the efficacy of the employed methodologies in advancing the precision and reliability of µFSSW weld quality predictions

Improving the weld seam quality in laser welding processes by means of Bayesian optimization

The determination of appropriate process parameters is crucial for the development of laser welding processes. This usually requires extensive and time-consuming experimentation combined with expert knowledge. To reduce the number of experiments required to determine appropriate process parameters, Bayesian optimization was used in this work. Bead on plate laser welding of AA5754 samples was performed while optimizing the laser power, the welding speed, the focus position and the power distribution in the core-ring fiber laser system with the objective of achieving welds with a specific weld depth and low number of defects at high welding speeds. The welds were evaluated using X-ray imaging and height measurements. A cost function was developed to quantify the overall weld quality based on the weld properties. It is demonstrated that the Bayesian optimizer can determine appropriate process parameters for the given objective, based on a cost function, within a comparatively small number of 29 experiments.

An embedded machine learning strategy for analyzing interfacial characteristics in impact welding of dissimilar alloys

ABSTRACT In this study, a novel embedded machine learning (ML) framework was employed to analyze interfacial characteristics in dissimilar-alloy impact welding. While keeping the substrates constant, i.e., stainless steel and Ti alloy, the influence of interlayer types on the resultant morphology of impact-welded bonds was also highlighted. The dataset was meticulously generated through numerical simulations, utilizing materials properties and processing parameters as primary inputs. These inputs were used to predict various interfacial features, including the extent of wave formation (L), specific welding zone area (S), average waveform amplitude (A), and maximum interface temperature (Tmax). Impressively, exceptional predictive capabilities were demonstrated by the ML model, with determination coefficients of 0.938, 0.924, 0.967, and 0.909 for S, L, A, and Tmax, respectively. Based on the results, strong dependencies between interfacial features and the weight functions of materials properties and processing parameters were unveiled. Intriguingly, as the weight function of materials properties for a specific output objective increased, a corresponding decrease in the weight function of processing parameter inputs was observed. These findings emphasize the complex connection between input parameters and output characteristics, revealing previously unexplored intricacies in the impact welding process, facilitated by our advanced ML methodology.

Experimental and Numerical Analysis of Fracture Mechanics Behavior of Heterogeneous Zones in S690QL1 Grade High Strength Steel (HSS) Welded Joint.

The heterogeneity of welded joints' microstructure affects their mechanical properties, which can vary significantly in relation to specific weld zones. Given the dimensional limitations of the available test volumes of such material zones, the determination of mechanical properties presents a certain challenge. The paper investigates X welded joint of S690QL1 grade high strength steel (HSS), welded with slightly overmatching filler metal. The experimental work is focused on tensile testing to obtain stress-strain properties, as well as fracture mechanics testing. Considering the aforementioned limitations of the material test volume, tensile testing is carried out with mini tensile specimens (MTS), determining stress-strain curves for each characteristic weld zone. Fracture mechanical testing is carried out to determine the fracture toughness using the characteristic parameters. The experimental investigation is carried out using the single edge notch bend (SENB) specimens located in several characteristic welded joint zones: base metal (BM), heat affected zone (HAZ), and weld metal (WM). Fractographic analysis provides deeper insight into crack behavior in relation to specific weld zones. The numerical simulations are carried out in order to describe the fracture behavior of SENB specimens. Damage initiation and evolution is simulated using the ductile damage material behavior. This paper demonstrates the possibility of experimental and numerical determination of fracture mechanics behavior of characteristic heterogeneous welded joint zones and their influence on crack path growth.

In-service property prediction of X80 self-shielded flux-cored (FCAW) girth weld metal based on strain ageing kinetics

ABSTRACT In order to achieve in-service safety evaluation of girth weld, property prediction of X80 self-shielded flux-cored girth weld metal based on strain ageing kinetics was conducted. Strength increment (ΔY) and toughness reduction (ΔCVN) relationship was obtained through tensile and Charpy impact test using specimens before and after strain ageing. 1% prestrain was conducted and then aged at different conditions (150°C/100°C for 1–6 h, 80°C/50°C for 1–120 days). Strain ageing kinetics was investigated based on experiment results, and diffusion activation energy (Q) was calculated as 88.1 kJ/mol. The correlation equation of ageing temperature and period was obtained: , T 1<T 2 <423 K. It is predicted that, since occurrence of 1% prestrain, toughness of girth weld metal would drop down to <80 J after 3.9 years service in the present case, and its service safety should be reconsidered. Other prediction should be based on experimental results of specific consumable and welding parameters.

Intelligent and Adaptive System for Welding Process Automation in T-Shaped Joints

The automation of welding processes requires the use of automated systems and equipment, in many cases industrial robotic systems, to carry out welding processes that previously required human intervention. Automation in the industry offers numerous advantages, such as increased efficiency and productivity, cost reduction, improved product quality, increased flexibility and safety, and greater adaptability of companies to market changes. The field of welding automation is currently undergoing a period of profound change due to a combination of technological, regulatory, and economic factors worldwide. Nowadays, the most relevant aspect of the welding industry is meeting customer requirements by satisfying their needs. To achieve this, the automation of the welding process through sensors and control algorithms ensures the quality of the parts and prevents errors, such as porosity, unfused areas, deformations, and excessive heat. This paper proposes an intelligent and adaptive system based on the measurement of welding joints using laser scanning and the subsequent analysis of the obtained point cloud to adapt welding trajectories. This study focuses on the optimization of T-joints under specific welding conditions and is intended as an initial implementation of the algorithm, thus establishing a basis to be worked on further for a broader welding application.

Surrogate model development using simulation data to predict weld residual stress: A case study based on the NeT-TG1 benchmark

Surrogate models have been used as a substitute for traditional engineering simulation to get target simulation results faster. These surrogate models usually need sufficient data to fulfill their optimum performance. However, the acquisition of high quality datasets for cases like welding residual stress is both difficult and costly. This work developed a workflow that can rapidly develop machine learning based surrogate models to predict post-weld residual stresses using finite element (FE) simulation data. The workflow comprises five steps: (i) baseline FE model creation, (ii) FE model processing (iii) scripting (iv) data generation and (v) surrogate model development. The workflow presented in the paper automates all the steps involved in generating, calculating, and extracting results from FE simulations, with the aim of reducing the time and cost required for data acquisition. In addition, to minimize the possibility of FE analyses not completing successfully during the automated data generation process and to ensure that the training data can be obtained without any manual intervention, the FE model was simplified without significant loss of accuracy. A single weld bead-on-plate structure, taken from the NeT project Task Group 1, was used as a case study to validate the developed workflow. The overall aim of this work was to validate the feasibility of developing a surrogate model for specific welding conditions with no experimental dataset and with the aid of simulations, and to investigate the predictive performance of an Artificial Neural Network based surrogate model. Four welding parameters, arc advance speed, net energy input rate, arc travel length and position, were considered as the input for the surrogate model to predict the longitudinal stress along the line running across the sample thickness in the mid-length of the specimen. The relative root mean square error of the predictive surrogate model for the test dataset was 0.0024. Residual stress prediction based on the NeT-TG1 benchmark welding case met the R6 validation criteria and the developed model generates predictions that are in line with other methods used by contributors to the NeT-TG1 round robin.

Design performance optimization of laser beam welded joints made for vehicle chassis application using deep neural network-based Krill Herd method

The welding to be performed must be defect free, offer lower residual stress and strain, be compact in size and withstand different load conditions. However, the existing investigations in this scenario are still not modernized. Therefore, in this study, a specific welding method called laser beam welding (LBW) is performed and different weld parameters have been inspected and analysed. Advanced instruments based on the non-destructive (ND) are implemented to find the variable LBW responses such as weld bead defects, residuals and strain. The experimentation has been designed using Design Expert software, response surface methodology (RSM) and Box Behnken design (BBD) and verified by analysis of variance (ANOVA) analysis and FIT statistics. Moreover, a hybrid deep neural network-based Krill Herd optimization (DNN-KHO) is implemented to predict the output parameters like, undercut (µm), overlap (µm), total strain (mm/mm) and residual stress (MPa) during welding. The proposed DNN-KHO was also used to optimize LBW input parameters such as, peak power (W), weld speed (mm/s), gas flow rate (l/min) and beam diameter (µm) simultaneously. Predictions show that the proposed DNN-KHO algorithm outperformed by 21.53%, 45.428% and 41.31% higher in accuracy compared to respective hybrid random forest based grey wolf method (RF-GWO), RF and DNN predictions.

Effect of heat-input and post-weld heat treatment on residual stress and deformation of butt welded joint made by structural carbon steel A516 grade 70

Residual stress and welding deformation are unfavorable factors and always exist in the welded joint. They seriously reduce the working capacity of the welded structure, and at the same time disfigure the aesthetics of the welded structure. Therefore, knowing the influence of the welding parameter, specifically the heat-input, on the residual stress and deformation of the welded joint is of great significance, helping to determine the appropriate welding parameter for each specific welding joint. Welding deformation can be easily measured with common measuring instruments, but determining the residual stress in the welded joint by experiment encounters many difficulties, especially in the interior of the material, so this study proposes a solution to determine the residual stress as well as welding deformation by numerical simulation using SYSWELD software with the least cost and fastest time. The research results show that when welding with a higher heat-input, the residual stress in the welded joint will be greater, causing the longitudinal and transverse deformations to be larger. For a butt welding joint made of structural carbon steel A516 grade 70 with 16 mm thickness, it is necessary to weld 4 passes with the heat-input respectively for the minimum residual stress and deformation are q1=2252 J/mm, q2=2828 J/mm, q3=2458 J/mm, and q4=2878 J/mm. The post-weld heat treatment immediately after welding at 595 oC for 40 minutes will reduce residual stress 3.45 times, reduce longitudinal deformation 2.82 times, reduce transverse deformation 1.54 times, and reduce the angular deformation 1.32 times.

Параметры режима ультразвуковой сварки полимерных материалов

It was established that the mode parameters determining efficiency of the plastics ultrasonic welding process include waveguide oscillation amplitude, welding static pressure, oscillation frequency, welding time and size of the fixed gap between the waveguide working end and the support. In each specific welding mode, an increase in the gap of more optimal value leads to a decrease in the breaking load; this is especially true in welding products of higher thickness. Welding could be performed on surfaces coated with various products. An expression was obtained to determine time and intensity coefficients of the ultrasonic welding mode. Fixture supports influence on quality and strength of the welded joints in the ultrasonic welding of polymeric materials was considered. When using an active support, strength and productivity of ultrasonic welding was much higher than in using the passive supports. Results of the experiments confirmed the relationship between the support oscillations amplitude and the heating, thickness and properties of the parts to be welded. A working cycle of the plastics ultrasonic welding is proposed, where the static welding pressure and the ultrasonic pulse time are ensuring maximum strength and tightness of the welded joint.

Development of an Artificial Intelligence-Based System for Predicting Weld Bead Geometry

The prediction of the weld bead geometry parameters is an important aspect of welding processes due to it is related to the strength of the welded joint. This research focuses on using statistical design techniques and a deep learning neural network to predict the weld bead shape parameters of shielded metal arc welding (SMAW), metal inert gas (MIG), and tungsten inert gas (TIG) welding processes. With the statistical design techniques, experiments were carried out to obtain the data for generating the regression models. Establishing mathematical models that shows the relationship between welding process parameters and weld bead size is significant for practical applications. The mathematical model enables the determination of the weld bead size when setting specific welding process parameters. In this research, experimental research results were obtained to build mathematical models showing the relationship between welding process parameters and weld bead geometries for SMAW, MIG, and TIG welding processes. The research results serve as the basis for establishing predictive systems or optimizing welding process parameters. With deep learning neural network techniques, we developed an artificial intelligence-based system for predicting complicated relations between the welding process parameters and the weld bead size. Both a regression model and the deep learning model result in a good correlation between the welding process parameters and the weld bead geometry.

Justification of the use of multifunctional assembly and welding manipulators at shipyards

This article substantiates the possibility of increasing the level of mechanization of assembly and welding production of shipyards by introducing multifunctional complex mechanized assembly and welding manipulators. The idea is substantiated that such a manipulator, initially having relatively small functionality, but the projected possibility of a gradual increase and complication of functions, can contribute to an increase in the level of mechanization of production, up to its complete robotization. It is concluded that the creation of such equipment already at this stage will increase the flexibility of assembly and welding production, due to readjustability (like a robot) and, most importantly, adaptability to continuous phased modernization, which in the final stage will lead to the creation of a full-fledged industrial robot to perform specific assembly and welding work.

SUBMERGED ARC WELDING AND MELTING TECHNOLOGY

The article dedicated "Submerged arc welding and meltıng technology" method, which is one of the technologies for repairing friction-worn parts of oil and gas equipment. The efficiency, technological regime,advantages of the process are noted, and it is also shown that this method is used today in the oil industry and shipbuilding. With these methods, the repairing of a part of the pieces corroded by friction, as well as the possibility of welding steel plates of different grades and thicknesses, was reflected. In determining welding procedures for certain applications, the welding engineer must first take into account the specific welding qualities required, since in practice, although some factors can be determined quite accurately, others change during manufacturing process. Arc voltage, electrode selection, welding speed are important considerations in determining operating mode, and the penetration caused by high voltage combined with low speed is indicated accordingly. The supply of agent (glycerin) to the recently created attachment zone is indicated. It reduces the thermal impact of the cooling arc on the half and will increase the cooling rate of the deposited and metal, decreasing the deformation and self-heating of the adjacent elements of the half. Besides, the liquid serves to safeguard the liquid metal from the harmful effects of chemical element and gas. Keywords: overheating, vibration, crack, arc voltage, welding defects, welding speed, technical glycerin, soda ash.

A novel seam extraction and path planning method for robotic welding of medium-thickness plate structural parts based on 3D vision

Currently, the robotic welding of medium-thickness plate structural parts has become a common welding application. With the rapid development of automation technology and robotics, the traditional teaching-playback mode and the off-line programming mode cannot meet the automation demand of welding robots. To realize automatic seam extraction and path planning for robotic welding of medium-thickness plate structural parts without programming and teaching, we use three models of medium-thickness plate structural parts as the research objects to propose a novel seam extraction and path planning method for robotic welding of medium-thickness plate structural parts based on 3D vision. Firstly, a set of improved RANSAC multiplanes fitting algorithms is proposed to accurately obtain the position of the intersection lines between the intersecting planes of the point cloud model. On this basis, we combine the geometric features of three models to propose the specific welding seam extraction methods respectively. Then, according to the spatial structure of the welding seams and the welding process, we carry out the welding path planning. Finally, a welding pose planning method based on the dihedral structure is proposed. Experiment results show that the proposed method can well realize the welding seam extraction, welding path and posture planning of medium-thickness plate structural parts without programming and teaching. • We propose specific welding seam extraction methods for different models. • We propose a welding path planning method according to the spatial structure of the welding seams and the welding process. • A welding posture planning method based on the dihedral structure is proposed to realize the automatic planning of welding torch posture. • We use real experimental data to evaluate the performance of the proposed method and compare it with the existing method to illustrate its effectiveness.

Evaluation of hardness and impact toughness of drawn arc stud weldments on different structural steel grades

The drawn arc stud welding (DASW) procedure is usually qualified by visual examinations and practical destructive tests of the joint according to international standards. An assessment of the material properties in specific weld zone regions is not requested under consideration of their limited areas or the general joint dimension although the press welding process implies a complex metallurgy. Nonetheless, there is a need of knowledge regarding the heterogeneous material properties in the weld zone, especially when welding on base materials with specified strength and toughness behavior that might be significantly diminished. In this study, the local strength and toughness properties in welded joints of non-alloyed steel studs (Ø20 mm) on plates of different generic structural steel grades are investigated by hardness test and Charpy impact testing. A non-alloyed normalized structural steel (nominal yield strength 355 MPa) and a thermomechanically rolled fine grain structural steel of same strength as well as a quenched and tempered structural steel with high nominal yield strength of 690 MPa were examined. In the weld metal region, relatively similar hardness and low impact energy were determined despite of the different base plate steel grades. The base plate heat-affected zones suffer a strong embrittlement due to the DASW thermocycle.