Defects In Friction Stir Welding Research Articles

Enhancing heat and mass transfer to suppress void defects in friction stir welding by superimposing ultrasonic vibration

Enhancing heat and mass transfer to suppress void defects in friction stir welding by superimposing ultrasonic vibration

Investigation of zigzag line defects in friction stir welding of SS304 stainless steel

The zigzag line defect (ZLD) formed in the stir zone (SZ) affects the microstructural and mechanical properties of the materials welded by friction stir welding (FSW). In this research, the cause of ZLD formation in the FSW of SS304 steel was analyzed. The results showed that this defect is formed under incomplete penetration conditions. Under such conditions, oxygen enters the SZ from the incomplete penetration site and linearly creates micro- and nano-voids of metal oxides. The formed oxide voids had high hardness, but due to crack nucleation, they showed low fracture toughness.

Machine Learning Tools for Flow-Related Defects Detection in Friction Stir Welding

Abstract Flow-related defects in friction stir welding are critical for the joints affecting their mechanical properties and functionality. One way to identify them, avoiding long and sometimes expensive destructive and nondestructive testing, is using machine learning tools with monitored physical quantities as input data. In this work, artificial neural network and decision tree models are trained, validated, and tested on a large dataset consisting of forces, torque, and temperature in the stirred zone measured when friction stir welding three aluminum alloys such as 5083-H111, 6082-T6, and 7075-T6. The built models successfully classified welds between sound and defective with accuracies over 95%, proving their usefulness in identifying defects on new datasets. Independently from the models, the temperature in the stirred zone is found to be the most influential parameter for the assessment of friction stir weld quality.

Revealing the mechanism of tool tilting on suppressing the formation of void defects in friction stir welding

Experiments have proven tool tilting in friction stir welding (FSW) can effectively suppress the formation of void defects. However, the mechanism of tool tilting on suppressing the formation of void defects has not been revealed. In this study, a CFD model considering the influence of tool tilting is established. A non-uniform distribution of normal pressure at the tool-workpiece contact interfaces is proposed to relate to the tool tilt angle. The discrete particle tracing method is used to characterize the formation of void defects in FSW. The heat generation, temperature distribution and plastic flow behaviors between the case without (i.e. 0° tool tilt angle) and with (i.e. 2.5° tool tilt angle) tool tilting are quantitatively compared and analyzed. The results show the tool tilting in FSW leads to higher heat flux and temperature near the pin side in the middle and low parts of the workpiece. Moreover, the frictional shear stress at the pin side/workpiece contact interface significantly increases when the tool is tilting, leading to a higher driving force for the plastic material to flow. As a result, the material flows farther to the advancing side of the workpiece after bypassing the tool when the tool is tilting at 2.5°, which helps to heal the voids on the advancing side of the FSW joints. The higher temperature with higher frictional shear stress enhances the plastic material flow in the middle and low part of the joints when the tool is tilting, which is attributed to suppressing the void defects. The model is validated by experimental results.

Probing finite element modelling of defects in friction stir welding by tailoring mass scaling factor

Friction stir welding (FSW), a solid-state welding process primarily used to join the low melting point materials, often encounters surface and subsurface defects. Modelling surface texture and internal volumetric defects in the FSW process using any numerical method is computationally expensive. The analysis of thermo-mechanical behavior of the FSW process using coupled Eulerian-Lagrangian (CEL) method is sensitive to mass scaling factor (MSF). Tailoring this factor for a computationally efficient model is a promising approach to predict defect in FSW process. The influence of MSF on the computational framework of the CEL finite element method (FEM) in the context of defect formation for two different types of materials (difference in densities) is addressed in the present work. The progress of different energies associated with the CEL approach is indicative of the solution availability by the application of MSF. The variation of these energies is significantly high in FSW process when encountering a defect and dictates the optimum conditions of MSF. An optimum range of MSF is deliberated on account of an efficiently model of FSW process. The simulated surface and subsurface defects, and the discrete temperature data are compared with experimental observation. The maximum difference between the predicted and experimentally measured tunnel defect is ∼ 0.75 mm, whereas the predicted temperature is within 5% of experimental result. It establishes the reliability of the developed numerical model and directs to trigger the model parameters for minimizing the computational time by tailoring the MSF. The optimum range of MSF between 106 and 107 strikes a balance between the result accuracy and computational feasibility for welding of AZ31B. Here, the distribution of strain and strain rate is representative of predicting the defect formation, such as, void, no material zone, crack, surface texture etc. The strain rate is almost double on the retreating side (RS) compared to advancing side (AS) due to the presence of the tunnel defect on AS.

A review of tensile, hardness, wear, and microstructural characteristics of friction stir welded/processed composites

In this review paper friction stir welding (FSW) of various aluminum matrix composites (AMCs) is highlighted. The use of traditional fusion welding processes such as the TIG/MIG process for joining of AMCs results in the formation of various defects such as porosity, solidification cracks, etc. such defects is formed mainly due to the melting of parent metals due to high heat supply during fusion welding. Such challenges could be overcome by using a solid-state welding process such as FSW, a novel joining process capable to obtain good-quality joints. In this paper, various AMCs joined by FSW are highlighted, especially emphasis given on the tensile, hardness, wear, and microstructural characteristics of AMCs joined by FSW. It is noted that tool material, tool pin profile, type of reinforcement, and selection of process parameters play a significant role in obtaining good-quality joints. FSW microstructure exhibits a homogeneous distribution of reinforcement as a result better joint efficiency is reported by different researchers. However, the integration of FSW with advanced technology tools such as artificial intelligence (AI) and machine learning (ML) is necessary for online quality inspection and monitoring. Such tools will help for obtaining better-quality joints by avoiding the formation of defects in FSW.

Force data-driven machine learning for defects in friction stir welding

This study proposes a strategy for developing force-data-driven machine learning models to precisely predict defects and their types in friction stir welding (FSW). The characteristics of the three component forces in FSW, including traverse force (Fx), lateral force (Fy), and plunge force (FZ) are studied. The change in the force wave corresponded well with the variation in the defect. Fyavg had the best correlation with the characteristics of tunnel defects, whereas some other time-frequency features had negligible effects on the defect variation. The machine learning models built with the input of 15 force features could detect defects with an accuracy of 95.8% and classify them into tunnels and porosities with an accuracy of 98.0%. The abnormal increase in Fyavg, caused by the buildup of redundant material transported to the retreating side, was the main characteristic of force change when a defect was formed.

Optimization of friction STIR welded AA6061 + SiCp metal matrix composite to increase joint tensile strength and reduce defects

Friction stir welding has been used in automobiles, locomotive, and aircraft structures. This metal joining process exhibits defects like kissing bonds, micropores, and tunnels. Factors like the joining material, joint thickness, tool geometry, and operating parameters control the defects in friction stir welding. The parameters like tool rotation, tool pass speed, and tool force have a greater influence on the joint quality. In this study, these parameters are considered to augment the strength of the joint and minimize defects. The metal matrix composite consisting of AA6061 matrix and 10 wt. % SiCp reinforcement is joined used FSW. The weld parameters were varied between 731 and 1068 rpm tool rotation speed, 0.33 and 1.17 mm/s tool pass speed, and 11 and 28 MPa tool force. The joint strength varied from 165 MPa to 244 MPa. The numerical analysis using ANOVA revealed that compared between the three parameters, the tool force had greater control over the tensile strength of the joint. After optimization, the joint was made at a tool rotation speed of 910 rpm, tool pass speed of 0.77 mm/s, and tool force of 22.33 MPa. The tensile strength increased to 249 MPa after using the optimized weld parameters. The number of defects in the joint was reduced after using the optimized weld parameters.

Weld defect localization in friction stir welding process

The article attempts to detect the defects in friction stir welding (FSW) process by analyzing the signal acquired during welding. The said welding technique utilizes pressure and heat developed by the usage of a non-consumable tool. Thus, the axial force signal carries a lot of information about the physical process, and hence, it could be used to identify the weld defects. Signal analysis has been performed by using wavelet-based techniques. Before this analysis, a methodology has been followed to select the best mother wavelets suitable for the signal. The results of defect identification have been validated by mapping the processed signal with the actual weld quality.

Wavelets Application in Prediction of Tunnel Defects in Friction Stir Welding of Alloy Joints from Vibroacoustic ANN-Based Model

This paper examines a connection of acoustic emission signal to the core parameters of the friction stir welding process, based on the artificial neural networks (ANNs). AE Instrument NI USB-9234 has obtained acoustic Z and Y emission signals. Wavelet Transform was used as the ANN's output through numerical and time parameters for discomposed acoustic pollution signals. The ANN inputs include rotation speed and frequency of the device, the machine profile and the tensile strength parameters. A multi-layer neural feed-forward network was selected and trained using the Levenberg Marquardt algorithm for different network architectures. Ultimately, an overview will be provided of the correlation between the estimated and analyzed results. The prototype obtained can be implemented with the aid of acoustical emission signals to design and improve automated system parameters and mechanical properties of the joint.

Main Issues in Quality of Friction Stir Welding Joints of Aluminum Alloy and Steel Sheets

Joining of aluminum alloys through friction stir welding (FSW) is effectively employed in several industries (e.g., aeronautics and aerospace) since it guarantees proper weld strength as compared to other joining technologies. Contrarily, dissimilar FSW of aluminum alloys and steels often poses important issues in the selection of welding parameters due to the difficulty to join different materials. Improper welding parameters give rise to the formation of intermetallic compounds, and internal and external defects (e.g., tunnel formation, voids, surface grooves, and flash). Intermetallic compounds are brittle precipitates of Al/Fe, which chiefly initiate crack nucleation, whereas internal and external defects mainly act as stress concentration factors. All these features significantly reduce joint strength under static and dynamic loading conditions. With reference to the literature, the influence of main welding parameters (rotational speed, welding speed, tool geometry, tilt angle, offset distance, and plunge depth) on the formation of intermetallic compounds and defects in FSW of aluminum alloys and steels is discussed here. Possible countermeasures to avoid or limit the above-mentioned issues are also summarily reported.

Analysis of vibration signal responses on pre induced tunnel defects in friction stir welding using wavelet transform and empirical mode decomposition

Abstract Among many condition-monitoring systems in welding operation, Defect identification is an important method to ensure the precision in finishing operation. Friction stir welding is a solid state welding process used to join two metals without the use of electrode at lower temperatures. The aim of this present work is to identify and localize the tunnel defect in aluminum alloy and measure the distance of the defect zone in the time domain of the vibration signal during Friction stir welding. The vibration signals are captured from the experiments and the burst in the vibration signal is focused in the analysis. A signal-processing scheme is proposed to filter the noise and to measure the dimensional parameters of the defect area. The proposed technique consists of discrete wavelet transform (DWT), which is used to decompose the signal. The enveloping technique is applied on the decomposed zero padded signal. The continuous wavelet transform (CWT) has been implemented on detailed signal followed by a time marginal integration (TMI) of the CWT scalogram. Empirical mode decomposition (EMD) is used to replace the detailing coefficients from DWT with Intrinsic Mode Function (IMF). Statistical parameters such as mean, kurtosis, S.D and crest factor have been extracted from the final filtered signal for validating the defect welds from the control defect free welds. Results produced were found to be that kurtosis is 7.4402 for tunnel defect induced weld and 3.3862 for defect free welds. As the increase in kurtosis value predicts the defect zone impact in the signal. The measurement of the defect zone of the cut 1 (voids) and cut 2 (tunnel grooves) in correlation with the processed signal is found to produce a much redundant results with an error rate of 0.02.

Detection of Friction Stir Welding Defects of AA1060 Aluminum Alloy Using Specific Damping Capacity.

The demand for nondestructive testing has increased, especially in welding testing. In the current study, AA1060 aluminum plates were jointed using the friction stir welding (FSW) process. The fabricated joints were subjected to free vibration impact testing in order to investigate the dynamic properties of the welded joint. Damping capacity and dynamic modulus were used in the new prediction method to detect FSW defects. The data acquired were processed and analyzed using a dynamic pulse analyzer lab shop and ME’Scope’s post-processing software, respectively. A finite element analysis using ANSYS software was conducted on different types of designed defects to predict the natural frequency. The results revealed that defective welded joints significantly affect the specific damping capacity. As the damping ratio increased, so did the indication of opportunities to increase the presence of defects. The finite element simulation model was consistent with experimental work. It was therefore revealed that natural frequency was insufficient to predict smaller defects.

Influence of Tool Tilt Angle on Material Flow and Defect Generation in Friction Stir Welding of AA2219

Heat treatable aluminium alloy AA2219 is widely used for aerospace applications, welded through gas tungsten and gas metal arc welding processes. Welds of AA2219 fabricated using a fusion welding process suffers from poor joint properties or welding defects due to melting and re-solidification. Friction stir welding (FSW) is a solid-state welding process and hence free from any solidification related defects. However, FSW also results in defects which are not related to solidification but due to improper process parameter selection. One of the important process parameters, i.e., tool tilt angle plays a critical role in material flow during FSW, controlling the size and location of the defects. Effect of tool tilt angle on material flow and defects in FSW is ambiguous. A study is therefore taken to understand the role of tool tilt angle on FSW defects. Variation in temperature, forces, and torque generated during FSW as a result of different tool tilt angles was found to be responsible for material flow in the weld, controlling the weld defects. An intermediate tool tilt angle (1o-2o) gives weld without microscopic defect in 7 mm thick AA2219 for a given set of other process parameters. At this tool tilt angle, x-force, and Z- force is balanced with viscosity and the material flow strain rate sufficient for the material to flow and fill internal voids or surface defects in the weld.

Modeling of defects in friction stir welding using coupled Eulerian and Lagrangian method

In the current research, a coupled Eulerian and Lagrangian method is used to model the friction stir welding process. Volume of fluid principle is used to predict the formation of defects during the process. Model is validated with experimentally observed spindle torque and axial force. Three different pin heights were simulated and pin height of 2.5 mm was found as the optimum value. Experimental defects are observed using the X-ray computer tomography method. The model has successfully predicted the presence of defect for different welding parameters. Defect free weld is produced at a tilt angle of 2°. Qualitative comparisons of effective plastic strain and welding efficiency were carried out and correlation among them is proposed.

Defects in Friction Stir Welding of Steel

Defects associated with friction stir welding of two steel grades including DH36 and EH46 were investigated. Different welding parameters including tool rotational and tool traverse (linear) speeds were applied to understand their effect on weld seam defects including microcracks and voids formation. SEM images and infinite focus microscopy were employed to identify the defects types. Two new defects associated with the friction stir welding process are introduced in this work. The first defect identified in this work is a microcrack found between the plunge and the steady state region and attributed to the traverse moving of the tool with unsuitable speed from the plunge-dwell to the steady state stage. The tool traverse speed has recommended to travel 20 mm more with accelerated velocity range of 0.1 from the maximum traverse speed until reaching the steady state. The maximum recommended traverse speed in the steady state was also suggested to be less than 400 mm/min in order to avoid the lack in material flow. The second type of defect observed in this work was microcracks inside the stirred zone caused by elemental precipitations of TiN. The precipitates of TiN were attributed to the high tool rotational speed which caused the peak temperature to exceed 1200 °C at the top of the stirred zone and based on previous work. The limit of tool rotational speed was recommended to be maintained in the range of 200-500 RPM based on the mechanical experiments on the FSW samples.

Investigation on the Effects of Process Parameters on Defect Formation in Friction Stir Welded Samples Via Predictive Numerical Modeling and Experiments

Setting optimum process parameters is very critical in achieving a sound friction stir weld joint. Understanding the formation of defects and developing techniques to minimize them can help in improving the overall weld strength. The most common defects in friction stir welding (FSW) are tunnel defects, cavities, and excess flash formation, which are caused due to incorrect tool rotational or advancing speed. In this paper, the formation of these defects is explained with the help of an experimentally verified 3D finite element (FE) model. It was observed that the asymmetricity in temperature distribution varies for different types of defects formed during FSW. The location of the defect also changes based on the shoulder induced flow and pin induced flow during FSW. Besides formation of defects like excess flash, cavity defects, tunnel/wormhole defects, two types of groove like defects are also discussed in this paper. By studying the different types of defects formed, a methodology is proposed to recognize these defects and counter them by modifying the process parameters to achieve a sound joint for a displacement-based FSW process.

Friction stir welding of aluminium alloys: An overview of experimental findings – Process, variables, development and applications

The never ending appetite of the mankind to produce more and more competitive products results in continuous development of newer and newer manufacturing processes. One of such a kind, a solid state welding process highly appreciated for joining of a variety of aluminium and copper alloys, is friction stir welding. The process is also an accomplished method for joining dissimilar materials efficiently. The process finds its major application for joining hard-to-weld metals, especially the precipitation hardenable aluminium alloys and is widely adopted by industries for the welding of such aluminium alloys. However, the process has still not found an economical way for welding of steels and hence found limited applications in industries for welding steels. This paper aims at providing a comprehensive review of the work undertaken in the field of friction stir welding and provides an insight into the friction stir welding of aluminium alloys. The article pays critical attention and analytical evaluation of classification of aluminium alloys, friction stir welding process parameters, the mechanical testing and properties of the friction stir welding joints, macrostructure and microstructure evolution during friction stir welding, friction stir welding defects and industrial applications of the process. The friction stir welding process variants are discussed as well. Special accentuation has been given to (i) effect of friction stir welding parameters on the microstructure evolved and thus the ultimate mechanical properties (viz. tensile strength, hardness, fatigue strength, fracture toughness and residual stresses), (ii) the texture formation, microstructure refinement and the role of intermetallics. However, studies related to welding of dissimilar aluminium alloys, temperature, and heat transfer modeling and material flow are out of the scope of this paper. Finally, the directions of future research are examined.

Using coupled Eulerian Lagrangian formulation for accurate modeling of the friction stir welding process

Abstract Accurate modeling of Friction-Stir Welding (FSW) would allow a better understanding of the material flow and temperature distribution during the process. This is important for the optimization of the process parameters to avoid the conditions that generate defects in FSW. However, the numerical simulation of the process is challenging due to the numerous complexities involved in FSW. It is a fully coupled thermo-mechanical problem that includes large plastic deformation, heat flow, nonlinear contact and friction. In this study a finite element simulation with a coupled Eulerian Lagrangian formulation was used to predict the temperature distribution during the FSW process of two similar plates of AA2024. The plates were defined as Eulerian parts and the tool was defined as a Lagrangian one. The behavior of the plates’ material during the simulation process was modeled using the Johnson-Cook model. In order to verify the accuracy of the results obtained from the simulation, FSW experiments were conducted with the same welding parameters. Results show good accord between the predicted and the measured temperature distributions. Furthermore, it was shown that the temperature distribution along the welding line is asymmetric with the maximum temperature in the advancing side being higher than that in the retreating side.

Simulation of material plastic flow driven by non-uniform friction force during friction stir welding and related defect prediction

Material flow during welding is an important factor that affects the quality of friction stir welding (FSW) joints. In this study, a new welding tool/work piece contact model is proposed to simulate material flow near the FSW tool. The material around the tool flows under the driving force of friction non-uniformly distributed on the interface. Friction force is calculated based on modified Coulomb friction law in this model. Wormhole defects under various welding parameters are graphically predicted by the distribution of tracing particles added in numerical models. Five groups of FSW experiments were conducted to obtain temperature and joint morphology results. Simulation results, including temperature and wormhole defects prediction, corresponded with the experimental results. Further analyses imply that under the process parameters selected in this study, an area with very low friction force occurred behind the tool pin. When the material in front of the tool pin moved to this area from the retreating side, the material speed significantly decreased due to the insufficient driving force, and it became difficult for the material to move to the advancing side along the circular path. The study reveals that the non-uniform friction force model provides a potential tool for predicting FSW defects.