Flash Formation Research Articles

Characterisation of Microstructure and Mechanical Properties of Linear Friction Welded α+β Titanium Alloy to Nitinol

A variable area nozzle integrated into the design of a high-bypass-ratio turbofan engine effectively saves up to 10% in aircraft fuel consumption. Additionally, noise emissions can be lowered at airports during take-off and landing by having better control of the nozzle diameter. Shape memory capabilities of Nitinol alloys could be availed in the form of actuators in the construction of such a nozzle. However, these Nitinol actuators must be joined to Ti-6Al-4V, a prominent alloy making up most of the rest of the nozzle. Because of the huge differences in the physical and metallurgical properties of these alloys, fusion welding is not as effective as solid-state welding. In the current study, a linear friction welding process was adopted to join Ti-6Al-4V to Nitinol successfully. The effect of friction welding on the evolution of weld macro and microstructures; hardness and tensile properties were studied and discussed. The macrostructure of Ti-6Al-4V and Nitinol’s dissimilar joint revealed flash formation mainly on the Ti-6Al-4V side due to its reduced flow strength at high temperatures. Optical microstructures revealed fine grains in Ti-6Al-4V immediately adjacent to the interface due to dynamic recrystallisation and strain hardening effects. In contrast, Nitinol remained mostly unaffected. An intermetallic compound (Ti2Ni) was seen to have formed at the interface due to the extreme rubbing action, and these adversely influenced the tensile strength and elongation values of the joints.

Numerical modelling on the plastic flow and interfacial self-cleaning in linear friction welding of superalloys

The self-cleaning effect of linear friction welding (LFW) is believed to be a crucial means to remove surface contaminants for achieving a satisfactory property of the resultant joints. To systematically investigate the self-cleaning process, a fully-coupled computational model was established based on an integrated approach considering dual plastic bodies for simulating the LFW of a typical Ni-based superalloy, GH4169. Since the removal of surface contaminants is determined by the material flow during LFW, the plastic flow behaviour was studied followed by the introduction of contaminant removal process and parameter optimisation. Results showed the material flow rate was at the highest level when two components were at the maximum relative displacement. The forging load continuously extruding the softened materials from the interface gave the greatest contribution to the material flow and formation of the flash. The removal rate of contaminants was initially increased followed by a subsequent decrease after reaching the peak during the welding process. The removal of 95 % surface contaminants was found to be a criterion to determine the critical welding parameter in simulation. The critical shortening length for removing the contaminants was decreased with increasing friction pressure, but an opposite trend was observed with the oscillation frequency. Validation by experiments suggested that the model provided reasonable and accurate predictions for flash formation, material flow, and final joint property in LFW of GH4169 superalloy. The present developed model could be utilised to select welding parameters of the LFWed Ni-based superalloy joint with a significant simplification to the manufacturing process.

Microstructural Investigation of a Friction-Welded 316L Stainless Steel with Ultrafine-Grained Structure Obtained by Hydrostatic Extrusion.

The paper presents the microstructural investigation of a friction-welded joint made of 316L stainless steel with an ultrafine-grained structure obtained by hydrostatic extrusion (HE). Such a plastically deformed material is characterized by a metastable state of energy equilibrium, increasing, among others, its sensitivity to high temperatures. This feature makes it difficult to weld ultra-fine-grained metals without losing their high mechanical properties. The use of high-speed friction welding and a friction time of <1 s reduced the scale of the weakening of the friction joint in relation to result obtained in conventional rotary friction welding. The study of changes in the microstructure of individual zones of the friction joint was carried out on an optical microscope (OM), scanning electron microscope (SEM), transmission electron microscope (TEM) and electron backscattered diffraction (EBSD) analysis system. The correlation between the microstructure and hardness of the friction joint is also presented. The heat released during the high-speed friction welding initiated the process of dynamic recrystallization (DRX) of single grains in the heat-affected zone (HAZ). The additional occurrence of strong plastic deformations (in HAZ) during flash formation and internal friction (in the friction weld and high-temperature HAZ) contributed to the formation of a highly deformed microstructure with numerous sub-grains. The zones with a microstructure other than the base material were characterized by lower hardness. Due to the complexity of the microstructure and its multifactorial impact on the properties of the friction-welded joint, strength should be the criterion for assessing the properties of the joint.

Direct Drive Friction Welding Joint Strength of AISI 304

The present study describes the effect of friction welding process on mechanical and metallurgical properties of AISI 304 steel. The joints were made by direct drive friction welding (DDFW) machine, while the characteristics of friction welded joints were evaluated by macro-microstructure, microhardness, tensile test with 4 mm and 6 mm effective diameter, and scanning electron microscope (SEM). The results showed severe flash formation at stationary side related to rotating side, highly plastically deformed zone (HPDZ) was revealed at the interface with large dimension of 110 µm. Maximum microhardness values were recorded at welded center and increased from peripheral to central zones. Reducing ratio of ultimate tensile strength (UTS) and ductility for welded joint related to AISI 304 were 86 and 67%, respectively. Tensile fractures occurred adjacent to the interface at thermo-mechanically affected zone (TMAZ). Fracture morphologies by SEM were discovered cleavage features and mostly ductile mode with micro-porosities of different forms and dimensions

Concept of forge application under effect of friction time for AISI 316 using friction welding process

Present study describes the effect of forge application under consideration of friction time on metallurgical and mechanical properties of friction welded joint for AISI 316. Direct drive friction welding (DDFW) has been utilized to obtain welded joints. Three friction times (6.5 s, 8.5 s and 10 s) with two directions for this study were used; the first direction was achieved by forge phase elimination and the second was performed under forge phase application. Macro-microstructure for welded joint, microhardness across the axial direction, tensile tests and tensile fracture morphology were investigated. As a conclusion, flash formation was decreased about 30% without forge compared to forge application. Thermo-mechanically affected zone (TMAZ) was extended in 8 mm at the interface without forge and highly plastically deformed zone (HPDZ) can be found at the interface after forge application. At shorter friction time for the two directions, ultimate tensile strength (UTS) and ductility were revealed closer to AISI 316. Hence, all tensile fractures were occurred adjacent to the interface at TMAZ, except in the shorter friction time after in forge application, the fracture was happened at the base metal, and that was confirmed with the fracture morphology which has the same character of AISI 316.

Microstructure and Mechanical Properties of Dissimilar Friction Welding Ti-6Al-4V Alloy to Nitinol

In the present study, a friction welding process was adopted to join dissimilar alloys of Ti-Al-4V to Nitinol. The effect of friction welding on the evolution of welded macro and microstructures and their hardnesses and tensile properties were studied and discussed in detail. The macrostructure of Ti-6Al-4V and Nitinol dissimilar joints revealed flash formation on the Ti-6Al-4V side due to a reduction in flow stress at high temperatures during friction welding. The optical microstructures revealed fine grains near the Ti-6Al-4V interface due to dynamic recrystallization and strain hardening effects. In contrast, the area nearer to the nitinol interface did not show any grain refinement. This study reveals that the formation of an intermetallic compound (Ti2Ni) at the weld interface resulted in poor ultimate tensile strength (UTS) and elongation values. All tensile specimens failed at the weld interface due to the formation of intermetallic compounds.

Investigation of Temperature Evolution and Flash Formation at AA5083 Studs during Friction Surfacing

Friction surfacing (FS), a solid-state joining process, is a coating technology for metallic materials. Frictional and plastic deformation enable the deposition of a consumable material on a substrate. Process temperatures stay below the melting point of the consumable material and are an important factor determining the quality of the resulting deposit. The focus of the current study is the experimental analysis of the flash formation and the temperature evolution in consumable studs during FS deposition of dissimilar aluminum alloys. The main process parameters, axial force, rotational speed and travel speed, were varied while the setting of the process surrounding was kept constant. The temperature evolution for the applied process parameter combinations are investigated for the stud material via infrared camera. The results show that the choice of applied force, rotational speed and travel speed did not lead to significant changes in maximum process temperature values of the consumable stud detectable via infrared camera. However, the flash formation at the tip of the plasticized stud shows significant differences for varied process parameters. Especially reduction of travelspeed or increase in axial force led to formation of larger flashes. Since the material that is pressed out of the process zone into the flash is not deposited on the substrate, the flash formation can be linked to the material efficiency of the FS process.

Microstructure and mechanical properties of friction welded carbon steel (EN24) and nickel-based superalloy (IN718)

Continuous-drive rotary friction welding was performed to join cylindrical specimens of carbon steel (EN24) and nickel-based superalloy (IN718), and the microstructures of three distinct weld zones—the weld interface (WI)/thermo-mechanically affected zone (TMAZ), the heat-affected zone (HAZ), and the base metal—were examined. The joint was observed to be free of defects but featured uneven flash formation. Electron backscatter diffraction (EBSD) analysis showed substantial changes in high-angle grain boundaries, low-angle grain boundaries, and twin boundaries in the TMAZ and HAZ. Moreover, significant refinement in grain size (2–5 μm) was observed at the WI/TMAZ with reference to the base metal. The possible causes of these are discussed. The microhardness profile across the welded joint shows variation in hardness. The changes in hardness are ascribed to grain refinement, phase transformation, and the dissolution of strengthening precipitates. The tensile test results reveal that a joint efficiency of 100% can be achieved using this method.

Cross-wire welding analyzed by experiments and simulations

Resistance welding of crossed wires, e.g. used in the electronics industry or in fabrication of wire meshes, is analyzed by both experiments and numerical modeling using an electro-thermo-mechanical finite element formulation. Welding machine characteristics are discussed and the necessary requirements to the type of welding machine are concluded. Cross-wire welding of stainless steel, structural steel and aluminum are presented, and differences in the welding process and flash formation are discussed. It is shown that the joints are primarily due to solid-state bonding and local melting. The simulations compare well to the experiments at low weld settings, while remaining challenges for simulation of high weld settings are identified in terms of requirements to the mesh and detailed description of the machine follow up of the electrode force and movement.

Novel method of nanoparticle addition for friction stir welding of aluminium alloy

ABSTRACT 2.5 mm thick 6061-T6 aluminium alloy sheets with nanoparticles addition at abutting edges were joined using Friction Stir Welding (FSW). This study aims to investigate the effect of novel nanoparticles’ addition strategy on the nanoparticle distribution for metallurgical and mechanical properties of weldments. The nanoparticle dispersion in different FSW zones were analysed using optical and SEM micrographs. The Vickers microhardness tests were conducted with more emphasis on the nugget zone (NZ). The outcomes were correlated with the grain size, nanoparticle dispersion, and their effect on metal-matrix. Results revealed that the surface appearances of welds with Al2O3 nanoparticles are smooth well forged compared to the TiO2 weld that has rough surface with the flash formation on both sides of NZ. The microstructural study revealed the more refined NZ grain structure for Al2O3 welds due to homogenous nanoparticles dispersion than TiO2 nanoparticles welds in which clusters formation in the NZ leads to increases the grain size that results in non-uniform nanoparticles distribution. The NZ microhardness of Al2O3 nanoparticles welds (88Hv) was significantly higher than TiO2 welds (76Hv) and base material (61Hv) due to grain refinement and uniform nanoparticles distribution in the NZ.

Flash Formation of I-Rich Clusters during Multistage Halide Segregation Studied in MAPbI1.5Br1.5

Author(s): Babbe, F; Masquelier, E; Zheng, Z; Sutter-Fella, CM | Abstract: Wide band gap mixed halide perovskites such as MAPb(I1-xBrx)3 sparked great research interest because of their outstanding optoelectronic properties, ease of fabrication, and band gap tunability. Their application thus far is however limited by light-induced halide segregation in which microscopic clusters with a high iodide content are formed and act as recombination centers. The key mechanism(s) underlying this halide segregation process are still debated. Here, we present a study on the photoluminescence evolution in MAPb(I1.5Br1.5) perovskites with varying microstructures under constant illumination at room temperature and at elevated temperature. Our findings reveal a more complicated picture of the segregation mechanism occurring in three stages instead of two as commonly reported. The process starts with a flash formation of I-rich domains. Following is a rapid blue shift before the gradual and typically observed red shift occurs. The evolution of the three stages is fully reversible in the dark and is also present at elevated temperatures (50 °C). We explain the existence of multiple stages during light-induced halide segregation by natural compositional fluctuations of the halides and the formation of halide clusters with a dynamically changing distribution in I-Br content. The variation in the I-Br ratio depends on the grain size and film heterogeneity. These findings add further details in the quest of unraveling the underlying segregation mechanism(s), which need to be identified to stabilize halides in wide band gap perovskites.

Improving the surface quality of friction stir welds using reinforcement learning and Bayesian optimization

Friction stir welding is an advanced joining technology that is particularly suitable for aluminum alloys. Various studies have shown a significant dependence of the welding quality on the welding speed and the rotational speed of the tool. Frequently, an inappropriate setting of these parameters can be detected through an examination of the resulting surface defects, such as increased flash formation or surface galling. In this work, two different learning-based algorithms were applied to improve the surface topography of friction stir welds. For this purpose, the surface topographies of 262 welds, which were performed as part of ten studies, were evaluated offline. The aim was to use reinforcement learning and Bayesian optimization approaches to determine the most appropriate settings for the welding speed and the rotational speed of the tool. The optimization problem was solved using reinforcement learning, specifically value iteration. However, the value iteration algorithm was not efficient, since all actions and states had to be iterated over, i.e., each possible parameter combination had to be evaluated, to find the best policy. Instead, it was better to solve the optimization problem directly using the Bayesian optimization. Two approaches were applied: both an approach in which the information from the other studies was not used and an approach in which the information from the other studies was used. On average, both the Bayesian optimization approaches found suitable welding parameters significantly faster than a random search algorithm, and the latter approach improved the result even further compared with the former approach. Future research will aim to show that optimization of the surface topography also leads to an increase in the ultimate tensile strength.

Experimental Validation of Injection Molding Simulations of 3D Microparts and Microstructured Components Using Virtual Design of Experiments and Multi-Scale Modeling.

The increasing demand for micro-injection molding process technology and the corresponding micro-molded products have materialized in the need for models and simulation capabilities for the establishment of a digital twin of the manufacturing process. The opportunities enabled by the correct process simulation include the possibility of forecasting the part quality and finding optimal process conditions for a given product. The present work displays further use of micro-injection molding process simulation for the prediction of feature dimensions and its optimization and microfeature replication behavior due to geometrical boundary effects. The current work focused on the micro-injection molding of three-dimensional microparts and of single components featuring microstructures. First, two virtual a studies were performed to predict the outer diameter of a micro-ring within an accuracy of 10 µm and the flash formation on a micro-component with mass a 0.1 mg. In the second part of the study, the influence of microstructure orientation on the filling time of a microcavity design section was investigated for a component featuring micro grooves with a 15 µm nominal height. Multiscale meshing was employed to model the replication of microfeatures in a range of 17–346 µm in a Fresnel lens product, allowing the prediction of the replication behavior of a microfeature at 91% accuracy. The simulations were performed using 3D modeling and generalized Navier–Stokes equations using a single multi-scale simulation approach. The current work shows the current potential and limitations in the use of micro-injection molding process simulations for the optimization of micro 3D-part and microstructured components.

Local binary pattern for the evaluation of surface quality of dissimilar Friction Stir Welded Ultrafine Grained 1050 and 6061-T6 Aluminium Alloys

Friction Stir Welding process is an advanced solid-state joining process which finds application in various industries like automobiles, manufacturing, aerospace and railway firms. Input parameters like tool rotational speed, welding speed, axial force and tilt angle govern the quality of Friction Stir Welded joint. Improper selection of these parameters further leads to fabrication of the joint of bad quality resulting groove edges, flash formation and various other surface defects. In the present work, a texture based analytic machine learning algorithm known as Local Binary Pattern (LBP) is used for the extraction of texture features of the Friction Stir Welded joints which are welded at a different rotational speed. It was observed that LBP algorithm can accurately detect any irregularities present on the surface of Friction Stir Welded joint.

Pipe-to-pipe friction welding of dissimilar Al-SS joints for cryogenic applications

In the present investigation, dissimilar materials such as AA6063 and SS304L having pipe dimensions of 60.33 mm outer diameter and 3.9 mm wall thickness were friction-welded with different conditions of process parameters. Nondestructive tests such as radiography test (RT) and liquid penetration test (LPT) were carried out on the welded pipes in order to qualify weld joint. Thermal shock test and helium leak tests were performed on the successful welded samples in order to check its ability to work with cryogenic application. The results revealed that the pipe-to-pipe configuration of dissimilar Al–SS joints was produced successfully by friction welding using continuous drive. There were no defects presented in the joint area as revealed by RT and LPT. Thermal shock test and helium leak tests have also confirmed the soundness of joints. Higher deformation of Al base material results in the huge flash formation of Al that results in the sleeve formation of Al pipe on SS pipe. The helium leak test has confirmed the soundness of joints without any defects after its testing at 1.2 psi pressure. More than 60% of joint efficiency was obtained from Al–SS friction-welded joints.

Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

Friction stir welding is an advanced joining technology that is particularly suitable for aluminum alloys. Various studies have shown that welding quality depends significantly on the welding speed and the rotational speed of the tool. It is frequently possible to detect an unsuitable setting of these parameters by examining the resulting surface defects, such as increased flash formation or surface galling. In this work, Artificial Neural Networks were used to analyze process data in friction stir welding and predict the resulting quality of the weld surface. For this purpose, nine different variables were recorded during friction stir welding of EN AW-6082 T6 sheets: the forces and accelerations in three spatial directions, the spindle torque, and temperatures at the tool shoulder and tool probe. In Case 1, the welds were assigned to the classes good and defective on the basis of a human visual inspection of the weld surface. In Case 2, the welds were categorized into the two classes on the basis of a surface topography analysis. Subsequently, three different major Artificial Neural Network architectures were tested for their ability to predict the surface quality: Feed Forward Fully Connected Neural Networks, Recurrent Neural Networks and Convolutional Neural Networks. The highest classification accuracy was achieved when Convolutional Neural Networks were used. Thus, the evaluation of the force signal transverse to the welding direction yielded the highest accuracy of 99.1% for the prediction of the result of the human visual inspection. The result achieved for the prediction of the topography analysis was an accuracy of 87.4% when the spindle torque was evaluated. Using all nine different process variables to predict the topography analysis, the accuracy could be improved slightly to 88.0%. The sampling rate of the spindle torque was varied between 40 Hz and 9600 Hz and no significant influence was determined. The findings show that Convolutional Neural Networks are well suited for the interpretation of friction stir welding process data and can be used to predict the resulting surface quality. In future work, the results are to be used to develop a parameter optimization method for friction stir welding.

Local Binary Pattern for the Evaluation of Surface Quality of Dissimilar Friction Stir Welded Ultrafine Grained 1050 and 6061-T6 Aluminium Alloys

Friction Stir Welding process is an advanced solid-state joining process which finds application in various industries like automobiles, manufacturing, aerospace and railway firms. Input parameters like tool rotational speed, welding speed, axial force and tilt angle govern the quality of Friction Stir Welded joint. Improper selection of these parameters further leads to fabrication of the joint of bad quality resulting groove edges, flash formation and various other surface defects. In the present work, a texture based analytic machine learning algorithm known as Local Binary Pattern (LBP) is used for the extraction of texture features of the Friction Stir Welded joints which are welded at a different rotational speed. It was observed that LBP algorithm can accurately detect any irregularities present on the surface of Friction Stir Welded joint.

Surface Quality Analysis of Friction Stir Welded Joints by Using Fourier Transformation and Local Binary Patterns Algorithms

Abstract: Friction Stir Welding process is an advanced solid-state joining process which finds application in various industries like automobiles, manufacturing, aerospace and railway firms. Input parameters like tool rotational speed, welding speed, axial force and tilt angle govern the quality of Friction Stir Welded joint. Improper selection of these parameters further leads to fabrication of the joint of bad quality resulting groove edges, flash formation and various other surface defects. In the present work, a texture based analytic machine learning algorithm known as Local Binary Pattern (LBP) and Fourier transformation algorithm are used for the extraction of texture features of the Friction Stir Welded joints which are welded at a different rotational speed. It was observed that LBP algorithm can accurately detect any irregularities present on the surface of Friction Stir Welded joint and Fourier transformation method can detect the groovy edges present on the Friction Stir Welded joint. In the case study, an image based defect recognition system by using Fourier transformation method is constructed. Five types of filters i.e. Ideal Filter, Butterworth Filter, Low pass Filter, Gaussian Filter and High Pass Filter were used. The results showed that the high pass filter has more capability to detect the edges in comparison to other four filters. It was also observed that Ideal filter has a lot of distortions when compared to the Gaussian Filter and Butterworth Filter.

CFD modelling of friction stir welding (FSW) process of AZ31 magnesium alloy using volume of fluid method

In this work, a validate couple-thermo flow model of the FSW process is generated using the CFD software FLUENT, with this model, then being developed to model the flash formation phenomena that occur during the FSW process using the volume of fluid method. From both models, pressure distribution on the tool surface was predicted at different radial positions. A comparison was made between single and multiple phase flow models. A significant reduction of the pressure values was seen when using a two-phase flow model. Volume fraction contour showed the changing in the metal phase when increased the welding time steps. A modelling procedure of this work is presented in details to utilize it in the further numerical investigation as a guide. This work could be used to afford more understanding of surface flash formation phenomena. Moreover, the outcome of this study can be a useful technique to aid the tool failure and increase the tool life during the FSW process.

Automated surface inspection of friction stir welds by means of structured light projection

Friction stir welding is an innovative joining technology that is particularly suitable for aluminium alloys. Various studies have shown a significant dependence of the weld seam quality on the welding speed and the rotational speed of the tool. Frequently, an unsuitable setting of these parameters can be detected by visual examination of resulting surface defects, such as increased flash formation, surface galling, or irregular formation of the surface arc texture. The visual inspection for these defects is often conducted manually and is therefore associated with increased costs and personnel effort. In this work, a method to automatically detect irregularities and features on the weld surface is introduced. It is based on a threedimensional shape detection of the surface features using structured light projection. For this purpose, the topography of EN AW-5754-H111 sheets, welded in a butt joint configuration, was measured. The data were evaluated and characteristic key structures of the weld seam surface were derived. It was shown that welding irregularities can be detected automatically by an evaluation of the weld seam topography. The results are the basis for the future development of an inline quality monitoring and parameter control method for friction stir welding.