An axial force controller with delay compensation for the friction stir welding process

Invented in 1991, friction stir welding (FSW) is a new solid state joining technique. This process has many advantages over fusion welding techniques including absence of filler material, shielding gas, fumes and intensive light, solid state joining, better microstructure, better strength and fatigue life, and etc. The difficulty with FSW is in the high forces involved especially in axial direction which requires use of robust fixturing and very stiff FSW machines. Reduction of FSW force would simplify implementation of the process on less stiff CNC machines and industrial robots. In this paper axial welding force reduction is investigated by use of tool design and welding parameters in FSW of 3.07 mm thick AA6061-T6 sheets at right angle. Attempt is made to reduce the required axial force while having acceptable ultimate tensile strength (UTS). It is found that shoulder working diameter and shoulder angle are the most important parameters in the axial force determination yet pin angle has minor effect. According to the developed artificial neural network (ANN) model, proper selection of shoulder diameter and angle can lead to approximately 40% force reduction with acceptable UTS. Regions of tool design and welding parameters are found which result in reduced axial force along with acceptable UTS.

Mechanical characterization of friction stir welded dissimilar aluminium alloy using Taguchi approach

• Accurate prediction of weld quality in friction stir welding process of high-density polyethylene. • Combining Coupled Eulerian-Lagrangian (CEL) simulations with machine learning (ML) models. • Both CEL and ML models closely match experimental results. • Simulations accurately replicated flow mixing patterns observed in experiments. Friction stir welding (FSW) has gained recognition as an effective technique for joining thermoplastic materials. This study presents a comprehensive investigation to predict FSW characteristics such as weld temperatures, vertical axial forces, material flow and defect formation in FSW of high-density polyethylene (HDPE) using both computational and machine learning approaches, validated by experimental results. A three-dimensional Coupled Eulerian-Lagrangian (CEL) numerical model is developed to simulate the HDPE welding process under varying process parameters of rotational and welding speeds, and bottom preheating conditions. The CEL numerical model is validated against experimental data, showing markedly good agreement in predicting void formation, thermal and mechanical responses. Results indicate that preheating the bottom surface proved effective in reducing thermal gradients and stabilizing vertical force fluctuations. Quantitatively, bottom-surface preheating at 60 °C reduced peak plunge force from 1600 N close to 1100 N and raised peak temperatures from 70 °C (without preheat) to about 100 °C, hence smoothing through-thickness gradients and stabilizing the axial force distribution. In parallel, two multilayer perceptron artificial neural networks (ANNs) are trained on experimental data to predict temporal temperature and axial force responses with R 2 between 0.91 and 0.99, demonstrating high accuracy and strong generalization across the tested FSW parameter space. The computational CEL simulations accurately predicted weld zone morphology, including stir zone, thermo-mechanically affected zone and heat-affected zone, however The combined CEL–ANN framework enables rapid generation of process maps that highlights defective and defect-free welding windows, with optimal conditions obtained at 1500 rpm and 30 mm/min with 60 °C preheating, and poor weldability observed at 1000 rpm without preheating due to wormhole formation. These findings provide process-driven design guidelines for developing robust HDPE joints and demonstrate how integrating computational modeling with machine learning enables efficient optimization, offering valuable insights into the key characteristics of the FSW process in thermoplastics and supporting its broader adoption in industrial applications.

Friction stir welding (FSW) is a promising welding technology and is being extensively researched because it can produce high-quality joints. However, attendant process variability such as improper clamping, uneven workpiece flatness and other process variations changes is known to lead to joints with inconsistent material properties. A large number of studies have indicated that axial force control can be used to achieve good welding quality. This work describes the development of a closed-loop control system which is equipped with a custom real-time wireless force dynamometer for friction stir welding. A multi-level adaptive fuzzy PID control approach for maintaining constant axial force is designed. Implementation of the control scheme is conducted on a vertical milling machine configured for FSW using an open architecture controller. The experimental results demonstrate that the controller maintains the constant axial force and shows desirable dynamic behavior, even when the disturbance is encountered during the welding process.

Various methods have been proposed to modify the friction stir welding. Friction stir vibration welding and underwater friction stir welding are two variants of this technique. In friction stir vibration welding, the adjoining workpieces are vibrated normal to the joint line while friction stir welding is carried out, while in underwater friction stir welding the friction stir welding process is performed underwater. The effects of these modified versions of friction stir welding on the microstructure and mechanical characteristics of AA6061-T6 aluminum alloy welded joints are analyzed and compared with the joints fabricated by conventional friction stir welding. The results indicate that grain size decreases from about 57 μm for friction stir welding to around 34 μm for friction stir vibration welding and about 23 μm for underwater friction stir welding. The results also confirm the evolution of Mg 2 Si precipitates during all processes. Friction stir vibration welding and underwater friction stir welding processes can effectively decrease the size and interparticle distance of precipitates. The strength and ductility of underwater friction stir welding and friction stir vibration welding processed samples are higher than those of the friction stir welding processed sample, and the highest strength and ductility are obtained for underwater friction stir welding processed samples. The underwater friction stir welding and friction stir vibration welding processed samples exhibit about 25% and 10% higher tensile strength compared to the friction stir welding processed sample, respectively. The results also indicate that higher compressive residual stresses are developed as underwater friction stir welding and friction stir vibration welding are applied.

Friction Stir Welding (FSW) is fairly a recent technique that uses a non-consumable rotating welding tool to generate frictional heat and plastic deformation at the welding location while the material is in solid state. The principal advantages are low distortion, absence of melt related defects and high joint strength. Tool design and material plays a vital role in addition to the important parameters like tool rotational speed, welding speed and axial force. Friction Stir Welding (FSW) is a solid state welding process to join materials by generating frictional heat between a rotating tool and materials being welded. It was invented at The Welding Institute (TWI), Cambridge (U.K.) in 1991. Since then FSW has become a major joining process in the aerospace, railway and ship building industries especially in the fabrication of aluminium alloys. It is difficult to weld the aluminium alloys, using arc welding, gas welding and other welding processes. Friction Stir Welding on the other hand, can be used to join most Al alloys and better surface finish is achieved. Although the work piece does heat up during friction stir weld, the temperature does not reach the melting point. In this research work, rotational speed and traversing speed was considered for friction stir welding butt joint of Al alloy 2014 T4 & 6061 T6 alloys and further mechanical properties such as tensile strength, Vickers hardness and micro structure was studied.

Comparison of energy consumption and environmental impact of friction stir welding and gas metal arc welding for aluminum

Friction- stir- welding (FSW) is comparatively a new welding process invented by The Welding Institute in 1991. Since no melting or fusion occurs during the welding process, FSW is free of high heat input and solidification defects. Two main objectives have been set forth in this work. The first object of this thesis is to develop thermomechanical models based on experimental knowledge and understanding the FSW process at a fundamental level. The quality of friction-stir-welding (FSW) joints depends on many critical weld process parameters. The main challenge for the FSW is in the selection of these critical process parameters that would produce a defect-free weld joint. For a particular pin tool, spindle rotational speed, welding speed, plunge rate and vertical plunge force are considered as key factors to generate heat during welding process. In this work, a temperature dependent friction coefficient is employed that takes into account both sticking and sliding friction conditions. Furthermore, both strain rate independent and strain rate dependent plasticity model was applied to develop FSW. Moreover, heat generation as a process in itself is modeled by accounting for friction heat and plastic deformation between tool/boundary conditions. To demonstrate the validity of the model, the model is applied to different weld schedules of Aluminum AA2219 alloys. Finally, the developed model is used to carry out parametric studies on the effect of process parameters such as rotational speed, welding speed, plunge rate and plunge force on heat generation during FSW. This parametric study helps to give an insight into creating defect free weld joints. Also the effect of process parameters on the quality of predictions using Coulomb and modified Coulomb models of FSW has been analyzed in this current work. A second objective of the research is to improve fatigue life of defect free AA2219 Friction welded joints by Post Weld Heat Treatment (PWHT). Later fatigue life was experimentally compared with base material, as welded specimen and post weld heat treated specimen. Post weld heat treated specimens have higher fatigue strength compare to as welded specimen.

Friction stir welding - FSW is a relatively new welding process, which is increasingly used in industry, due to the advantages which it has in relation to conventional processes (by melting and adhesions). The advantages of the FSW welding process (as a solid phase welding process) are all the more obvious in the case of joining copper and its alloys, because they have a high melting temperature and high thermal diffusivity. The influence of the technological parameters of the process, the tool rotational speed and the welding feed, on the temperature and the axial force, as well as on the quality of the joint surface is presented. The study shows that the stabilization of the process takes place after a certain time from the beginning of the advance stage and highlighted the major influence of the tool rotational speed on the process temperature, surface defects and the roughness of the joint surface.

Friction stir welding (FSW) is gaining traction as a preferred technique due to its potential to reduce heat input and enhance the mechanical properties of welded joints. However, the path to commercializing FSW for flange joints is not without challenges. Two primary obstacles are the complexity of the welding path and the intricate design requirements for the fixtures. These factors contribute to the difficulty in determining the ideal weld settings and process parameters, which are critical for achieving optimal results. The current study addresses these challenges by applying FSW to flange joints using custom-engineered fixtures. These fixtures are meticulously designed to hold the pipes and plates securely during the welding process. The focus of the research is on optimizing the multi-performance characteristics of FSW for Al 6063 flange joints through the hybrid Grey-based Taguchi method. The integrity of the weld joint is assessed by examining various mechanical properties within the weld zone, including rotation speed, travel speed, tool profile, and shoulder diameter. The study identifies the optimal parameter settings for the FSW process: a rotation speed of 3000 rpm, a travel speed of 3 mm/min2, a shoulder diameter of 20 mm, and a conical tool profile. Under these ideal conditions, the welded material exhibited a tensile strength of 170.169 MPa, a hardness of 63.7709 HV, and a corrosion rate of 0.022 mm/year. These findings underscore the effectiveness of the optimized FSW process in producing robust and durable flange joints.

In the present work, effect of gas tungsten arc welding (GTAW), gas metal arc welding (GMAZ) and friction stir welding (FSW) processes on microstructure and mechanical properties of AA7005 alloy were investigated. In general, conventional welding process like GTAW and GMAW is preferred by industries but they produce a joint efficiency of 55%. In this study, welding of AA7005 alloy was carried out with ER 5183 filler wire using GTAW and GMAW welding and without filler using solid-state FSW. Coarse grain with wide HAZ was observed in conventional welding which resulted in a reduction in tensile strength by 37 to 43%. Nevertheless, only a 17% reduction in strength was observed in FSW weld due to fine equiaxed grain size and precipitates in the weld. A joint efficiency of 83% was achieved by the FSW process.

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A Fully Coupled Thermomechanical Model of Friction Stir Welding (FSW) and Numerical Studies on Process Parameters of Lightweight Aluminum Alloy Joints

On the low-cycle fatigue behavior of friction stir welded Al–Si12 parts produced by selective laser melting

Prediction of Tensile Strength in Friction Stir Welded Aluminium Alloy Using Artificial Neural Network