Model-based proposal of a control strategy for friction stir welding

In this study, Al-5083 and St-12 alloy sheets were friction stir lap welded at different travel (7–23 cm min−1) and rotation (750–1125 rev min−1) speeds of the welding tool. The welded joints were characterised by various methods including shear tensile and Vickers microhardness tests, optical and scanning electron microscopies and X-ray diffraction analysis. The results showed that the weld zone defects decreased, and the joint strength also improved significantly with the reducing tool travel speed from 23 to 7 cm min−1. The travel speed of 11 cm min−1 was an optimum speed within the travel speeds investigated in this work. Additionally, raising the tool rotation speed enhanced the joint strength slightly. The microhardness results confirmed the formation of intermetallic phase layer with the hardness of ∼335 HV at the swirl layered structure.

Friction stir welding process has been studied extensively in the last decades since its early stage. Most of the research done so far is related to the process development including tool design, material weldability, post-weld mechanical behavior, and microstructural properties. More recently, in-line process monitoring and artificial intelligence algorithms are introduced into this process, but mainly to specific material configuration and joint thicknesses. This study will focus on the evaluation of different machine learning approaches including principle component analysis, K-nearest neighbor, multilayer perceptron, single vector machine, and random forest methods on a friction stir welding cell environment. The input variables provided from this cell environment are namely divided into two groups: one group refers to the application variables and the other group is related to the friction stir welding process variables. The application variables target the aluminum alloys, joint configuration, sheet thicknesses, initial mechanical properties, and their chemical composition. The friction stir welding process variables dictate the rotational speed, travel speed, forging force, longitudinal and transverse forces, torque, and specific energy. The output response to model from these machine learning algorithms is the defect index, which has been quantified using high-resolution immersed bath ultrasounds. This nondestructive evaluation technique has been described previously, which can detect defects ≥150 µm in thin sheets. The defect index has been classified into five classes, which is distinguished by the nature of defect, cold weld, or hot weld, as well as the width of the internal volumetric defect upon ultrasound C-scan result. The dataset, which is composed of around 500 various process conditions, has been generated over the last few years and the variables were taken exclusively in constant weld regime and in the force control mode using the output average values. This paper compares the best resulting machine learning methods applied on a friction stir welding cell basis, which is the K-nearest neighbor and multilayer perceptron algorithms. The K-nearest neighbor model reaches a deviation of 0.55 on the defect index in comparison with the experimental values, which is slightly better than the multilayer perceptron model, which obtains a score of 0.69. Over the initial 59 available model parameters, 10 and 15 of them were retained in the final algorithm using these techniques. The main predictors include the material thickness, base material ultimate tensile stress, rotational speed, travel speed, weld forces, and specific energy. The K-nearest neighbor model was able to provide a map of defect indices with regard to rotational speed and travel speed but was only possible when a higher density of data was found within the prediction area. A data density score was also included within the model to inform the end-user about the prediction reliability. The machine learning models are mainly about differentiating various cases rather than representing the physical phenomena as determined using the finite element analysis. That being said, in order to improve the prediction reliability as well as the machine learning models, the data twinning concept, which consists of generating simulated friction stir welding process conditions by finite element analysis, is briefly discussed.

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

Prediction of multiple characteristics of Friction-Stir welded joints by Levenberg Marquardt algorithm based artificial neural network

Investigating the robust bonding between dissimilar materials such as aluminium alloys and polymers is vital in welding processes. This research delves into the structural interface and mechanical attributes achieved through aluminium alloy 6061 (AA6061) and Polypropylene (PP) friction stir welding by varying traverse and rotational speeds. A travel speed was varied with a range of 50–80 mm/min, and the tool rotational speed between 1300 and 1900 rpm was investigated to analyse their impact on mechanical properties. The lap joint configuration in which the PP is at the top and AA6061 is at the bottom was chosen from trial experiments. A scanning electron microscope observed that many locations had a tight bond in most interface areas. The average hardness values at the stir zone in the joints at different rotational speeds initially increased up to 1700 rpm due to fine fragmentation and proper distribution of these aluminium chips, and then reduced after rising to 1900 rpm. Evaluation of weld quality encompassed assessments of weld nugget hardness and lap tensile shear strength, elucidating the mechanical interlocking mechanism between AA6061 particles and PP as a critical factor contributing to joint stability. The intersection of aluminium chips and solidified polymer contributes to enhancement in the shear strength due to the phenomenon known as mechanical interlocking. The highest tensile shear strength of 5.4 MPa was observed at a rotational speed of 1700 rpm and a traverse speed of 60 mm/min, signifying optimal process conditions for enhancing mechanical integrity.

Ensuring consistent surface quality in friction stir welding (FSW) remains a challenge due to the complex interaction between process parameters such as axial force, rotational speed, and travel speed that govern heat generation and material flow. This study presents a data-driven and physically interpretable machine learning framework to predict weld surface quality directly from these parameters. Thirty robot-assisted FSW trials were performed on sand cast aluminium plates, varying axial force (4–7 kN), rotational speed (700–1700 rpm), and travel speed (180–300 mm min −1 ) at a constant tool tilt of 1°. The resulting welds were classified into four surface-quality categories: good weld, flash/burr, surface roughness, and surface void. Four models logistic regression, support vector machine, random forest, and artificial neural network were trained and validated using stratified five-fold cross validation. The support vector machine achieved the highest performance, with 81.8 % accuracy and a balanced precision recall score of approximately 0.77. Decision-boundary maps and calibrated probability predictions identified an optimal defect-free process window (≈ 4–6 kN, 1200–1500 rpm, ≥ 300 mm min −1 ), consistent with known thermo-mechanical behaviour in FSW. The proposed framework integrates predictive analytics with process physics, offering a deployable foundation for real-time, data-driven quality assurance in automated FSW.

'Friction stir welding' (FSW) is an eco-friendly, energy-efficient solid-state welding procedure invented for high strength alloys as well as materials that are otherwise difficult for welding through the traditional fusion welding methods. This research paper elaborates on a hybrid Taguchi-genetic algorithm that optimises the FSW process parameters to yield favourable electrical conductivity of aluminium alloy AA1350. The optimisation parameters considered are travel speed, rotational speed and tool tilt angle. First, the Taguchi method is applied to reduce the number of design experiments and find the optimal set for quality parameters of the system. Subsequently, the genetic algorithm is employed to search for the optimum set of design parameters by using Taguchi solution as the initial population. The results obtained by the proposed method were compared to the outcomes of the conventional Taguchi L9 method and the simple genetic algorithm. The best performance was obtained by the proposed method in 51 iterations, whereas simple genetic algorithm used 182 iterations for the same.

This paper is to study of dissimilar welding AA6061 aluminium alloy and AZ31B magnesium alloy with ER5356 filler using friction stir welding. 2 mm thick plates of aluminium and magnesium were used. Friction stir welding operations were performed at different rotation and travel speeds and used the fixed tilt angle which is 3°. The rotation speeds varied from 800 to 1100 rpm, and the travel speed varied from 80 to 100 mm/min. In the range rotation speed of 800 to 1000 rpm and welding speed of 80 to 100 mm/min there are no defect at the weld. Tensile test show the higher tensile strength is 198 MPa and the welding efficiency is about 76%.

Current friction stir welding (FSW) process modeling research is mainly concerned with the detailed analysis of local effects such as material flow, heat generation, etc. These detailed thermomechanical models are typically solved using finite element or finite difference schemes and require substantial computational effort to determine temperature, forces, etc., at a single point in time, or for a very short time range. Dynamic models describing the total forces acting on the tool throughout the entire welding process are required for the design of feedback control strategies and improved process planning and analysis. In this paper, empirical models relating the process parameters (i.e., plunge depth, travel speed, and rotation speed) to the process variables (i.e., axial, path, and normal forces) are developed to understand their dynamic relationships. First, the steady-state relationships between the process parameters and the process variables are constructed, and the relative importance of each process parameter on each process variable is determined. Next, the dynamic characteristics of the process variables are determined using recursive least-squares. The results indicate the steady-state relationship between the process parameters and the process variables is well characterized by a nonlinear power relationship, and the dynamic responses are well characterized by low-order linear equations. Experiments are conducted that validate the developed FSW dynamic models.

Microstructural degradation control and dissimilar joint optimization through friction stir processing

The joint conditions of a weldment entirely rely on the set of process parameters applied during the welding operation. In friction stir welding (FSW), proper mechanical mixing of the material signifies the appropriate welding variables. The present work aspires to investigate the influence of tool rotational speed (RS), welding speed (WS) and plunge depth (PD) on external as well as internal defect formation, identify the different types of defect encountered during the FSW process, and evaluate the influence of different parameters on tensile properties and microstructure of the joints. In order to achieve the goal of the present work, a detailed experimental investigation was carried out using AA5086-O as base metal. On account of high strength-to-weight ratio, good weldability and high corrosion resistance, friction stir welded joints of aluminum alloys are widely applications in aerospace, automotive, railway and shipbuilding industry. It has been observed that theultimate tensile strength (UTS) of joint tends to decrease when there is increased in the welding speed at constant PD of 0.2 mm and RS of 1400 rpm. Similarly, with consistent PD of 0.3 mm and rotational speed of 2000 rpm, UTS of joints initially enhanced with an increment in welding speed and then reduced eventually. A joint fabricated with maximum rotation speed, intermediate welding speed and the higher plunge depth produced highest tensile strength of 215 MPa which is 16% higher than the conditions required for aerospace applications of FSW for aluminum alloys, as per the standard of American Welding Society (AWS). Microscopic analysis was conducted to scientifically ascertain the grain size, crystal structure and surface morphology of the FSW joints.

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.

The application of lightweight materials in the automotive industry can effectively achieve further weight reduction while maintaining overall structural strength, thereby reducing energy consumption. Currently, friction stir spot welding (FSSW) is the primary method for joining carbon fiber-reinforced polyphenylene sulfide (CF-PPS) with aluminum alloys. This study successfully achieved the connection between 6061-T6 aluminum alloy and CF-PPS using the more operationally convenient friction stir lap welding (FSLW) technique. The primary objective of this study was to explore the potential of expanding the welding technologies available for successfully joining these two dissimilar materials. The joint morphology and strength were analyzed through metallographic observation and tensile testing, and the effects of different welding parameters on the microstructure and mechanical properties of dissimilar joints were studied. The study demonstrated that the successful connection between AA6061-T6 and CF-PPS was primarily attributable to the combined effects of mechanical interlocking and mixture bonding. The joint strength demonstrated a maximum value of 9.41 MPa when the following parameters were set: a rotation speed of 1800 rpm, a welding speed of 40 mm/min, and a plunge depth of 0.2 mm. Although low rotation speed and low welding speed cannot form an effective mechanical interlocking structure for the joint, the failed joints have different causes. When the rotation and welding speeds are fixed, changing the plunge depth cannot change the interlocking structure of the joint. A larger plunge depth will thin the weld and greatly reduce the joint strength.

<div class="htmlview paragraph">In this study, the factors involved in producing a swept friction stir spot weld (FSSW) are investigated to determine their effect on mechanical properties and weld joint morphology. The essential variables involved in producing linear friction stir welds (FSW), namely tool rotation speed, travel speed, and plunge depth, are also significant variables in producing swept FSSW. In addition to these variables, placement of the advancing side, spot diameter, lead angle, and start/stop location can also be influential. A design of experiments (DOE) approach was used to investigate the effect of each of these variables based on unguided lap shear tensile strength and examination of the metallographic cross sections in 1 mm (0.040-in.) thick 7075 and 2024 aluminum alloys.</div>

This work examines the influence of the rolling direction of plate stocks of the AA2024 aluminium alloy on the structure and properties of weld joints in different modes during friction stir welding (FSW). It has been established that during FSW, when the instrument's load on the material ranges from 1900 to 100 kg, the instrument's rotation rate— from 250 to 450 rpm, and the instrument's travel speed—from 100 to 500 mm/min, it is possible to obtain defect-free samples. It has been determined that during welding of D16 aluminium alloy 5 mm thick, the best results are obtained during FSW in the rolling direction, at that the rolling direction does influence the structure of weld joints. It has been shown that during welding of plate stocks in the transverse direction of rolling, the strength of the weld seam is no more than 88% of the strength of the base metal, whereas weld joints, obtained in the rolling direction, reach 91–92% of the strength of the base metal. Weld joints, obtained in the rolling direction, also have the highest values of the relative elongation (from 2 to 28%) in comparison with those of the samples, obtained in the transverse direction of rolling of AA2024.