Sukhomay Pal

Effect of Preheating in Hybrid Friction Stir Welding of Aluminum Alloy

The controlled energy input into the system by introducing an extra heat source to enhance the material flow along with reduction of the plunging force remains a potential area of considerate for the development of hybrid friction stir welding (FSW) process. Hence, the effect of preheating on the weld joint properties is evaluated using plasma-assisted friction stir welding (P-FSW) process for joining aluminum alloy. A comparative study of mechanical and macro-microstructural characterizations of weld joint by FSW and P-FSW has been performed. Transverse tensile strength of weld joint is approximately 95% of base metal produced by P-FSW and is 8% more than conventional FSW welds. The effect of preheating enhances material flow and dissolution of fine oxide particles by plasma arc results in increase of strength and marginal modification of deformation behavior. The preheating brings uniformly distributed hardness in weld zone and the magnitude is higher in the advancing side with overall increase in average hardness value. Grain sizes are much finer due to the pinning effect of Al2O3 particles that retarded grain growth following recrystallization during P-FSW and thus led to more pronounced reduction in grain size and relatively brittle fracture during tensile loading of welded joint. Overall, the influence of preheating acts quite homogeneously throughout the structure as compared to conventional FSW. However, the results reveal that the development of P-FSW is still in initial stage and needs to improve in various aspects.

Heat transfer analyses in friction stir welding of aluminium alloy

This work is aimed to develop a heat transfer model of friction stir welding process and subsequently to utilize the same for transient thermal analysis under differential influence of process parameters. The heat generation is assumed due to friction and plastic deformation at the tool–workpiece interface. A contact state variable is defined to estimate the amount of heat generation due to plastic deformation. The symmetric heat flux at the interfaces of flat tool shoulder surface, tool pin side, and bottom surfaces acts as heat input to the system by neglecting the effect of transverse tool speed. The heat generation from both the side and bottom surfaces of pin plays a significant role for the development of the temperature. Thermal history of friction stir welded AA1100 and AA6061 is estimated by developed numerical model and is compared with experimental results under similar welding conditions, thus validating the developed model. The experiments reveal that the temperature distributions are not symmetric with respect to welding line and maximum temperature occurs behind the tool pin. With the addition of heat generation due to plastic deformation, the heat transfer model precisely predicts the maximum temperature and time–temperature profiles at different welding conditions.

Effect of FSW parameters on microstructure and mechanical properties of AM20 welds

ABSTRACT This paper aims to demonstrate the successful friction stir welding (FSW) conditions of AM20 magnesium alloy. The maximum yield strength and ultimate tensile strength of weld were found to be 75% and 65% of the base metal strength, respectively. The maximum bending angle of the welded joint was 45°. Observations revealed that less plunging depth, high shoulder diameter, and low tool rotational speed and welding speed give better tensile properties. Maximum temperature was observed at 1 mm away from the tool shoulder toward the advancing side. Micro-hardness variation is found to be decreasing along the depth of the weld, and nugget zone (NZ) gives the higher hardness values when compared with base material (BM) and other welded zones. Needle-like grains of the BM became equiaxed grains due to grain recrystalized by the FSW process. The grains in the NZ were finer than thermo-mechanically affected zone and almost same size of grains observed at bottom, middle, and top of the NZ.

On the effect of tool offset in hybrid-FSW of copper-aluminium alloy

ABSTRACT Tool offset is one the most significant parameters in joining of dissimilar materials by friction stir welding (FSW) process. An investigation is carried out on the effect of tool offset toward thermal history, material flow pattern, mechanical properties, welding force, and weld joint morphology. It was found that offsetting toward aluminum side along with a plasma-assisted heat source is an efficient approach to address one of the most important apprehensions in aluminum-copper solid-state welding process. The offset influences the amount of intermetallic at the joint interface and in-effect impacts on final strength and material flow behavior. The optimum and continuous layer of intermetallic produces the maximum weld joint strength. The specimen welded with optimum tool offset shows the highest strength using 55 A plasma current in hybrid friction stir welding process.

Optimization of friction stir welding process parameters using soft computing techniques

In welding processes, desired weld quality is highly dependent on the selection of optimal process conditions. In this work, the influence of input parameters of friction stir welding process is studied using Taguchi method and full factorial design of experiment. The experimental data set is used to develop multilayer feed-forward artificial neural network (ANN) models using back-propagation training algorithm. These models are used to predict weld qualities as a function of eight process parameters. The weld qualities of the welded joint, such as ultimate tensile strength, yield stress, percentage elongation, bending angle and hardness, are considered. In order to offline optimize these quality characteristics, four evolutionary algorithms, namely binary-coded genetic algorithm, real-coded genetic algorithm, differential evolution and particle swarm optimization, are coupled with the developed ANN models. The optimized quality characteristics obtained from these proposed techniques are compared and verified with experimental results.

Optimization of friction stir welding process using NSGA-II and DEMO

In welding processes, the selection of optimal process parameter settings is very important to achieve best weld qualities. In this work, neuro-multi-objective evolutionary algorithms (EAs) are proposed to optimize the process parameters in friction stir welding process. Artificial neural network (ANN) models are developed for the simulation of the correlation between process parameters and mechanical properties of the weld using back-propagation algorithm. The weld qualities of the weld joint, such as ultimate tensile strength, yield stress, elongation, bending angle and hardness of the nugget zone, are considered. In order to optimize those quality characteristics, two multi-objective EAs that are non-dominated sorting genetic algorithm II and differential evolution for multi-objective are coupled with the developed ANN models. In the end, multi-criteria decision-making method which is technique for order preference by similarity to the ideal solution is applied on the Pareto front to extract the best solutions. Comparisons are conducted between results obtained from the proposed techniques, and confirmation experiments are performed to verify the simulated results.

Monitoring of friction stir welding process using weld image information

Monitoring friction stir welding process based on weld images is attempted in this research. Well-known fractal theory is applied to images of the welds and extracted features in terms of fractal dimension are correlated to ultimate tensile strength (UTS) of the joints. The correlation shows a decreasing trend that can be an indicator towards monitoring of weld quality. Apart from fractal theory, wavelet transform is also applied to the acquired images and an indicator is proposed relating the information gathered after the decomposition. Interestingly, the proposed indicator also describes a decreasing trend of UTS with the increase in its value. The proposed approaches can be effectively applied in real-time monitoring of the process with appreciable accuracy.

Hybrid Friction Stir Welding of Similar and Dissimilar Materials

Hybrid friction stir welding is an innovative solid-state joining technology which has great potential to produce effective and defect-free joint for similar materials and dissimilar materials irrespective of high chemical affinity and completely different physical and mechanical properties like aluminium and copper. Among the possible preheating source, plasma arc provides unique combination of high arc stability, concentrated energy density and low equipment cost. Plasma arc usually coupled with various manufacturing system in order to enhance the performance of conventional machining and bonding processes. Hence, plasma-assisted friction stir welding (P-FSW), as a hybrid system, is investigated in order to improve the weld joint quality and joint efficiency. Preheating effect using plasma arc, the P-FSW of aluminium alloy and other high-strength alloys enables to decrease the plunging force and enhance mechanical properties of welded joint. The integration of plasma arc on FSW tool also aids to decrease the probability of formation of welding defects. In present work, an overview of plasma-assisted friction stir welding (P-FSW) is presented by means of experimental investigation and prediction of it through numerical modelling. Finite element-based simulation using ABAQUS is carried out to evaluate the temperature profiles. A comparative study of mechanical and macro-microstructural characterizations of weld joint by conventional FSW and P-FSW processes has been conducted on similar (AA1030) and dissimilar (AA1100-pure copper) materials joining. Overall, the influence of preheating acts quite homogeneously throughout the structure as compared to conventional FSW. However, the results reveal that the development of P-FSW is still in initial stage and needs to improve in various aspects. Although P-FSW process is quite effective to improve mechanical properties and reduction of plunging forces, it needs to investigate the potentiality for relatively harder materials and dissimilar materials joining.

Machine vision system based monitoring approach for friction stir welding process

Development of intelligent systems for the monitoring of manufacturing processes is always in great demand. In this study, an approach based on image processing through 2D wavelet transform is proposed for monitoring of weld quality in friction stir welding process. Welding experiments are conducted over three ranges of two most influencing process parameters; tool rotational speed and welding speed. Top surface images of the welds are captured and converted to binary images for the analysis using wavelet transformation. The technique of peak signal to noise ratio is executed to find the best mother wavelet function out of 53 available functions for optimum decomposition. All the images are decomposed to second level and the coefficients from the approximations of the images are computed. In this work, a novel indicator based on double logarithmic values of average root mean square of the approximation coefficients of the image is introduced to study the behaviour of weld joint strength. It is observed that, with the increase in the proposed indicator, ultimate tensile strength of the joints is found to follow a decreasing trend. The proposed methodology can be implemented as a tool based on machine vision system for the monitoring of the process with less human intervention.

Monitoring of Weld Quality in Friction Stir Welding Based on Spindle Speed and Motor Current Signals

The process of friction stir welding passed more than two decades since its invention in the year 1991 in TWI, UK. It involves complex physics and has not been explored fully to understand its physical behavior. Due to the lack of precise mathematical modeling and too many influencing factors that govern the welding process, difficulty arises in direct monitoring of the process based on the process parameters only. Moreover, the influencing parameters are so correlated that the effect of one on the weld quality cannot be isolated from the others for effective monitoring of the process. Thus, a need is realized to develop different methods for the efficacious monitoring of the process with the acquired signals during welding for better control over the outcome of the process. In this study, effectiveness of spindle speed and main motor current signals is investigated for the development of tools which will lead to real-time weld quality prediction.