Optimization of process parameters of aluminum alloy AA 2014-T6 friction stir welds by response surface methodology

Kadaganchi, Ramanjaneyulu; Gankidi, Madhusudhan Reddy; Gokhale, Hina

doi:10.1016/j.dt.2015.03.003

Cited by 145 publications

(68 citation statements)

References 14 publications

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“…These reinforcement materials enhanced various properties of matrix material such as tensile strength, hardness etc. However, sometimes toughness and ductility are reduced [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Microstructure and Mechanical Behaviour of Al/SiC/Agro-Waste RHA Hybrid Metal Matrix Composite

Dwivedi¹,

Srivastava²,

Maurya³

et al. 2020

RCMA

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In the present investigation, an attempt was made to utilize RHA in the development of aluminium based composite material. AA6061 alloy was melted in muffle furnace. Preheated RHA and SiC were added in the aluminium alloy. Composite material was fabricated by stir casting technique. Microstructure image showed proper wettability of reinforcement material with aluminium alloy matrix. Uniform distribution was observed of reinforcement particles in aluminium alloy. It has been observed that by using the RHA and SiC simultaneously in aluminium alloy, mechanical properties were improved significantly. However; toughness and ductility were reduced. Heat treatment of composite material was also carried out to observe the enhancement of mechanical property. Results showed that after the heat treatment process, mechanical properties were further improved.

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“…These reinforcement materials enhanced various properties of matrix material such as tensile strength, hardness etc. However, sometimes toughness and ductility are reduced [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Microstructure and Mechanical Behaviour of Al/SiC/Agro-Waste RHA Hybrid Metal Matrix Composite

Dwivedi¹,

Srivastava²,

Maurya³

et al. 2020

RCMA

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show abstract

“…Significant input parameters such as tool pin profile, rotational speed, welding speed, tool tilt angle, etc. are responsible for weld quality [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] . Tool geometry is a very critical factor for the success of the FSW process.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore presently researchers prefer response surface design to design the experiments and analyze the welding parameters. Several works have been carried out on the RSM based design and analysis of FSW between aluminium alloy [17][18][19][20][21] .…”

Section: Introductionmentioning

confidence: 99%

Optimization of Multiple Performance Characteristics of Friction Stir Welded Joint with Grey Relational Analysis

Shamsudeen

Dhas

2018

Mat. Res.

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In the present work, multi-objective optimization of the input welding parameters influencing the quality of weld joint of AA 5052 H32 aluminium alloy by FSW process is investigated using response surface methodology and grey relational analysis. The quality of the welded joint is measured based on the higher value of tensile strength and hardness of the joint obtained by experimentation. A central composite design technique consists of 31 sets of experiments using four factors and five levels are used. Tool pin profile, rotational speed, welding speed, and tool tilt angle are selected as the significant input parameters. Analysis of variance (ANOVA) is used to analyze the effect of each input welding parameters on the output responses. Statistical results show that the developed regression model is adequate. Optimum levels of welding parameters are finally identified using grey relation grade. Grain structure of the welded joint is examined by observing the micro and macro structures of the weld.

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“…The output parameters for the dissimilar metals are difficult to achieve due to distinctive heat coefficients, the base metal chemical composition, and the properties of heat-treated aluminium and non-heat-treated aluminium alloy [6]. The study of the optimized effect of the welding 2 Modelling and Simulation in Engineering parameters on 5 mm thick AA6082 aluminium plates using ANOVA technique is based on L8 orthogonal array showing that the tool's rotational, welding, and plunge speed are the significant parameters in deciding the strengths and percentage elongation [7,8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The mathematical model with process parameters and tool geometry to predict the responses such as yield strength, tensile strength, and ductility of friction stir welds of the AA2014-T6 aluminium alloy are formulated [6] with empirical relations. Optimization of the process parameters to achieve high tensile strength to find the effect of the process parameters of the FSW joint for similar aluminium alloys H30 helped in developing a practical approach of determining the optimum conditions leading to higher tensile strength [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical Modelling of a Friction Stir Welding Process to Predict the Joint Strength of Two Dissimilar Aluminium Alloys Using Experimental Data and Genetic Programming

Yunus

Alsoufi

2018

Modelling and Simulation in Engineering

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Friction stir welding (FSW) is the most popular and efficient method of solid-state joining for similar as well as dissimilar metals and alloys. It is mostly used in applications for aerospace, rail, automotive, and marine industries. Many researchers are currently working with different perspectives on this FSW process for various combinations of materials. The general input process parameters are the thickness of the plate, axial load, rotational speed, welding speed, and tilt angle. The output parameters are joint hardness, % of elongation, and impact and yield strengths. Genetic programming (GP) is a relatively new method of evolutionary computing with the principal advantage of this approach being to evaluate efficacious predictive mathematical models or equations without any prior assumption regarding the possible form of the functional relationship. This paper both defines and illustrates how GP can be applied to the FSW process to derive precise relationships between the output and input parameters in order to obtain a generalized prediction model. A GP model will assist engineers in quantifying the performance of FSW, and the results from this study can then be utilized to estimate future requirements based on the historical data to provide a robust solution. The obtained results from the GP models showed good agreement with experimental and target data at an average prediction error of 0.72%.

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Optimization of process parameters of aluminum alloy AA 2014-T6 friction stir welds by response surface methodology

Cited by 145 publications

References 14 publications

Microstructure and Mechanical Behaviour of Al/SiC/Agro-Waste RHA Hybrid Metal Matrix Composite

Microstructure and Mechanical Behaviour of Al/SiC/Agro-Waste RHA Hybrid Metal Matrix Composite

Optimization of Multiple Performance Characteristics of Friction Stir Welded Joint with Grey Relational Analysis

Mathematical Modelling of a Friction Stir Welding Process to Predict the Joint Strength of Two Dissimilar Aluminium Alloys Using Experimental Data and Genetic Programming

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