Modelling and Optimization of Friction Stir Welding Parameters for Dissimilar Aluminium Alloys Using RSM

Elatharasan, G.; Kumar, V.S. Senthil

doi:10.1016/j.proeng.2012.06.401

Cited by 49 publications

(27 citation statements)

References 8 publications

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“…Response surface method [3] is a more common method of parameter optimization by building regression model between the response variables and significant impact factor and optimizing the process to determine the parameters of the optimal level combination. The paper establish Response surface model between the design vector, temperature(x 1 ) and time(x 2 ), and the response vector, transmittance(Y), by using Minitab15 software.…”

Section: Response Surface Methodsmentioning

confidence: 99%

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Neural network-based method for anti-glare glass preparation process parameters optimization

Yu¹,

Hongqi²,

Niu³

2016

Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering

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Abstract. Along with the urban development, the application of anti-glare glass is more and more widely, high transmittance and low reflection of glass manufacturing technology research is of great significance. Because of the complexity of the anti-glare glass preparation technology, anti-glare glass transmittance is affected by multiple factors. Because of complexity nonlinear relation between the real production data, response surface method can't solve the problem of anti-glare glass preparation process parameters optimization. The BP neural network is proposed in this paper to structure the complex nonlinear model between the design vector with the response vector. BP neural network has high learning and representation ability and have ability by establishing a good mapping model. Using BP neural network model of high generalization ability on the optimal parameter combination optimization search about the corrosion condition of glass, with less test data to get the ideal parameter design.

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Section: Response Surface Methodsmentioning

confidence: 99%

“…Comprehensive consideration after analysis, the BP neural network model parameters optimization temperature level range is set in interval [1,4], and the time level rang is set in interval [3,5].…”

Section: Construction Of Bp Neural Network Optimization Modelmentioning

confidence: 99%

Neural network-based method for anti-glare glass preparation process parameters optimization

Yu¹,

Hongqi²,

Niu³

2016

Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering

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“…ANOVA was used to examine the adequacy and the confidence interval of the mathematical model (6). In the ANOVA analysis F value is used to examine the adequacy of the mathematical model.…”

Section: Lab Shear Strengthmentioning

confidence: 99%

“…Among the various statistical methods desirability approach resolve the response problems effectively (6). It is simple, flexible and easily accessible in MINTAB 17.It has dimensionless number it between 0 to 1.The predicted optimal results from the desirability value on lap shear strength as 22.96MPa and the composite desirability values is 0.9953.…”

Section: Effect Of Process Parameters On Lap Shear Strengthmentioning

confidence: 99%

Modeling and Optimization of Diffusion Bonding Parameters for Aluminium Aa6061 � Sic Composites Using Response Surface Methodology

Sittaramane¹,

Mahendran²

2015

Int. Jour. Des. Manf. Tech.

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The Diffusion Bonding (DB) process parameters play an important role in joining characteristics of similar and dissimilar metals. Response surface methodology (RSM) technique is used to predict Lap shear strength of the diffusion bonded Aluminium AA6061-SiC composites (ASC). The experiments were conducted based on three factors namely bonding temperature, bonding pressure and bonding time. Using RSM technique empirical relationship developed by utilizing the parameters such as bonding temperature, bonding pressure and bonding time on lap shear.

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“…Regression analysis (Jayaraman et al 2009;Mohanty et al 2012;Rajakumar et al 2011a), response surface methodology Elatharasan and Kumar 2012;Palanivel and Mathews 2012;Rajakumar et al 2011b), and ANN (Ocuyucu et al 2007;Boldsaikhan et al 2011;Ghetiya and Patel 2014;Chiteka 2014;Fahd 2014;Shojaeefard et al 2013;Tansel et al 2010;Rezgui et al 2013) are different tools used for the modeling of weld strength. Among these, ANN was mostly used because of its robustness and flexibility in modeling complex processes where precise mathematical formulation is not available as FSW process.…”

Section: Modeling Of Weld Qualitymentioning

confidence: 99%

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

Das

Pal

Bag

2015

Advances in Material Forming and Joining

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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.

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Modelling and Optimization of Friction Stir Welding Parameters for Dissimilar Aluminium Alloys Using RSM

Cited by 49 publications

References 8 publications

Neural network-based method for anti-glare glass preparation process parameters optimization

Neural network-based method for anti-glare glass preparation process parameters optimization

Modeling and Optimization of Diffusion Bonding Parameters for Aluminium Aa6061 � Sic Composites Using Response Surface Methodology

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

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