Modelling for tensile strength of friction welded aluminium pipes by ANFIS

In this study, fuzzy model has been proposed to predict the tensile strength of radial friction welded aluminium pipes. The model is of multi-input single-output (MISO) type having two inputs signals; rotational speed (RPM) and forge load and tensile strength as output. The adaptive neuro-fuzzy inference system (ANFIS) technique of fuzzy based systems for modelling and simulation of the complex systems has been employed. The performance of the model is authenticated by evaluating the predicted results with the practical results obtained by conducting the confirmation experiments. The proposed model can be used for intelligent online adaptive control mode.

[1]  Lotfi A. Zadeh,et al.  Commonsense Knowledge Representation Based on Fuzzy Logic , 1983, Computer.

[2]  Lotfi A. Zadeh,et al.  Fuzzy Algorithms , 1968, Inf. Control..

[3]  A. Şengur Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification , 2008 .

[4]  Chitralekha Mahanta,et al.  A novel approach for ANFIS modelling based on full factorial design , 2008, Appl. Soft Comput..

[5]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[6]  D. Yapp,et al.  Recent developments in high productivity pipeline welding , 2004 .

[7]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[8]  Elif Derya íbeyli Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of ophthalmic arterial disorders , 2008 .

[9]  W. A. Baeslack,et al.  Friction welding of a rapidly solidfied Al-Fe-V-Si alloy , 1992 .

[10]  Jagdev Singh,et al.  MULTI INPUT SINGLE OUTPUT FUZZY MODEL TO PREDICT TENSILE STRENGTH OF RADIAL FRICTION WELDED GI PIPES , 2008 .

[11]  Engin Avci,et al.  Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system , 2008, Appl. Soft Comput..

[12]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[13]  Li-Chih Ying,et al.  Using adaptive network based fuzzy inference system to forecast regional electricity loads , 2008 .