Prediction of tensile strength of friction stir weld joints with adaptive neuro-fuzzy inference system (ANFIS) and neural network
暂无分享,去创建一个
T. Warren Liao | Daniel J. Huggett | Ayman M. Okeil | Mohammad W. Dewan | M. A. Wahab | T. Liao | M. Wahab | A. Okeil | M. W. Dewan | D. Huggett | Muhammad A. Wahab | Warren Liao | Mohammad W. Dewan¹ | Daniel J. Huggett¹ | T. W. Liao¹ | Muhammad A. Wahab¹ | Ayman M. Okeil²
[1] T. W. Liao,et al. Model based optimisation of friction stir welding processes , 2009 .
[2] Kaizhu Huang,et al. Field Support Vector Machines , 2017, IEEE Transactions on Emerging Topics in Computational Intelligence.
[3] C. Muralidharan,et al. Predicting tensile strength, hardness and corrosion rate of friction stir welded AA6061-T6 aluminium alloy joints , 2011 .
[4] Deniz Baş,et al. Modeling and optimization I: Usability of response surface methodology , 2007 .
[5] D. S. Nagesh,et al. Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks , 2002 .
[6] Chih-Hung Tsai,et al. Optimization of wire electrical discharge machining for pure tungsten using a neural network integrated simulated annealing approach , 2010, Expert Syst. Appl..
[7] M. Jayaraman,et al. Prediction of Tensile Strength of Friction Stir Welded A356 Cast Aluminium Alloy Using Response Surface Methodology and Artificial Neural Network , 2008 .
[8] C. Muralidharan,et al. Establishing Empirical Relationships to Predict Grain Size and Tensile Strength of Friction Stir Welded AA 6061-T6 Aluminium Alloy Joints , 2010 .
[9] S. Babajanzade Roshan,et al. Optimization of friction stir welding process of AA7075 aluminum alloy to achieve desirable mechanical properties using ANFIS models and simulated annealing algorithm , 2013, The International Journal of Advanced Manufacturing Technology.
[10] Habibollah Haron,et al. Optimization of process parameters in the abrasive waterjet machining using integrated SA-GA , 2011, Appl. Soft Comput..
[11] Sheng Chen,et al. Identification of nonlinear systems using generalized kernel models , 2005, IEEE Transactions on Control Systems Technology.
[12] T. Warren Liao,et al. Classification of welding flaw types with fuzzy expert systems , 2003, Expert Syst. Appl..
[13] Serge Guillaume,et al. Designing fuzzy inference systems from data: An interpretability-oriented review , 2001, IEEE Trans. Fuzzy Syst..
[14] Mostafa Akbari,et al. Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm , 2013 .
[15] V. Balasubramanian,et al. Establishing relationships between mechanical properties of aluminium alloys and optimised friction stir welding process parameters , 2012 .
[16] Mehdi Jalali-Heravi,et al. Comparison of Shuffling-Adaptive Neuro Fuzzy Inference System (Shuffling-ANFIS) with Conventional ANFIS as Feature Selection Methods for Nonlinear Systems , 2007 .
[17] B. Parida,et al. Fuzzy assisted grey Taguchi approach for optimisation of multiple weld quality properties in friction stir welding process , 2015 .
[18] Erol Arcaklioğlu,et al. Artificial neural network application to the friction stir welding of aluminum plates , 2007 .
[19] S. Muthukumaran,et al. An analysis to optimize the process parameters of friction stir welded low alloy steel plates , 2013 .
[20] Jyh-Shing Roger Jang,et al. ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..
[21] H. Doude,et al. Optimizing weld quality of a friction stir welded aluminum alloy , 2015 .
[22] I. Dinaharan,et al. Optimization of friction stir welding process to maximize tensile strength of AA6061/ZrB2 in-situ composite butt joints , 2012, Metals and Materials International.
[23] Yahya Bozkurt,et al. The optimization of friction stir welding process parameters to achieve maximum tensile strength in polyethylene sheets , 2012 .
[24] M. Bilici,et al. Application of Taguchi approach to optimize friction stir spot welding parameters of polypropylene , 2012 .
[25] V. Balasubramanian,et al. Developing an Empirical Relationship to Predict Tensile Strength of Friction Stir Welded AA2219 Aluminum Alloy , 2008, Journal of Materials Engineering and Performance.
[26] Michio Sugeno,et al. Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.
[27] Ibrahim N. Tansel,et al. Optimizations of friction stir welding of aluminum alloy by using genetically optimized neural network , 2010 .
[28] Yusuke Tanaka,et al. Cross-Validation, Bootstrap, and Support Vector Machines , 2011, Adv. Artif. Neural Syst..
[29] V. Balasubramanian,et al. Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints , 2009 .
[30] Dong-Mok Lee,et al. Optimization of electric discharge machining using simulated annealing , 2009 .
[31] R. Mishraa,et al. Friction Stir Welding And Processing , 2005 .
[32] Anthony P. Reynolds,et al. Process response parameter relationships in aluminium alloy friction stir welds , 2007 .
[33] A. Kokabi,et al. The influence of the ratio of “rotational speed/traverse speed” (ω/v) on mechanical properties of AZ31 friction stir welds , 2006 .
[34] Sunil Pandey,et al. Microstructure Evolution during Friction Stir Welding of Aluminum Alloy AA2219 , 2010 .
[35] Zhongya Zhang,et al. Artificial neural networks applied to polymer composites: a review , 2003 .
[36] N. Murugan,et al. Prediction of tensile strength of friction stir welded aluminium matrix TiCp particulate reinforced composite , 2011 .
[37] G. Karsai,et al. Artificial neural networks applied to arc welding process modeling and control , 1989, Conference Record of the IEEE Industry Applications Society Annual Meeting,.
[38] N. D. Ghetiya,et al. Prediction of Tensile Strength in Friction Stir Welded Aluminium Alloy Using Artificial Neural Network , 2014 .
[39] Ning Wang,et al. Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness , 2011 .