Indian Journal of Engineering & Materials Sciences Vol. 18, October 2011, pp. 351-360 Weld residual stress prediction using artificial neural network and Fuzzy logic modeling J Edwin Raja Dhas a * & Somasundaram Kumanan b a Department of Automobile Engineering, Noorul Islam University, Nagercoil 629 180, India b Department of Production Engineering, National Institute of Technology, Tiruchirappalli 620 015, India Received 26 February 2010; accepted 11 October 2011 Artificial intelligent tools such as expert systems, artificial neural network and fuzzy logic support decision-making are being used in intelligent manufacturing systems. Success of intelligent manufacturing systems depends on effective and efficient utilization of intelligent tools. Weld residual stress depends on different process parameters and its prediction and control is a challenge to the researchers. In this paper, intelligent predictive techniques artificial neural network (ANN) and fuzzy logic models are developed for weld residual stress prediction. The models are developed using Matlab toolbox functions. Data set required to train the models are obtained through finite element simulation. Results from the fuzzy model are compared with the developed artificial neural network model, and these models are also validated. Keywords: Weld residual stress, Artificial neural network, Fuzzy logic, Finite element analysis Weld residual stress is a major parameter in evaluating the quality of weldments. Quality of weld plays an important role in the performance of a welded product as it improves fatigue strength, corrosion resistance, creep life and reduces rework and scrap. Due to intense concentration of heat in heat source of welding, the regions near the weld line undergo severe thermal cycles, thereby generating inhomogeneous plastic deformation and residual stresses in weldment. Welding-induced residual stresses play an important role in the function of welded structures. Different experimental methods for directly measuring welding residual stresses are available like X-ray diffraction 1 , Neutron diffraction, Deep hole drilling 2,3 holographic interferometry 4 . All these methods require special equipments and are expensive. These techniques are limited in obtaining the entire picture of the residual stress distribution in weldment. In 1971, Ueda 5 applied finite element method to analyse thermal elastic-plastics stress and strain during welding and Nomoto 6 pioneered finite element method to analyze the thermal stress during welding. Muhammad et al. 7 investigated the finite element simulation of laser beam welding induced residual stresses and distortions in thin sheets. Andres et al. 8 applied finite element models to analyze the thermal and mechanical phenomena observed in welding processes. Although the finite element method has emerged as one of the most attractive approaches for computing residual stresses in welded joints, its application to practical analysis and design problems has been hampered by computational difficulties and also this method of obtaining residual stresses is not feasible for all welding parameters. Lee et al. 9 used multiple regression analysis for prediction of process parameters for gas metal arc welding and Yang et al. 10 used linear regression equations for modeling the submerged arc welds. Due to the inadequacy and inefficiency of the mathematical models to explain the nonlinear properties existing between the input and output parameters, intelligent systems such as ANN, fuzzy logic and expert system have emerged. ANN technique 11 is used to handle problems of nonlinearity. Jeongick et al. 12 utilized ANN technique for back-bead prediction of gas metal arc welding process. Nagesh et al. 13 employed ANN to predict weld bead geometry in shielded metal-arc welding process. Kim et al. 14 applied ANN to predict bead height in robotic arc welding. Edwin et al. 15 used ANN to predict weld bead width using artificial neural networks. Hakan Ates 16 applied ANN technique for prediction of gas metal arc welding parameters. —————— *Corresponding author (E-mail: [email protected])
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Indian Journal of Engineering & Materials Sciences
Vol. 18, October 2011, pp. 351-360
Weld residual stress prediction using artificial neural network and
Fuzzy logic modeling
J Edwin Raja Dhasa*
& Somasundaram Kumanan
b
aDepartment of Automobile Engineering, Noorul Islam University, Nagercoil 629 180, India bDepartment of Production Engineering, National Institute of Technology, Tiruchirappalli 620 015, India
Received 26 February 2010; accepted 11 October 2011
Artificial intelligent tools such as expert systems, artificial neural network and fuzzy logic support decision-making are
being used in intelligent manufacturing systems. Success of intelligent manufacturing systems depends on effective and
efficient utilization of intelligent tools. Weld residual stress depends on different process parameters and its prediction and
control is a challenge to the researchers. In this paper, intelligent predictive techniques artificial neural network (ANN) and
fuzzy logic models are developed for weld residual stress prediction. The models are developed using Matlab toolbox
functions. Data set required to train the models are obtained through finite element simulation. Results from the fuzzy model
are compared with the developed artificial neural network model, and these models are also validated.