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Modeling of Process Parameters of Friction Stud Welding Using Fuzzy Logic System N Hynes et al., International Journal of Advanced Engineering Technology E-ISSN 0976-3945 Research Paper MODELING OF PROCESS PARAMETERS OF FRICTION STUD WELDING USING FUZZY LOGIC SYSTEM N. Rajesh Jesudoss Hynes1, R.Kumar2, J. Angela Jennifa Sujana3 Address for Correspondence 1Associate Professor, Department of Mechanical Engineering, Mepco Schlenk Engineering College Sivakasi,Tamilnadu, India 626 005. 2 Research Scholar, Department of Mechanical Engineering, Mepco Schlenk Engineering College Sivakasi, Tamilnadu, India 626 005. 3Senior Assistant Professor, Department of Information Technology, Mepco Schlenk Engineering College Sivakasi, Tamilnadu, India 626 005. India ABSTRACT The purpose of this work is to study the influence of process parameters of friction stud welding on joining of AA 6063 and AISI 1030 steel. With the application of fuzzy logic, empirical models are developed for output response characteristics. Rotational speed, friction time and friction pressure are considered as the influential input process parameters. The values of Impact strength and axial shortening are predicted by fuzzy models and they are compared with the experimental data. The correlation coefficients between predicted and experimental values are 99.15% for impact strength and 98.05% for axial shortening length respectively. Moreover through response optimizer, to achieve impact strength of 206.46 KJ/m2 and axial shortening length 7.3798 mm optimal conditions are predicted as 1063 rpm rotational speed, 7 seconds friction time and 300 Kpa friction pressure. KEYWORDS Friction stud welding, Fuzzy logic, Multi-objective optimization. INTRODUCTION technique for optimizing the process parameters to Friction stud welding is a solid phase welding achieve the maximum tensile strength. Karupanan et performance involving a stud or appurtenance being al. (2014) have examined the process parameters of rotated at high speed while being forced against a friction welding of AISI 904L/ Austenitic Stainless substrate, producing heat by friction Ates et al. Steel. In that study, they conducted a set of (2007); Ellis (1972). The metal surfaces reach a experiments based on Taguchi L18 orthogonal array temperature as a result of which they flow plastically where parameters such as frictional power, burn-off under pressure, surface impurities are ejected and a length, upset power and rotational speed were forged weld is formed. The maximum temperatures involved. They used grey relational analysis and reached for the duration of welding are much lower genetic algorithm to identify the significant factor than the melting point of the metals. It is a novel and optimised level of input parameters on the variant of friction welding process. Various factors responses like fatigue life, width and welding time such as rotational speed, friction time, friction respectively. Ajith et al. (2015) studied the influence pressure, forging pressure etc., impact the quality of of process parameters like friction pressure, upsetting friction stud welded joints Ozdemir et al. (2007); pressure, rotational speed and burn-off length on the Mumin et al. (2008). Benefits of this welding process responses like tensile strength and hardness during are low production time, high reproducibility and low friction welding of UNS S32205 duplex stainless consumption of energy Fauzi et al. (2010); Ozdemir steel. They used artificial neural network and particle et al. (2005).The friction stud welding process is swarm optimization technique to predict the ideally suited for deep water naval applications, optimised parameters. They have developed short-term emergency repairs and for submarine regression model followed by response surface rescue Rajesh Jesudoss Hynes et al (2012 & 2014). methodology method and optimised the process Ocean engineers pursued commercial joining parameters using genetic algorithm. Manideep et al. applications for offshore platform repairs. (2012) optimised the process parameters of friction Additionally, Aluminum/stainless steel friction stud welding of AISI 321/ AISI 430 joints using Taguchi welded joints are unavoidable in pipes of tanks of L9 orthogonal array method. In that work they liquid propellants in Satellite Launch Vehicles and considered process parameters such as friction rocket fuel tanks Alves et al. (2010). Recently, there pressure, upset pressure and burn-off length (metal is a need of joining a steel ear thing pin, which is in loss). They analysed the metallurgical characteristics the shape of a stud to an aluminum car body Rajesh and micro hardness across the weld interface. Paulraj Jesudoss Hynes et al (2014 & 2015). et al. (2006) have analysed the effect of process Optimization of friction welding parameters is to be parameters of friction welding such as heating carried out in order to achieve best results. In the pressure, heating time, upsetting pressure, upsetting previous years some of the researchers had optimised time on tensile strength and metal loss using the process parameters of friction welding on multi- integrated Taguchi and ANOVA (Analysis of response characteristics. Paventhan et al. (2011 & variance) approach. The present work modelling the 2012) studied the influence of process parameters in process parameters of friction stud welding of friction welding of carbon steel/AISI 304 and AA dissimilar AA 6063/AISI 1030 joints by means of 6082/AISI 304 joints. They considered input process fuzzy logic Kovac et al. (2013) and optimized parameters as friction pressure, forging pressure, through response optimizer with the intention of friction time and forging time. They have maximizing the impact strength and minimizing axial implemented response surface methodology shortening length (metal loss). Table 1: Composition of AA 6063 Int J Adv Engg Tech/Vol. VII/Issue I/Jan.-March.,2016/413-417 Hynes et al., International Journal of Advanced Engineering Technology E-ISSN 0976-3945 Table 2: Composition of AISI 1030 steel EXPERIMENTATION welding process is carried out in four stages. In the In this work, dissimilar metals AA 6063 and AISI first phase, axial load and rotational speed of the mild 1030 steel are joined by friction stud welding process steel rod are applied and the two rods are brought and the resulting mechanical properties are close to the joined surfaces. During the second stage, investigated. The chemical combinations of AA 6063 friction heating takes place at the interface the two and AISI 1030 steel are as shown in table1 & 2. rods. At the third phase, when the rotational speed Quality of any friction welded components depends was stopped and upsetting under increased pressure on the appropriate selection of the process occurs simultaneously. In the final phase, the parameters. Hence, the influence of the process pressure remains constant until the axial pressure is parameters such as friction time, friction pressure and released. According to orthogonal L9 array, the speed of rotation on the impact strength and metal experiment was carried out for different loss of the friction welded AA6063/AISI 1030 joints combinations of rotational speed, friction time and are studied in this present work. The friction stud friction pressure as shown in table 3 & 4. Table 3 Input process parameters and their levels Table 4 Experimental results FUZZY LOGIC ANALYSIS a fuzzy variable has been described. Afterwards Generally soft computing techniques are helpful allocate the suitable membership functions for both when accurate mathematical modelling does not exist input variables and the output response and these techniques vary from traditional computing characteristics. In the final steps, fuzzy rules are because of its inexactness, ambiguity, and partial created. The membership function is a graphical reality. Fuzzy logic is the one of the soft computing illustration of the degree of contribution of each method that take part in a prominent role in order to variable. However there are several numbers of create the relationship in between input parameters membership functions that are recommend such as with their response characteristics. Fuzzy Logic has triangular (trimf), Trapezoidal (trapmf), Gaussian and been employed to find out the output parameters by bell-shaped (gbelmf), Gaussian (gaussmf), Gaussian means of Matlab software Kovac et al. (2013). It combinational (gauss2mf), Sigmoidal (sigmf), etc. involves following three process) fuzzification b), The graphical demonstration of fuzzy logic system is fuzzy inference) and defuzzification. In the first step shown in figure 1. Fig. 1 Fuzzy logic system Int J Adv Engg Tech/Vol. VII/Issue I/Jan.-March.,2016/413-417 Hynes et al., International Journal of Advanced Engineering Technology E-ISSN 0976-3945 Fig. 2 Membership function for (a) Rotational speed (b) Friction time (c) Friction Pressure where p, q, r show the process parameters and y is a variable. Input process parameters are segregated into three membership functions; they are small (S), medium (M) and large (L). Similarly output parameters are separated into nine membership functions; very very small (VVS), very small (VS), small (S), medium low (ML), medium (M), medium high (MH), high (H), very high (VH), very very high (VVH) each. In order to characterize the connection between the inputs and outputs variables, fuzzy If- then rules are effectively employed. It can be specified as follows: Rule 1: if y1 is P1 and y2 is Q1, and y3 is R1then z1 is S1 and z2 is T1else Rule 2: if y1 is P2 and y2 is Q2, and y3 is R2then z1 is S2
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