A Review on different optimization techniques used to optimize the process parameters of Resistance spot welding

Transcription

1 A Review on different optimization techniques used to optimize the process parameters of Resistance spot welding Kamran Rasheed 1, Dr. M.I.Khan 2 1 Assistant professor, Mechanical engineering, Integral University, Lucknow- India 2 Professor, Mechanical engineering, Integral University, Lucknow- India ABSTRACT- Resistance spot welding process is one of the very useful and widely utilized joining processes in automobile industry due to its high working speed, high productivity, flexibility and economical cost operation. In this paper various optimization techniques like Taguchi method, Artificial neural network (ANN), Genetic algorithm (GA) etc. and their applications are discussed to get the optimum weld parameters. These optimum weld parameters affect the quality of weld and help to improve the quality of weld product in design phase. Key words- Resistance spot welding, weld parameters, Taguchi method, ANN, GA, Response surface method 1. INTRODUCTION Resistance spot welding (RSW) is a major sheet metal joining process in many industries, such as the automobile, domestic appliances, air craft and space craft fabrications. It is an efficient joining process widely used for the fabrication of sheet metal assemblies. There are spot welds in any car, which shows the level importance of the resistance spot welding. RSW has excellent technoeconomic benefits such as low cost, high production rate and adaptability for automation which make it an attractive choice for auto-body assemblies, truck cabins, rail vehicles and home appliances. It is one of the oldest of the electric welding processes in use by industry today. Furthermore, other metal-to-metal connections, such as wire-to-wire joints in the electronics industry, are accomplished by resistance spot welding. Application-specific measures, such as the diameter of the welding spot, determine the quality of the joint. 65 Fig.1 Resistance welding set up

2 The weld is made by a combination of heat, pressure, and time. As the name implies, it uses the resistance of the materials to the flow of current that causes localized heating between the parts to be joined. Understanding of physical mechanisms for easily manipulating and controlling weld qualities in advance is important [9]. 2. PRINCIPLE OF RESISTANCE SPOT WELDING In Resistance spot welding process a heavy current is passed for short time through the area of interface of metals and by applying pressure metals to be joined. In resistance spot welding process there is no fluxes are used, and filler metal rarely used. Resistance welding operations are normally automatic and, therefore, all process parameters are pre-set and maintained constant. Heat is generated in localized area which is enough to heat the metal to sufficient temperature, so that the parts can be joined with the application of pressure. 3. HEAT GENERATION IN RESISTANCE SPOT WELDING Heat generation in resistance spot welding is based on the Ohm s law. The amount of heat generated in an electrical conductive work piece depends on mainly three parameters welding current, resistance of work piece and time of current flow. The amount of heat generated can be calculated as- H = I 2 RT H = Heat generated I = Welding Current (A) R = Resistance T= time of current flow (sec) A major part of heat generated in resistance spot welding is used to melt the metal and to create the weld pool, and remaining part of heat is dissipated in the surrounding through work piece and electrodes. Heat generated in RSW can be controlled by adjusting the weld parameters. Every work piece has its own specific resistance and thermal conductivity coefficients which are the properties of the material and cannot be changed. 4. PROCESS PARAMETERS OF RESISTANCE SPOT WELDING a. Welding current Welding current is an important parameter in resistance spot welding. Diameter of the weld, root penetration and strength of the weld is dependent on the welding current. A little increase in the welding current creates a rapid change in the above factors. 66

3 b. Welding time The heat generation in RSW process is directly proportional to the welding time. A minimum welding time is needed to make high quality weld. Welding time is divided in four basic stages- i. Squeeze time- The time between the initial application of the electrode force on the work piece and the first application of the welding current. ii. Weld time- The time during which the welding current is applied to the work piece. iii. Hold time- The time during which force is applied at the point of welding after last pulse of welding current ceases. iv. Off time- It is the time duration in which the electrodes are off the work. c. Weld force Weld force is another important parameter of resistance spot welding. Welding force affects the contact resistance and contact area at the weld interface. The proper and consistent welding force improves the mating of the materials, increasing the current paths, reducing the interface resistance and also removes oxides barriers between work pieces. d. Contact resistance Contact resistance is also a major influential parameter at the weld surface. Generally contact resistance decreases with increasing temperature. At the metal surface many impurities are available (i.e. oil, dirt, oxides etc.). As temperature increases, few of these impurities burn with the metal in initial cycles. These impurities softened at high temperature and this causes decrease in contact resistance with increase in temperature. e. Material properties Normally all material properties are changes with respect to temperature. Resistivity of material, thermal conductivity and heat capacity of the material affects the heat generation during welding process. Hardness of the material also affects the contact resistance at weld surface. Harder materials having high contact resistance for the same weld force. f. Surface coatings Materials surfaces are offered by some kind of corrosion resistant surface coatings. These surface coatings also affect the welding process. Separate adjustments of process parameters are made according to the individual type of surface coatings. Surface coatings are selected systematically to make the favourable welding conditions at weld surface. 5. APPLICATION OF VARIOUS OPTIMIZATION TECHNIQUES USED TO OPTIMIZED THE PROCESS PARAMETERS OF RESISTANCE SPOT WELDING 67

4 Pradeep M et al present an approach to find out the optimum weld parameters in joining the dissimilar thickness materials. Welding of dissimilar thickness materials is difficult task and weld parameters are not generally available in standards for thickness beyond 4mm. Low carbon steel sheet having chemical composition of (w t %) C, Mn, S, P, (balance) Fe and having thickness of 0.8 mm and metal strips of cross section 10 x 5mm is used for process parameters optimization.. Taguchi quality design method is used for weld current and time optimization using L9 orthogonal array. Optimum process parameters (current- 3.5kA and time- 10 cycles) were obtained from the Taguchi analysis and shear test results [1]. Suresh R K investigates a systematic approach to determine the effect of process variable such as welding current, weld time and electrode size on tensile shear strength. The experiments are conducted on mild steel corrugated sheet of 1mm thick, 18mm width and 150mm length was used and the welding electrodes used are copper Taguchi s Design of Experiments (DOE) and orthogonal array method is used to investigate the results. In the present work spot welding parameters such as Weld current, Weld time and Electrode diameter takes as the input parameters, remaining spot welding parameters Squeezing time, Hold time, Sheet thickness, Material combination and Electrode force kept constant. L27 orthogonal array was selected for this study. A total of 27 experiments based on Taguchi L27 mixed level orthogonal array were carried out with mixed combinations of the input parameters. It is observed that the optimal conditions for maximum tensile strength are at weld current 17.5amps, welding time 15sec, and electrode diameter 3 mm. ANOVA shows that the percentage contribution of weld current (63.70%) is the dominant parameter followed by weld time (28.70%) for tensile shear strength [2]. Darshan Shah et al focused on the development and evaluation of neural network-based systems for finding the optimum process parameters for resistance spot welding and weld quality assessment. There are three different input parameters are utilized to check the weld strength of the resistance spot welding. Parametric analysis is carried out for the quality of the resistance spot welding, i.e. weld strength. This parametric analysis (ANOVA) shows the percentage contribution of parameters individually, i.e. welding current is %, thickness of % and cycle time of 2.61 % and the error is of 9.62 %. Neuro solution 6.0 is used for prediction of experimental work. It is found that weld strength will be increase as welding current increases and weld strength will be decrease as thickness of the material increase [3]. Arvinder Singh et al presents an approach to determine the effect of the process parameters (pressure, weld time and welding current) on tensile strength of resistance weld joint for austenitic stainless steel AISI 316L with the help of Taguchi method. Experimental investigation on resistance spot welding has been carried out using L9 Taguchi orthogonal array. Nine standard combinations of input parameters have been tried to get the better results. L-9 orthogonal array results show that 68

5 Welding current has significant impact on the tensile strength of nugget. The best suitable value of input parameters were found as follows Pressure 3.1 kn, Welding time 4 ms, Welding current 15 ka. The output value maximum tensile strength is found to be N/mm 2 [4]. Panchakshari et al have done a comparative study of responses of resistance spot welding process obtained from three methodologies of Genetic algorithm, Design of Experiments and Response surface method. Low carbon steel is used as a testing material for this research work. Mathematical model developed by using regression analysis and ANOVA. Trails are done on NASH ROBOTIC resistance spot welding machine. Results obtained from the experiments are cross validating by using D-optimal method. Nugget diameter and strength of weld should be equal to 5.4 mm and 290 N/mm 2 respectively. Weld cycle and welding current are most effective factors for welding to obtain above mentioned values of nugget diameter and strength of weld, both these variables should be at middle values of their range at 12 cycles and 11.2 KA respectively. Hold cycle time is less significant, to obtain optimal result it should always at it mid value of range that is 20 cycle. Squeeze cycle take part in formation of nugget diameter and to maintain strength of weld it should be at its high value of range equal to 30 cycles [5]. Kadam et al have studied the effects of process parameters of resistance spot welding on Nugget Diameter & Strength of weld with the help of a Non-Linear method like Genetic Algorithm to optimize these parameters. The aim of this research is to develop a relation of responses like nugget diameter and weld strength. Low carbon cold rolled EDD grade material is used for this study. Corresponding mathematical model based on factorial regression & ANOVA developed which expresses generalized relation between responses and variable input process parameters. The result of this study shows that the nugget size and strength of weld is affected by weld parameters. As the number of generation increases size of nugget diameter is also increases. Genetic algorithm methodology provides an alternative method for obtaining maximum value of nugget diameter [6]. Pandey et al investigate the effect of various weld parameters like weld current, welding time and pressure force on the quality of weld strength with the help of Taguchi method. The experiments are performed on low carbon cold rolled 0.9 mm thick mild steel sheets under varying pressure, weld current and welding time. Experimental results show that the response of S/N ratio with respect to tensile strength indicates the welding current is most significant parameter that controls the weld tensile strength. The contribution of welding current holding time and pressure towards tensile strength is 61%, 28.7%and 4% respectively as determined by the ANOVA method. Optimum results have been found by Taguchi method using medium current of 6.8 KA, medium pressure of 0.79KPa and high holding time of 5 seconds [7]. Thakur et al, focused on an experimental analysis for optimization of Tensile Shear strength of RSW for Galvanized steel by using Taguchi method. Due to zinc coating alloying with electrode resistance spot welding is difficult for galvanized steel. The experimental studies were conducted 69

6 under varying welding current, welding time, electrode diameter and electrode force. Taguchi quality design method of L27 orthogonal array has been used to determine Strength to Noise(S/N ratio), Analysis of Variance (ANOVA) and F test value for finding the most significant parameters affecting the spot weld performance. On the basis of ANOVA method, the highly effective parameters on tensile shear strength are welding current and welding time, whereas electrode force and electrode diameter were less effective factors. The results showed that welding current was about two times more important than the second factor weld time for controlling the tensile shear strength. An optimum parameter combination for the maximum tensile shear strength was obtained by using the analysis of S/N ratio. The confirmation tests indicated that it is possible to increase tensile shear strength significantly (13.43 %) by using the proposed statistical technique [8]. Hamed Pashazadeh et al had investigated the effect of welding parameters (welding time, welding current and welding pressure) on the weld nugget dimensions including the weld nugget diameter and height using full factorial design of experiments. They also used hybrid combination of the artificial neural networks and multi-objective genetic algorithm in order to achieve the best nugget sizes. Finally the number of spots which should be welded before the electrode tip dressing operation was calculated. The materials used in the present work are AISI 1008 commercially steel sheets. In this study it is found that the welding current is the most important parameter affecting the nugget dimensions. With increasing electrical current the nugget diameter increases and also increasing the welding time leads to a relative increase in the nugget diameter. The optimum values of welding process parameters are found successfully with the help of hybrid combination of the artificial neural networks (ANNs) and Multi Objective Genetic It are found that, the range of 591 up to 615 spot welds is suitable for the tip dressing operation in the studied welding operations [9]. Niranjan Kumar Singh et al present a systematic approach to determine effect of process parameters on indentation as a primary & initial measure of weld quality and subsequently tensile strength, nugget diameter and penetration. The experiments have been conducted on Austenitic Stainless Steel grade 301L to select important welding parameters like welding current, weld cycle, hold time & cool cycle using Taguchi method. To have wide spectrum of analysis and variability with time, L32 Orthogonal Array (OA) experiments are conducted. Minitab 14 software is used for the analysis part. Experiments are conducted in two phasesi. The indentation at old level before conducting the taguchi experiment. ii. The results of indentation after conducting the taguchi experiments. Results of indentation before conducting Taguchi experiment show that the average indentation is 0.87 mm and data follows normal distribution because p value is more than 0.05 and the results of indentation after conducting Taguchi experiment show that residuals follow normal distribution and do not show any pattern with time and fitted value. An optimum parameter combination for the 70

7 nominal indentation was obtained using the cube plot. The experimental results confirmed the validity of Taguchi method for optimizing the process parameter in resistance spot welding [10]. 6. CONCLUSION The present study is an overview of latest research works in resistance spot welding. The literature review provides brief information about the application of Taguchi method, artificial neural network, genetic algorithm, response surface and D optimal method used to optimized the weld parameters to get the high quality weld joint. REFERNCES 1. Pradeep M., N. S. Mahesh, Raja Hussain Process Parameter Optimization in Resistance Spot Welding of Dissimilar Thickness Materials World Academy of Science, Engineering and Technology International Journal of Mechanical, Industrial Science and Engineering Vol:8 No:1, R.K. Suresh Parameter Optimization for Tensile Shear Strength during Spot Welding Of Corrugated Mild Steel Plates VSRD International Journal of Mechanical, Civil, Automobile and Production Engineering, Vol. IV Issue VII July 2014 e-issn: , p-issn: VSRD International Journals. 3. Darshan Shah, Prof. Dhaval P Patel Prediction of Weld Strength of Resistance Spot Welding Using Artificial Neural Network Darshan Shah et al Int. Journal of Engineering Research and Applications, ISSN : , Vol. 3, Issue 5, Sep-Oct 2013, pp ). 4. Arvinder Singh, Vanraj, Kant Suman, Suri N.M Parameter optimization for tensile strength of spot welds for 316l stainless steel International Journal of Scientific & Engineering Research, Volume 4, Issue 7, July-2013, ISSN A.S.Panchakshari, Dr.M.S.Kadam Comparative Study of Responses of Resistance Spot Welding Obtained From Genetic Algorithm, Response Surface and D- Optimal Method International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 4, July 2013, ISSN: ). 6. A.S. Panchakshari and Dr. M.S. Kadam, Optimization of the Process Parameters in Resistance Spot Welding Using Genetic Algorithm International Journal Of Multidisciplinary Sciences And Engineering, Vol. 4, No. 3, March 2013) 7. A. K. Pandey, M. I. Khan, K. M. Moeed Optimization of resistance spot welding parameters using Taguchi method A. K. Pandey et al. / International Journal of Engineering Science and Technology (IJEST), ISSN : Vol. 5 No.02 February A. G. Thakur, T. E. Rao, M. S. Mukhedkar and V. M. Nandedkar Application of Taguchi method for resistance spot welding of galvanized steel VOL. 5, NO. 11, November 2010 ISSN , ARPN Journal of Engineering and Applied Sciences. 71

8 9. Hamed Pashazadeh,Yousof Gheisari, Mohsen Hamedi, Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multiobjective genetic algorithm Received: 7 January 2014 / Accepted: 24 February 2014 Springer Science+Business Media New York 2014, J Intell Manuf DOI /s Mr. Niranjan Kumar Singh and Dr. Y. Vijayakumar Application of Taguchi method for optimization of resistance spot welding of austenitic stainless steel AISI 301L Innovative Systems Design and Engineering, ISSN (Paper) ISSN (Online) Vol 3, No 10,