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Parametric optimization of resistance spot welding for multi spot welded lap shear specimen to predict weld strength
Materials Today: Proceedings xxx (xxxx) xxx
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Parametric optimization of resistance spot welding for multi spot welded lap shear specimen to predict weld strength P.A. Dhawale ⇑, B.P. Ronge SVERI’s College of Engineering and Research, Pandharpur 413304, India
a r t i c l e
i n f o
Article history: Received 19 July 2019 Accepted 24 July 2019 Available online xxxx Keywords: Number of spots Distance between the spots Taguchi method Particle swarm optimization Tensile shear strength
a b s t r a c t This paper examining the impact of welding parameters, by parametric investigation of multi spot welded lap shear specimen, on the tensile shear strength of spot welded specimen. Further, study is carried out, to develop mathematical model by using regression analysis which shows relationship between selected welding parameters and weld strength. The design parameters considered are specimen thickness, number of spots and distance between the spots while process parameters are electrode force and weld current. As per Taguchi design of experiment approach, five welding parameters with three levels of each are considered for analysis. Experimentation is carried out as per L27 orthogonal array to investigate the effect of process and design parameters on tensile shear strength. Particle swarm optimization (PSO) method was used to determine welding parameter has a significant impact on weld strength. Tests for confirmation have been conducted to validate the results. Confirmation tests exhibit that deviation between the results obtained by experimentation and regression is up to 0.571741%. Particle swarm optimization explore that maximum tensile shear strength 9399.55 N is obtained at 06 numbers of spot and 16 mm distance between the spots as optimum value for 1.22 mm galvanized steel sheets. Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Conference on Manufacturing, Material Science and Engineering.
1. Introduction Resistance spot welding (RSW) is a conventional welding technique used to join thin metal sheets, due to the resistance to the flow of electric current which will generate the heat at the joint, where nugget is not visible outside. The joining of sheet metal is a combination of a mechanical, electrical, thermal and metallurgical phenomenon. It is different from other welding processes, as the filler material is not required. It has astounding technomonetary advantages, for example, ease, high creation rate and flexibility for mechanization which settles on it an appealing decision for vehicle -body gatherings, bus lodges, railways, and home appliances. The transport sector is having tremendous application of spot welded joint [1]. This process is regular for welding sheets of various metals like steels, titanium, aluminium alloys, and so on. It is a sophisticated amongst the most prepared of the electric welding forms being used by industry today. Interfacial mode of failure was observed after tensile shear test of spot welded galva-
⇑ Corresponding author Tel.: +91 8412053701. E-mail address: [email protected] (P.A. Dhawale).
nized sheets at the permissible level of parameters [2]. Impact of design and process parameters on the tensile strength of galvanized steel sheets are examined through investigations. This examination comprises of utilization of the Taguchi strategy to think about the impact of process and design parameters on the quality of welded joints [3]. The quality of spot weld characterizes structural stability of the automobile and improves unwavering quality of assembled sheets [4]. The author found that increase in shear strength of spot welds with increments in processing time [5]. The fatigue strength of the lap shear specimen is prepared and the importance of nugget diameter spot weld is evaluated [6]. Examine the improvement and impact of process parameters on the quality of AISI 301L stainless steel spot weld. ANOVA was used to control the level of significance of the welding parameters. [7]. Spot welding of greased up sheet brings about a more uniform and diminished alloyed material take-up that stores on the welding anode cap [8]. Examine the part of the zinc layer in RSW of aluminum to steel through correlation of microstructures and mechanical properties of the resistance spot welds amongst aluminum and low carbon steels [9,10]. In spite of the fact that this work author establishes a mathematical model by changing welding input parameters as indicted by the results from design of
https://doi.org/10.1016/j.matpr.2019.07.756 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Conference on Manufacturing, Material Science and Engineering.
Please cite this article as: P. A. Dhawale and B. P. Ronge, Parametric optimization of resistance spot welding for multi spot welded lap shear specimen to predict weld strength, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.756
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experiments and quality of the spot weld [11]. The Taguchi technique, a prominent experimental design method used in the industry, can mitigate the limitations of full factorial design and methodologies the optimization of parameter design, although the number of experiments is reduced [14]. Possibility of producing even one or two defective welds needs to be eliminated, in order to ensure and maintain structural integrity under a wide range of operating conditions of finished component. Now a day, weld manufacturer’s practice for more spot welds than what actually needed for maintaining structural integrity. As the cost associated with over welding is significant provides a considerable driving force for optimizing the resistance spot welding process. Till date several attempts have been made by many researchers to model the spot welding process, investigating the influence of the spot welding parameters on resistance spot weld performance and identifying the optimal welding parameter. However, the resistance spot welding is non-linear process requires the application of deterministic as well as stochastic techniques. The stability of structure was ensured by quantity of spots, its placement and applied loads on welded structure. So, to solve the problem in the design of the multi-spot welded structure it is necessary to obtain the relations between the strength of multi-spot weld and design parameters. Therefore, the optimization of the resistance spot welding process will remain a key research area, matching the numerous welding parameters with the performance measures of resistance spot welds. This work will help to set the optimum welding parameters for multi- spot welded lap shear and cross tension specimens to achieve the maximum weld strength. An empirical formula recommended by the American Welding Society (AWS), the diameter of the spot weld nugget, d, is chosen and considered as follows:
1.2. Resistance spot welding parameters Theoretical study related to parameters of spot weld is carried out. Impact of process and design parameters on the tensile shear strength can be seen on developed cause effect diagram is shown in Fig. 1. 2. Design of experiment 2.1. Selection of welding parameters and levels In this investigation welding current, welding force, specimen thickness, number of spots, spot spacing are chosen as fundamental parameters with three levels of each as shown in Table 1. The output parameter foreseeing weld quality is tensile shear strength. 2.2. Design of experiment Analysis of manufacturing and production processes has been done by using design of experiments as per Taguchi method. The design of experiment (DOE) method can be used to improve the quality of weld. It helps to analyse the impact of process and design parameters on welding quality at various level. Taguchi analysis was done to find out optimal settings of the various parameters which have been selected. For this experimentation, we select L27 Taguchi orthogonal array (OA) for five welding parameters with three levels of each for Lap shear specimen. 3. Experimentation 3.1. Specimen preparation
d4t
ð1Þ The material used for test specimens was galvanized steel sheet of 1.22 mm thickness. The measured dimensions for lap shear specimen are 130 mm length (L) and 120 mm width (W) with an
1.1. Heat generation in resistance spot welding To ensure a good quality weld, the weld current, weld time and contact resistance, must be controlled properly. The total amount of heat generated is given by:
Table 1 Parameters and Level. Parameters
Q ¼ I2 RT
ð2Þ
Where, Q – Heat generated, J; I – Welding current, A; R – Resistance of the work piece, X and T – Time of current flow, sec.
Electrode Force (N) Number of Spots Spots Spacing (mm) Specimen Thickness (mm) Welding Current (KA)
Level
Notation
1
2
3
700 2 12 0.71 7
800 4 14 0.91 8
900 6 16 1.22 9
A B C D E
Fig. 1. Cause-effect diagram [12].
Please cite this article as: P. A. Dhawale and B. P. Ronge, Parametric optimization of resistance spot welding for multi spot welded lap shear specimen to predict weld strength, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.756
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overlap of 30 mm. Silicon carbide paper P220 grade was used to clean the sheet surfaces. The welds were made by utilizing an electric resistance spot welding machine, having 10 KVA as nominal welding power. The chemical composition for of test specimen is listed below in Table 2. Test specimen design and its FE model shown in Fig. 2a and b. 3.2. Experimental procedure A universal testing machine (UTM) at a cross-head speed of 1.31 mm/min to 2 mm/min is used for testing the weld specimens in order to find out weld strength. For selected ranges of parameters, partial pull out failure was observed for the test specimen under constant loading velocity. The Brinell microscope is utilized to measure the diameter of nugget, after testing of the specimen. Fixtures are designed and fabricated used to load test specimens in the UTM. The fixture is designed and manufactured such that it should transmit the load symmetrically to the joint of the test specimen. Fixture was made from cast iron and mild still to sustain the failure load of the test specimen and must perform the required number of test. Fixture accessories like studs, bolts and Table 2 Chemical Composition of Galvanized steel sheet. T.S
Y.S
nuts are capable of sustaining maximum load as shown in as shown in Fig. 3. Test specimens of maximum thickness 1.22 mm were tested successfully. Two studs of M14 and 10 in. long are welded at the center of jaw plate as shown in Fig. 3. These two studs are axially aligned in order to avoid eccentric pulling load over the specimen. Four nuts and bolts of size M10 are used to hold two jaw plates. Two blocks of C shape are drilled having 10 mm diameter and 12 mm tapping is done to provide internal threads. These C shaped blocks add extra force for holding sheet metal in jaw plates so that it will not slide over the surface of jaw plates. Lap shear specimens were tested successfully having maximum thickness 1.22 mm. Fig. 3 shows testing of lap shear test specimen. At dynamical loading condition for example; loading velocities are 1.32 m/min and 2 m/min, specimen testing has been carried out to measure tensile shear strength. Ten number of test specimens are tested. Sample load verses displacement curve is shown in Fig. 4. Loading velocity was constant up to 5 mm displacement. It was observed that load was increased linearly with increase in time. Spot welded test specimens have been made by keeping same welding conditions and tested at different loading velocities. The results of the dynamic strength tests are shown in Fig. 4. 4. Taguchi analysis
Alloying elements (wt%)
MPa 350
3
4.1. Signal to noise ratio 240
C 0.16
Mn 0.30
Si 0.25
S 0.030
P 0.03
Cr 0.004
Analyzing the data to select the optimum level based on least variation around on the average value, by using the S/N ratio. Fur-
Fig. 2. a) Work piece design for test specimen b) FEA model of specimen.
Fig. 3. Testing of multi spot welded lap shear specimen.
Please cite this article as: P. A. Dhawale and B. P. Ronge, Parametric optimization of resistance spot welding for multi spot welded lap shear specimen to predict weld strength, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.756
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Fig. 4. Load verses displacement curve.
thermore, compare two sets of experimental data with respect to deviation of the average from the target. To explore the main effects, the experimental results were analyzed. According to quality designing, the characteristics are classified as Higher the best (HB), lower the best (LB) and Nominal the best. HB includes Weld strength which desires higher values [15,16]. As the experimental design is orthogonal, the effect of each welding process parameter at different levels on the S/N ratio can be obtained separately. The S/N ratio was summarized for each level of the welding parameters and process output. By using Minitab software, the mean, S/N ratio for each level of the welding parameters is summarized and tabulated values are shown in Table 3. For selected parameters corresponding higher tensile shear strength, which parameter level is significant shown in Fig. 5. The optimal combination which gives higher tensile shear strength is A1B3C3D3E3. 4.2. Analysis of variance The analysis of variance (ANOVA) is used to find out the process parameter prominently affecting the weld quality [13,14]. The percent contribution in the total sum of the squared deviations can be utilized to assess the significance of the welding process parameter change on these quality attributes. Decomposition of variance is done to get the impact of the different welding parameters on the tensile strength (T-S). ANOVA is used to found out the design
parameters in order to show the parameters which are significantly influencing the output parameters. The sum of squares and variance are also calculated during the analysis. To choose the significant factors affecting the process, F-test value at 95% confidence level is used and percentage contribution is calculated. The variation of the process parameter shows an improvement on the performance indicated by the larger F-value. The ANOVA analysis for weld strength is shown in Table 4.
Table 4 Results of ANOVA for Tensile Shear Strength. Source
DF
A B C D E Error Total S = 514.822,
2 139,422 69,711 2 26,006,502 13,003,251 2 119 59 2 21,073,070 10,536,535 2 423,932 211,966 16 4,240,667 265,042 26 51,883,710 – R-sq = 91.83%, R-sq(adj) = 86.72%
Electrode Force (N)
Number of Spots
Spot Spacing (mm)
Thickness (mm)
Current (KA)
1 2 3 Delta Rank
6299 6208 6123 176 4
5009 6208 7413 2404 1
6208 6208 6213 4 5
5214 6054 7361 2147 2
6055 6213 6362 307 3
Fig. 5. Main effects plot for S/N ratio.
Adj MS
F-Value
P-Value
0.26 49.06 0 39.75 0.8 – –
0.772 0 1 0 0.467 – –
Table 5 Comparative results for Tensile shear strength. Sr. No.
Table 3 Response for Signal to Noise Ratios. Level
Adj SS
1 2 3 4
Tensile shear strength X1 By Regression Modelling
X2 Experimental Results
7307.3 6271.2 6052.1 5611.1
7301.78 6235.345 6047.87 5610.357
Percentage Deviation % [(X1-X2)/X1] 100
0.071438 0.571741 0.069893 0.013242
Fig. 6. Meshed geometry of three spot welded specimen.
Please cite this article as: P. A. Dhawale and B. P. Ronge, Parametric optimization of resistance spot welding for multi spot welded lap shear specimen to predict weld strength, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.756
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According to Table 4, Number of spots was observed as the significant parameter influencing the tensile shear strength, specimen thickness was observed to be the second-positioning component, while welding current, electrode force, and spot spacing have ranking sequentially. 5. Regression modeling Data obtained from experimentation has been analyzed by performing a regression analysis. Regression analysis helps to develop regression equation for tensile shear strength of lap shear specimen with the multi-spot. Randomly spot welded lap shear specimens are tested for tensile shear strength. The regression equation has been obtained, and it is given as follows.
Tensile Shear Strength ¼ 38600 þ 46:311 A þ 601:00B þ 4210:62 D þ 4908:1 E þ 0:000222 A A 5:9433 A E Fig. 7. Load verses time.
The obtained regression equation is validated by confirmation tests. Comparative results by the regression equation and experimentation in Table 5 show that there is a 0.57% maximum deviation. So, the obtained equation is right in agreement.
Table 6 Random Input Variable Specifications multi spot welded specimen. No.
Name
Type
Par1
Par2
1 2 3
DISP R T
UNIF UNIF UNIF
0.10000 1.0000 0.71000
0.90000 5.0000 1.5000
6. Simulation Lap shear three spot welded specimen is modeled for simulation. In order to perform simulation FEM package ANSYS is used. For meshing the geometry of the test specimen, Solid 186, MPC
Table 7 Optimization result for tensile shear strength. Particle
10
25
50
75
100
150
200
300
500
A B C D E Tensile Shear Strength (N)
741.6495 5.588664 12 1.22 8.972277 8852.806
700 6 16 1.22 9 9399.546
700 6 16 1.22 9 9399.546
700 6 16 1.22 9 9399.546
700 6 16 1.22 9 9399.546
700 6 16 1.22 9 9399.546
700 6 16 1.22 9 9399.546
700 6 16 1.22 9 9399.546
700 6 16 1.22 9 9399.546
Fig. 8. Tensile Shear Strength Vs Number of spots and distance between two spot.
Please cite this article as: P. A. Dhawale and B. P. Ronge, Parametric optimization of resistance spot welding for multi spot welded lap shear specimen to predict weld strength, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.756
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184, Target 170 and Contact 175 are selected as the elements. In order to define spot weld MPC184 element is used. Material properties are entered. Meshed model of specimen was prepared by mapped type mesh geometry by giving element size 5 mm which is shown below in Fig. 6. Same configuration of specimen has been modeled in FEM package ANSYS and loading has been done in different intervals of time. Time has been kept in between 1 s to 11 s. Fig. 7 shows relation between loading conditions and time of spot welded specimen During the simulation, input variables displacement; spot weld radius; thickness and time of analysis are varied shown in Table 6 Displacement of one end of specimen, spot weld radius and thickness is represented as DISP, R, T respectively. During simulation, response parameter is selected as maximum shear stress at spot welding element. Non-linear properties of material are entered. At one end of specimen all degrees of freedom (DF) are
Table 8 Results validation for tensile shear strength of multi spot welded specimen. Number of Spots
1 2 3 4 5 6
Tensile Shear Strength in N Experimentation
Regression Model
Simulation
2012.57 3415.78 4790.57 5905.79 6997.83 8043.97
2009.117 3413.189 4783.875 5901.357 6991.781 8035.345
2147.34 3490.67 4711.9 5878.84 6901.87 8011.76
made zero while displacement is subjected to other end. Suitable range of displacement is selected to avoid excessive distortion of the elements. Each progression is incremented by 1. Then, simulation loop is defined. It is executed multiple times within defined range by varying design parameters randomly.
Fig. 9. Tensile Shear Strength Vs Current and specimen thickness.
Fig. 10. Shear strength Vs Probability.
Please cite this article as: P. A. Dhawale and B. P. Ronge, Parametric optimization of resistance spot welding for multi spot welded lap shear specimen to predict weld strength, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.756
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P.A. Dhawale, B.P. Ronge / Materials Today: Proceedings xxx (xxxx) xxx Table 9 Results of the Confirmation Experiment. Factors
Initial combination
Operating Setting S/N Ratio T-S Strength (N)
A1B1C1D1E1 70.550 3369.1
Optimal Combination Prediction
Experimentation
A1B3C2D3E1 77.8409 7619.4
A1B3C2D3E1 77.736 7585.5
7. Optimization To improve the performance of the process, particle swarm optimization is an intelligence optimization algorithm is used in the field of computational intelligence. Through this algorithm, a potential solution to the optimization problem indicated by a value each particle which may be determined by the fitness function. In order to complete the dynamic change, the moving direction is decided by each particle’s velocity and separation following its and other particles’ movement. At that point improvement in the arrangement space is accomplished. For every cycle, by considering the fitness value, personal optimal solution, and the optimal global solution, the particles update their speed and position. In this paper, we utilize the PSO to optimize the penalty parameter, C. We set the iteration number to 500, and the size of the population is set to 25 in the PSO. Then, to avoid the problems of premature convergence, we introduce the mutation into the PSO algorithm and low iteration efficiency to ensure population diversity in later periods which can expand the search space which will helps to improve the ability to search the optimal solution. Particle swarm optimization for tensile shear strength is shown in Table 7. 8. Results and discussion From the experimental, regression and simulation results, effect of selected process and design parameters on the tensile shear strength of lap shear test specimen is obtained. Effect of each process parameter, by keeping other parameters as constant, on the tensile shear strength of multi spot welded lap shear specimen is obtained and discussed as below. Fig. 8 shows contour plot of Tensile shear Strength Vs Number of spots and distance between two spot. Specimens which are having number of spot 04 and 05 at the same time, specimens having distance between two spots 11.5 mm to 12.5 failed in the green colored region. It has tensile shear strength in the region 6400– 7200 N. Test specimens having 06 number of with distance between two spots 14.75 mm to 16 in the region 6400–7200 N. Fig. 9 shows contour plot of Shear Strength Vs Current and specimen thickness. Specimens which are spot welded at current 7 to 8.5 Kamp and at the same time, specimens having thickness 1 mm to 1.22 mm failed in the green colored region. It has shear strength in the region 6400–7200 N. Specimen having six number of spots and at the same time, specimens having distance between two spots 14.75 mm to 16 in the region 6400–7200 N. The curves in Fig. 10 show that the shear stress remains below 2000 N/mm2 with a 93% probability. It shows that, within the selected ranges of input parameters multi spot welded specimen will not fail. Table 8 shows results validation for tensile shear strength of multi spot welded specimen by experimentation and simulation etc. Table 9 shows the results of experimental confirmation and comparison of the predicted and actual tensile shear strength for the optimal welding parameters. The improvement in tensile shear strength from the initial welding parameters to the optimal welding parameters is 9399.5 for 1.2 mm galvanized steel sheets. In this
Improvement in S/N Ratio
Maximum T-S Strength (N)
7.186
9399.5
way, the tensile shear strength is significantly improved by using the particle swarm optimization method.
9. Conclusion Regression analysis is carried out to propose simple regression equation to obtain the tensile shear strength of multi-spot welded lap shear specimen. Obtained regression equation is approved by conducting confirmation tests. The confirmation tests showed that it is conceivable to improve the tensile shear strength significantly. From the result analysis it is found that 9399.55 N as maximum tensile shear strength is obtained at spot spacing 16 mm and 06 numbers of spot as an optimum values, for 1.22 mm galvanized steel sheets by particle swarm optimization. 1. There was maximum 0.571741% deviation between experimental results and results obtained by regression equation for the recommended regression model for tensile shear strength of multi-spot welded lap shear specimen. 2. There is 6.69% maximum deviation in between results obtained by regression and simulation. 3. Results of simulation show that, if the shear stress remains below 2000 N/mm2 the probability of failure welded specimens is about 93% for the ranges of selected welding parameters. 4. It is observed that, with increased number of spot, probability of failure decreases. 5. Tensile shear strength increases with increase in loading velocity from 1.32 m/min to 2 m/min.
References [1] V.X. Trana, J. Pana, T. Panb, Effects of processing time on strengths and failure modes of dissimilar spot friction welds between aluminum 5754-O and 7075– T6 sheets, J. Mater. Process. Technol. 209 (2009) 3724–3739. [2] Luo Yi, Liu Jinhe, Xu Huibin, Xiong Chengzhi, Liu Lin, Regression modeling and process analysis of resistance spot welding on galvanized steel sheet, Mater. Des. 30 (2009) 2547–2555. [3] A.G. Thakur, V.M. Nandedakar, Application of Taguchi method to determine resistance spot welding conditions of austenitic stainless steel AISI304, J. Sci. Res. 69 (2010) 680–683. [4] Xin Yang, Yong Xia, Qing Zhou, A simplified FE model for pull-out failure of spot welds, Eng. Fract. Mech. 77 (2010) 1224–1239. [5] A.M. Pereira, J.M. Ferreira, A. Loureiro, J.D.M. Costa, P.J. Bártolo, Effect of process parameters on the strength of resistance spot welds in 6082–T6 aluminium alloy, Mater. Des. 31 (2010) 2454–2463. [6] M. Vural, A. Akkus, B. Eryurek, Effect of welding nugget diameter on the fatigue strength of the resistance spot welded joints of different steel sheets, J. Mater. Process. Technol. 176 (2006) 127–132. [7] Niranjan Kumar Singh, Y. Vijayakumar, Application of Taguchi method for optimization of resistance spot welding of austenitic stainless steel AISI 301Linnovative, Syst. Des. Eng. 3 (10) (2012). [8] M. Spitza, M. Fleischanderl, R. Sierlingerb, M. Reischauerb, F. Perndorfer, G. Fafilek, Surface lubrication influence on electrode degradation during resistance spot welding of hot dip galvanized steel sheets, J. Mater. Process. Technol. 216 (2015) 339–347. [9] M.R. Arghavani, M. Movahedi, A.H. Kokabi, Role of zinc layer in resistance spot welding of aluminium to steel, J. Mater. Des. 102 (2016) 106–114. [10] Hong Tae Kang, Pingsha Dong, J.K. Hong, Fatigue analysis of spot welds using a mesh-insensitive structural stress approach, J. Fatigue 29 (2007) 1546–1553. [11] Xin Yang, Yong Xia, Qing Zhou, A simplified FE model for pull-out failure of spot welds, Eng. Fract. Mech. 77 (2010) 1224–1239.
Please cite this article as: P. A. Dhawale and B. P. Ronge, Parametric optimization of resistance spot welding for multi spot welded lap shear specimen to predict weld strength, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.756
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[12] M.L. Kulkarni, Prasad Kulkarni, Effect of process parameters on the strength of spot weld, Int. J. Appl. Eng. Res. 18 (2011) 2513–2516, ISSN: 0973-4562. [13] G. Taguchi, E.A. Elsayed, T.C. Hsiang, Quality Engineering in Production Systems, McGraw-Hill, New York, 1989. [14] P.J. Ross, Taguchi Techniques for Quality Engineering, McGraw-Hill, New York, 1988.
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Please cite this article as: P. A. Dhawale and B. P. Ronge, Parametric optimization of resistance spot welding for multi spot welded lap shear specimen to predict weld strength, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.756
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Parametric optimization of resistance spot welding for multi spot welded lap shear specimen to predict weld strength P.A. Dhawale ⇑, B.P. Ronge SVERI’s College of Engineering and Research, Pandharpur 413304, India
a r t i c l e
i n f o
Article history: Received 19 July 2019 Accepted 24 July 2019 Available online xxxx Keywords: Number of spots Distance between the spots Taguchi method Particle swarm optimization Tensile shear strength
a b s t r a c t This paper examining the impact of welding parameters, by parametric investigation of multi spot welded lap shear specimen, on the tensile shear strength of spot welded specimen. Further, study is carried out, to develop mathematical model by using regression analysis which shows relationship between selected welding parameters and weld strength. The design parameters considered are specimen thickness, number of spots and distance between the spots while process parameters are electrode force and weld current. As per Taguchi design of experiment approach, five welding parameters with three levels of each are considered for analysis. Experimentation is carried out as per L27 orthogonal array to investigate the effect of process and design parameters on tensile shear strength. Particle swarm optimization (PSO) method was used to determine welding parameter has a significant impact on weld strength. Tests for confirmation have been conducted to validate the results. Confirmation tests exhibit that deviation between the results obtained by experimentation and regression is up to 0.571741%. Particle swarm optimization explore that maximum tensile shear strength 9399.55 N is obtained at 06 numbers of spot and 16 mm distance between the spots as optimum value for 1.22 mm galvanized steel sheets. Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Conference on Manufacturing, Material Science and Engineering.
1. Introduction Resistance spot welding (RSW) is a conventional welding technique used to join thin metal sheets, due to the resistance to the flow of electric current which will generate the heat at the joint, where nugget is not visible outside. The joining of sheet metal is a combination of a mechanical, electrical, thermal and metallurgical phenomenon. It is different from other welding processes, as the filler material is not required. It has astounding technomonetary advantages, for example, ease, high creation rate and flexibility for mechanization which settles on it an appealing decision for vehicle -body gatherings, bus lodges, railways, and home appliances. The transport sector is having tremendous application of spot welded joint [1]. This process is regular for welding sheets of various metals like steels, titanium, aluminium alloys, and so on. It is a sophisticated amongst the most prepared of the electric welding forms being used by industry today. Interfacial mode of failure was observed after tensile shear test of spot welded galva-
⇑ Corresponding author Tel.: +91 8412053701. E-mail address: [email protected] (P.A. Dhawale).
nized sheets at the permissible level of parameters [2]. Impact of design and process parameters on the tensile strength of galvanized steel sheets are examined through investigations. This examination comprises of utilization of the Taguchi strategy to think about the impact of process and design parameters on the quality of welded joints [3]. The quality of spot weld characterizes structural stability of the automobile and improves unwavering quality of assembled sheets [4]. The author found that increase in shear strength of spot welds with increments in processing time [5]. The fatigue strength of the lap shear specimen is prepared and the importance of nugget diameter spot weld is evaluated [6]. Examine the improvement and impact of process parameters on the quality of AISI 301L stainless steel spot weld. ANOVA was used to control the level of significance of the welding parameters. [7]. Spot welding of greased up sheet brings about a more uniform and diminished alloyed material take-up that stores on the welding anode cap [8]. Examine the part of the zinc layer in RSW of aluminum to steel through correlation of microstructures and mechanical properties of the resistance spot welds amongst aluminum and low carbon steels [9,10]. In spite of the fact that this work author establishes a mathematical model by changing welding input parameters as indicted by the results from design of
https://doi.org/10.1016/j.matpr.2019.07.756 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Conference on Manufacturing, Material Science and Engineering.
Please cite this article as: P. A. Dhawale and B. P. Ronge, Parametric optimization of resistance spot welding for multi spot welded lap shear specimen to predict weld strength, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.756
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experiments and quality of the spot weld [11]. The Taguchi technique, a prominent experimental design method used in the industry, can mitigate the limitations of full factorial design and methodologies the optimization of parameter design, although the number of experiments is reduced [14]. Possibility of producing even one or two defective welds needs to be eliminated, in order to ensure and maintain structural integrity under a wide range of operating conditions of finished component. Now a day, weld manufacturer’s practice for more spot welds than what actually needed for maintaining structural integrity. As the cost associated with over welding is significant provides a considerable driving force for optimizing the resistance spot welding process. Till date several attempts have been made by many researchers to model the spot welding process, investigating the influence of the spot welding parameters on resistance spot weld performance and identifying the optimal welding parameter. However, the resistance spot welding is non-linear process requires the application of deterministic as well as stochastic techniques. The stability of structure was ensured by quantity of spots, its placement and applied loads on welded structure. So, to solve the problem in the design of the multi-spot welded structure it is necessary to obtain the relations between the strength of multi-spot weld and design parameters. Therefore, the optimization of the resistance spot welding process will remain a key research area, matching the numerous welding parameters with the performance measures of resistance spot welds. This work will help to set the optimum welding parameters for multi- spot welded lap shear and cross tension specimens to achieve the maximum weld strength. An empirical formula recommended by the American Welding Society (AWS), the diameter of the spot weld nugget, d, is chosen and considered as follows:
1.2. Resistance spot welding parameters Theoretical study related to parameters of spot weld is carried out. Impact of process and design parameters on the tensile shear strength can be seen on developed cause effect diagram is shown in Fig. 1. 2. Design of experiment 2.1. Selection of welding parameters and levels In this investigation welding current, welding force, specimen thickness, number of spots, spot spacing are chosen as fundamental parameters with three levels of each as shown in Table 1. The output parameter foreseeing weld quality is tensile shear strength. 2.2. Design of experiment Analysis of manufacturing and production processes has been done by using design of experiments as per Taguchi method. The design of experiment (DOE) method can be used to improve the quality of weld. It helps to analyse the impact of process and design parameters on welding quality at various level. Taguchi analysis was done to find out optimal settings of the various parameters which have been selected. For this experimentation, we select L27 Taguchi orthogonal array (OA) for five welding parameters with three levels of each for Lap shear specimen. 3. Experimentation 3.1. Specimen preparation
d4t
ð1Þ The material used for test specimens was galvanized steel sheet of 1.22 mm thickness. The measured dimensions for lap shear specimen are 130 mm length (L) and 120 mm width (W) with an
1.1. Heat generation in resistance spot welding To ensure a good quality weld, the weld current, weld time and contact resistance, must be controlled properly. The total amount of heat generated is given by:
Table 1 Parameters and Level. Parameters
Q ¼ I2 RT
ð2Þ
Where, Q – Heat generated, J; I – Welding current, A; R – Resistance of the work piece, X and T – Time of current flow, sec.
Electrode Force (N) Number of Spots Spots Spacing (mm) Specimen Thickness (mm) Welding Current (KA)
Level
Notation
1
2
3
700 2 12 0.71 7
800 4 14 0.91 8
900 6 16 1.22 9
A B C D E
Fig. 1. Cause-effect diagram [12].
Please cite this article as: P. A. Dhawale and B. P. Ronge, Parametric optimization of resistance spot welding for multi spot welded lap shear specimen to predict weld strength, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.756
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overlap of 30 mm. Silicon carbide paper P220 grade was used to clean the sheet surfaces. The welds were made by utilizing an electric resistance spot welding machine, having 10 KVA as nominal welding power. The chemical composition for of test specimen is listed below in Table 2. Test specimen design and its FE model shown in Fig. 2a and b. 3.2. Experimental procedure A universal testing machine (UTM) at a cross-head speed of 1.31 mm/min to 2 mm/min is used for testing the weld specimens in order to find out weld strength. For selected ranges of parameters, partial pull out failure was observed for the test specimen under constant loading velocity. The Brinell microscope is utilized to measure the diameter of nugget, after testing of the specimen. Fixtures are designed and fabricated used to load test specimens in the UTM. The fixture is designed and manufactured such that it should transmit the load symmetrically to the joint of the test specimen. Fixture was made from cast iron and mild still to sustain the failure load of the test specimen and must perform the required number of test. Fixture accessories like studs, bolts and Table 2 Chemical Composition of Galvanized steel sheet. T.S
Y.S
nuts are capable of sustaining maximum load as shown in as shown in Fig. 3. Test specimens of maximum thickness 1.22 mm were tested successfully. Two studs of M14 and 10 in. long are welded at the center of jaw plate as shown in Fig. 3. These two studs are axially aligned in order to avoid eccentric pulling load over the specimen. Four nuts and bolts of size M10 are used to hold two jaw plates. Two blocks of C shape are drilled having 10 mm diameter and 12 mm tapping is done to provide internal threads. These C shaped blocks add extra force for holding sheet metal in jaw plates so that it will not slide over the surface of jaw plates. Lap shear specimens were tested successfully having maximum thickness 1.22 mm. Fig. 3 shows testing of lap shear test specimen. At dynamical loading condition for example; loading velocities are 1.32 m/min and 2 m/min, specimen testing has been carried out to measure tensile shear strength. Ten number of test specimens are tested. Sample load verses displacement curve is shown in Fig. 4. Loading velocity was constant up to 5 mm displacement. It was observed that load was increased linearly with increase in time. Spot welded test specimens have been made by keeping same welding conditions and tested at different loading velocities. The results of the dynamic strength tests are shown in Fig. 4. 4. Taguchi analysis
Alloying elements (wt%)
MPa 350
3
4.1. Signal to noise ratio 240
C 0.16
Mn 0.30
Si 0.25
S 0.030
P 0.03
Cr 0.004
Analyzing the data to select the optimum level based on least variation around on the average value, by using the S/N ratio. Fur-
Fig. 2. a) Work piece design for test specimen b) FEA model of specimen.
Fig. 3. Testing of multi spot welded lap shear specimen.
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Fig. 4. Load verses displacement curve.
thermore, compare two sets of experimental data with respect to deviation of the average from the target. To explore the main effects, the experimental results were analyzed. According to quality designing, the characteristics are classified as Higher the best (HB), lower the best (LB) and Nominal the best. HB includes Weld strength which desires higher values [15,16]. As the experimental design is orthogonal, the effect of each welding process parameter at different levels on the S/N ratio can be obtained separately. The S/N ratio was summarized for each level of the welding parameters and process output. By using Minitab software, the mean, S/N ratio for each level of the welding parameters is summarized and tabulated values are shown in Table 3. For selected parameters corresponding higher tensile shear strength, which parameter level is significant shown in Fig. 5. The optimal combination which gives higher tensile shear strength is A1B3C3D3E3. 4.2. Analysis of variance The analysis of variance (ANOVA) is used to find out the process parameter prominently affecting the weld quality [13,14]. The percent contribution in the total sum of the squared deviations can be utilized to assess the significance of the welding process parameter change on these quality attributes. Decomposition of variance is done to get the impact of the different welding parameters on the tensile strength (T-S). ANOVA is used to found out the design
parameters in order to show the parameters which are significantly influencing the output parameters. The sum of squares and variance are also calculated during the analysis. To choose the significant factors affecting the process, F-test value at 95% confidence level is used and percentage contribution is calculated. The variation of the process parameter shows an improvement on the performance indicated by the larger F-value. The ANOVA analysis for weld strength is shown in Table 4.
Table 4 Results of ANOVA for Tensile Shear Strength. Source
DF
A B C D E Error Total S = 514.822,
2 139,422 69,711 2 26,006,502 13,003,251 2 119 59 2 21,073,070 10,536,535 2 423,932 211,966 16 4,240,667 265,042 26 51,883,710 – R-sq = 91.83%, R-sq(adj) = 86.72%
Electrode Force (N)
Number of Spots
Spot Spacing (mm)
Thickness (mm)
Current (KA)
1 2 3 Delta Rank
6299 6208 6123 176 4
5009 6208 7413 2404 1
6208 6208 6213 4 5
5214 6054 7361 2147 2
6055 6213 6362 307 3
Fig. 5. Main effects plot for S/N ratio.
Adj MS
F-Value
P-Value
0.26 49.06 0 39.75 0.8 – –
0.772 0 1 0 0.467 – –
Table 5 Comparative results for Tensile shear strength. Sr. No.
Table 3 Response for Signal to Noise Ratios. Level
Adj SS
1 2 3 4
Tensile shear strength X1 By Regression Modelling
X2 Experimental Results
7307.3 6271.2 6052.1 5611.1
7301.78 6235.345 6047.87 5610.357
Percentage Deviation % [(X1-X2)/X1] 100
0.071438 0.571741 0.069893 0.013242
Fig. 6. Meshed geometry of three spot welded specimen.
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According to Table 4, Number of spots was observed as the significant parameter influencing the tensile shear strength, specimen thickness was observed to be the second-positioning component, while welding current, electrode force, and spot spacing have ranking sequentially. 5. Regression modeling Data obtained from experimentation has been analyzed by performing a regression analysis. Regression analysis helps to develop regression equation for tensile shear strength of lap shear specimen with the multi-spot. Randomly spot welded lap shear specimens are tested for tensile shear strength. The regression equation has been obtained, and it is given as follows.
Tensile Shear Strength ¼ 38600 þ 46:311 A þ 601:00B þ 4210:62 D þ 4908:1 E þ 0:000222 A A 5:9433 A E Fig. 7. Load verses time.
The obtained regression equation is validated by confirmation tests. Comparative results by the regression equation and experimentation in Table 5 show that there is a 0.57% maximum deviation. So, the obtained equation is right in agreement.
Table 6 Random Input Variable Specifications multi spot welded specimen. No.
Name
Type
Par1
Par2
1 2 3
DISP R T
UNIF UNIF UNIF
0.10000 1.0000 0.71000
0.90000 5.0000 1.5000
6. Simulation Lap shear three spot welded specimen is modeled for simulation. In order to perform simulation FEM package ANSYS is used. For meshing the geometry of the test specimen, Solid 186, MPC
Table 7 Optimization result for tensile shear strength. Particle
10
25
50
75
100
150
200
300
500
A B C D E Tensile Shear Strength (N)
741.6495 5.588664 12 1.22 8.972277 8852.806
700 6 16 1.22 9 9399.546
700 6 16 1.22 9 9399.546
700 6 16 1.22 9 9399.546
700 6 16 1.22 9 9399.546
700 6 16 1.22 9 9399.546
700 6 16 1.22 9 9399.546
700 6 16 1.22 9 9399.546
700 6 16 1.22 9 9399.546
Fig. 8. Tensile Shear Strength Vs Number of spots and distance between two spot.
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184, Target 170 and Contact 175 are selected as the elements. In order to define spot weld MPC184 element is used. Material properties are entered. Meshed model of specimen was prepared by mapped type mesh geometry by giving element size 5 mm which is shown below in Fig. 6. Same configuration of specimen has been modeled in FEM package ANSYS and loading has been done in different intervals of time. Time has been kept in between 1 s to 11 s. Fig. 7 shows relation between loading conditions and time of spot welded specimen During the simulation, input variables displacement; spot weld radius; thickness and time of analysis are varied shown in Table 6 Displacement of one end of specimen, spot weld radius and thickness is represented as DISP, R, T respectively. During simulation, response parameter is selected as maximum shear stress at spot welding element. Non-linear properties of material are entered. At one end of specimen all degrees of freedom (DF) are
Table 8 Results validation for tensile shear strength of multi spot welded specimen. Number of Spots
1 2 3 4 5 6
Tensile Shear Strength in N Experimentation
Regression Model
Simulation
2012.57 3415.78 4790.57 5905.79 6997.83 8043.97
2009.117 3413.189 4783.875 5901.357 6991.781 8035.345
2147.34 3490.67 4711.9 5878.84 6901.87 8011.76
made zero while displacement is subjected to other end. Suitable range of displacement is selected to avoid excessive distortion of the elements. Each progression is incremented by 1. Then, simulation loop is defined. It is executed multiple times within defined range by varying design parameters randomly.
Fig. 9. Tensile Shear Strength Vs Current and specimen thickness.
Fig. 10. Shear strength Vs Probability.
Please cite this article as: P. A. Dhawale and B. P. Ronge, Parametric optimization of resistance spot welding for multi spot welded lap shear specimen to predict weld strength, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.756
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P.A. Dhawale, B.P. Ronge / Materials Today: Proceedings xxx (xxxx) xxx Table 9 Results of the Confirmation Experiment. Factors
Initial combination
Operating Setting S/N Ratio T-S Strength (N)
A1B1C1D1E1 70.550 3369.1
Optimal Combination Prediction
Experimentation
A1B3C2D3E1 77.8409 7619.4
A1B3C2D3E1 77.736 7585.5
7. Optimization To improve the performance of the process, particle swarm optimization is an intelligence optimization algorithm is used in the field of computational intelligence. Through this algorithm, a potential solution to the optimization problem indicated by a value each particle which may be determined by the fitness function. In order to complete the dynamic change, the moving direction is decided by each particle’s velocity and separation following its and other particles’ movement. At that point improvement in the arrangement space is accomplished. For every cycle, by considering the fitness value, personal optimal solution, and the optimal global solution, the particles update their speed and position. In this paper, we utilize the PSO to optimize the penalty parameter, C. We set the iteration number to 500, and the size of the population is set to 25 in the PSO. Then, to avoid the problems of premature convergence, we introduce the mutation into the PSO algorithm and low iteration efficiency to ensure population diversity in later periods which can expand the search space which will helps to improve the ability to search the optimal solution. Particle swarm optimization for tensile shear strength is shown in Table 7. 8. Results and discussion From the experimental, regression and simulation results, effect of selected process and design parameters on the tensile shear strength of lap shear test specimen is obtained. Effect of each process parameter, by keeping other parameters as constant, on the tensile shear strength of multi spot welded lap shear specimen is obtained and discussed as below. Fig. 8 shows contour plot of Tensile shear Strength Vs Number of spots and distance between two spot. Specimens which are having number of spot 04 and 05 at the same time, specimens having distance between two spots 11.5 mm to 12.5 failed in the green colored region. It has tensile shear strength in the region 6400– 7200 N. Test specimens having 06 number of with distance between two spots 14.75 mm to 16 in the region 6400–7200 N. Fig. 9 shows contour plot of Shear Strength Vs Current and specimen thickness. Specimens which are spot welded at current 7 to 8.5 Kamp and at the same time, specimens having thickness 1 mm to 1.22 mm failed in the green colored region. It has shear strength in the region 6400–7200 N. Specimen having six number of spots and at the same time, specimens having distance between two spots 14.75 mm to 16 in the region 6400–7200 N. The curves in Fig. 10 show that the shear stress remains below 2000 N/mm2 with a 93% probability. It shows that, within the selected ranges of input parameters multi spot welded specimen will not fail. Table 8 shows results validation for tensile shear strength of multi spot welded specimen by experimentation and simulation etc. Table 9 shows the results of experimental confirmation and comparison of the predicted and actual tensile shear strength for the optimal welding parameters. The improvement in tensile shear strength from the initial welding parameters to the optimal welding parameters is 9399.5 for 1.2 mm galvanized steel sheets. In this
Improvement in S/N Ratio
Maximum T-S Strength (N)
7.186
9399.5
way, the tensile shear strength is significantly improved by using the particle swarm optimization method.
9. Conclusion Regression analysis is carried out to propose simple regression equation to obtain the tensile shear strength of multi-spot welded lap shear specimen. Obtained regression equation is approved by conducting confirmation tests. The confirmation tests showed that it is conceivable to improve the tensile shear strength significantly. From the result analysis it is found that 9399.55 N as maximum tensile shear strength is obtained at spot spacing 16 mm and 06 numbers of spot as an optimum values, for 1.22 mm galvanized steel sheets by particle swarm optimization. 1. There was maximum 0.571741% deviation between experimental results and results obtained by regression equation for the recommended regression model for tensile shear strength of multi-spot welded lap shear specimen. 2. There is 6.69% maximum deviation in between results obtained by regression and simulation. 3. Results of simulation show that, if the shear stress remains below 2000 N/mm2 the probability of failure welded specimens is about 93% for the ranges of selected welding parameters. 4. It is observed that, with increased number of spot, probability of failure decreases. 5. Tensile shear strength increases with increase in loading velocity from 1.32 m/min to 2 m/min.
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Please cite this article as: P. A. Dhawale and B. P. Ronge, Parametric optimization of resistance spot welding for multi spot welded lap shear specimen to predict weld strength, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.756