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Heat flow prediction in friction stir welded aluminium alloy 1100
1.
International INTERNATIONAL Journal
of Mechanical JOURNAL Engineering OF and MECHANICAL Technology (IJMET), ISSN ENGINEERING 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 9, September (2014), pp. 336-346 © IAEME AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 5, Issue 9, September (2014), pp. 336-346 © IAEME:
2.
Journal Impact Factor
(2014): 7.5377 (Calculated by GISI) 336 IJMET © I A E M E HEAT FLOW PREDICTION IN FRICTION STIR WELDED ALUMINIUM ALLOY 1100 S. Deivanai1, Dr. Reeta Wattal2, Mrs. Sushila Rani2, Ms. Surabhi lata3 1Lecturer Mech.Engg .Deptt, Pusa Polytechnic, New Delhi, India 2 Professor, Mech. Engg. Deptt, Delhi Technological University, New Delhi, India 2Asst.Professor, Mech.Engg. Deptt. Delhi Technological University, New Delhi, India 3Asst.Professor, Mech. Engg. Deptt. Maharaja Agresan Institute of Engg Technology New Delhi, India ABSTRACT Aluminium alloy 1100 is used in manufacturing of Aircraft electrical conduits, Rivets, hose reel, sewage pumps, pressure regulator, level indicator ,control valves etc,. Friction stir welding (FSW) is an innovative solid state joining technique, employed for joining aluminium, magnesium, zinc and copper alloys. The FSW process parameters play a major role in deciding the weld quality. Effect of the tool rotational speed, weld speed, shoulder pin diameter, on the temperature at weld nugget and temperature at HAZ were found out. The mathematical model developed. The model checked for adequacy using ANOVA technique. Main and interaction effects of the process variables are presented in graphical form using MINITAB 17.0. The developed model used for prediction of Heat flow. Comparison of the performance of the experimental values, the mathematical Model and ANN model was done. ANN using feed forward algorithm used for modelling of the FSW process which provided satisfactory outputs. Heat flow calculations made and micro structural analysis done. Keywords: Full Factorial Design, Design of Experiment, Mathematical Model, ANN, Heat Flow, Microstructure. 1. INTRODUCTION Friction stir welding provides Good mechanical properties than fusion welding. Consumables, filler rod, gas shield is not required. The process is easily automated on simple milling machines. Hence lower setup costs and less training is required. Good weld appearance and minimal thickness under/over-matching, thus reducing the need for expensive machining after welding. It is called as Green process because Low energy input and Absence of toxic fumes, No spatter of molten material, makes FSW friendly to our environment. Tool Rotational Speed, Weld Speed, Tool
3.
International Journal of
Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 9, September (2014), pp. 336-346 © IAEME shoulder diameter, Tool pin diameter, Axial force, Tool tilt angle and Tool design are important process variables in Friction stir welding. The research work was planned to carry out in the following steps: 337 2. PLAN OF INVESTIGATION 1. Identification of important process control variables. 2. Deciding the working range of the process control variables, viz. Tool rotational speed, weld speed, tool pin diameter. 3. Developing the design matrix. 4. Conducting the experiments as per the design matrix. 5. Recording the responses viz. Temperature at weld nugget and Temperature at heat affected zone. 6. Development of mathematical model. 7. Checking the adequacy of the model. 8. Development of an ANN architecture to model and to predict the mechanical properties. 9. Comparison of performances of mathematical model, experimental value, ANN model. 10. Metallurgical analysis to study the micro structure of the welded material with the process variables. 11. Plotting of graphs and drawing conclusions. 12. Discussion of the results. 3. IDENTIFICATION OF IMPORTANT PROCESS CONTROL VARIABLES Based on literature review the important process parameters of friction stir welding are identified as tool rotational speed. Weld speed, axial force, tool shoulder diameter, tool pin diameter, tool pin shape etc. 4. DECIDING THE WORKING RANGE OF THE PROCESS CONTROL VARIABLES Trial runs are conducted to find the upper and lower limits of the process parameters by varying one of the parameter and keeping the rest of parameters at constant values. Feasible limits of the parameters are chosen in such a way that the joint should be free from visible defects. Upper limit of parameter is coded as HIGH and lower limit as LOW. The selected process parameters and their upper and lower limits together with notations and units are given in Table.1. Table.1: Process Control Parameters And Their Limits Sl.No parameters Units Notation -1 0 +1 1 Tool Rotational speed RPM N 460 690 1130 2 Weld speed Mm/min V 24 40 65 3 Tool pin diameter mm D 6 7 8
4.
International Journal of
Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 9, September (2014), pp. 336-346 © IAEME 338 5. DEVELOPING THE DESIGN MATRIX When three factors N, V and D, each at two levels, are of interest, the design is called a 23 factorial design and the eight treatment combinations can be as follows. Table .2: Design matrix Weld no. Trial no. Input parameters labels N V D 1 4 -1 -1 -1 (1) 2 7 +1 -1 -1 a 3 1 -1 +1 -1 b 4 6 +1 +1 -1 ab 5 2 -1 -1 +1 c 6 8 +1 -1 +1 ac 7 5 -1 +1 +1 bc 8 3 +1 +1 +1 abc There are seven degrees of freedom between the eight treatment combinations in the 23 design. Three degrees of freedom are associated with the main effects of N, V, and D. Four degrees of freedom are associated with interactions; one each with NV, ND, and VD and one with NVD. 6. CONDUCTING THE EXPERIMENTS AS PER THE DESIGN MATRIX The experiments were conducted at the central workshop of Delhi Technological University. A conventional vertical milling machine was used for FSW of aluminium alloy 1100. The feed speed and tool rotation (rpm) value of the milling machine play major roles in ensuring sound FSW joints. During the investigation, basic limitations of the milling machine also became apparent, for example rpm and feed speed setting were as per the machining operations. Furthermore, the load value is also unknown. The experience gained with the milling machine for FSW of aluminium is that it is the tool that is capable of demonstrating the FSW process for aluminium at a low feed speed. 6.1 The Base Metal For carrying out the research work, test specimens were prepared from 5 mm thickness Aluminium Alloy 1100 plate. Dimension of each plate was 100x50x5 mm. Composition of the base material is identified by spectro analysis using Hilger Polyvac 2000 Optical Emission Spectrometer. Optical emission spectrometry involves applying electrical energy in the form of spark generated between an electrode and a metal sample, whereby the vaporized atoms are brought to a high energy state within a so-called “discharge plasma”. These excited atoms and ions in the discharge plasma create a unique emission spectrum specific to each element. The chemical composition of AA1100 is tabulated as follows Table.3: chemical composition of AA 1100 Al Cu Mg Si Fe Mn Ni Zn Pb Sn Ti Cr 99.16 % 0.123% 0.036 0.185 0.322 0.023 0.025 0.052 0.023 0.020 0.010 0.005
5.
International Journal of
Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 9, September (2014), pp. 336-346 © IAEME 6.2 Tool Material Used EN 8: It is a very popular grade of through-hardening medium carbon steel, which is readily machinable in any condition. EN8 is suitable for the manufacture of parts such as general-purpose axles and shafts, gears, bolts and studs. 339 Eight sets of plates were welded as per the design matrix by selecting trials at random and welding was carried out. Figure.2 shows few samples in welded condition. Figure.1: welded samples 7. RECORDING THE RESPONSE The Temperature at work piece during Friction stir welding is measured using Laboratory mercury thermometer at weld centre and at heat affected zone. Table.4: Temperature and heat flow Run no. Tool speed (RPM) Weld speed (mm/min) Toolpin Diameter (mm) Temp At nugget (0C) Heat flow [Q] in w/cm/0c In weld nugget Temp At HAZ (0C) Heat flow [Q] in w/cm/0c In HAZ of weld 1 460 24 6 55 297.48 43 178.48 2 1130 24 6 70 446.22 55 297.48 3 460 65 6 85 594.96 61 356.97 4 1130 65 6 68 426.39 60 347.06 5 460 24 8 50 247.90 42 168.57 6 1130 24 8 55 297.48 40 148.74 7 460 65 8 65 396.64 58 327.23 8 1130 65 8 108 823.03 92 664.38 7.1 Heat flow calculations The majority of the heat generated from the friction, i.e., about 95%, is transferred into the work piece and only 5% flows into the tool. Heat flow during welding, strongly affect phase transformations during welding and thus the resultant microstructure and properties of the weld. It is also responsible for weld residual stresses and distortion. The analytical solution derived by
6.
International Journal of
Mechanical Engineering and Tec ISSN 0976 – 6359(Online), Volume 5, Issue hnology – 6340(Print), Rosenthal.D for three-dimensional heat flow in a semi infinite work piece during welding was used for calculation of heat input. 0 C; - initial temperature in 0 C ; k- thermal conductivity of Where T – measured temperature in aluminium alloy in J/ms K = 229 for aluminium ;R + y^2 + z^2)^1/2 ; x – desired distance from - workpiece thermal diffusivity = 8.5 X 10 8. DEVELOPMENT OF MATHEMATICAL MODEL The Response function can be expressed as: Y = ƒ (N, Where, Y = response; N = tool rotational speed; A linear regression model with three predictor variables can be expressed with the following equation: Y = b0+b1N+b2 V+b3D+b12N Where b0 is constant and b1, b2, b3, b b0, the Y-intercept, can be interpreted as the value to predict for Y if N= Since N is a continuous variable, each one-unit difference in N, if other independent variables remains constant. This means that if N is differed by one unit, and V and D did not differ, Y will differ by 8.1 Evaluation of the co-efficient of model The values of the co efficient of the response function were calculated using regression analysis. The calculations were carried out using MINITAB17 Table.5: Evaluation Sl.no Co-efficient 1 b0 2 b1 3 b2 4 b3 5 b12 6 b13 7 b23 8 b123 Technology (IJMET), ISSN 0976 9, September (2014), pp. 336-346 © IAEME 340 - radial distance from the origin, namely, (x^2 origin ; Q – heat input (W)V – travel speed (mm/s); -5 m2 / s for aluminium V, D) V = weld speed; D= tool pin diameter NV+b13ND+b23 VD+b123NVD , b12, b13, b23, b123 are co- efficients of model V=D = 0 b1 represents the difference in the predicted value of Y for b1 units, on average. and values listed in Table 5 of the co-efficient of model Temperature(0C) At weld At HAZ 0 -102.1 -69.43 1 + 0.2786 + 0.2204 2 + 5.882 + 3.265 3 + 19.39 + 14.74 12 - 0.008810 - 0.005825 13 - 0.03804 - 0.03185 23 - 0.7690 - 0.4347 123 + 0.001274 + 0.000892 5.
7.
International Journal of
Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 9, September (2014), pp. 336-346 © IAEME Temperature at weld nugget (TN) = b0+b1N+b2V+b3D+b12NV+b13ND+b23VD+b123NVD TN = -102.1 + 0.2786 N + 5.882V + 19.39 D – 0.008810 NV – 0.03804 ND – 0.7690 VD + 0.001274 NVD Temperature at HAZ (T HAZ) = b0+b1N+b2V+b3D+b12NV+b13ND+b23VD+b123NVD THAZ = -69.43 + 0.2204 N + 3.265 V + 14.74 D – 0.005825 NV – 0.03185 ND – 0.4347 VD + 0.000892 NVD 9. CHECKING ADEQUACY OF THE MODEL 341 8.2 Regression Equations The analysis of variance (ANOVA) was used to check the adequacy of the developed models. As per this technique: a. The F-ratio of the developed model is calculated and is compared with the standard tabulated value of F- ratio for a specific level of confidence. b. If calculated value of F- ratio does not exceed the tabulated value, then with the corresponding confidence probability the model may be considered adequate. For analysis , a confidence interval of 95% was taken Table.6: ANOVA or Temperature at Weld Nugget source Sum of squares DF Mean square F value P value model 2546 7 363.7143 5.980913 0.04439346 N 264.5 1 264.5 4.349435 0.28463858 W 1152 1 1152 18.94347 0.14377340 D 0 1 0 0 0 NW 4.5 1 4.5 0.073997 0.83092317 ND 312.5 1 312.5 5.138726 0.26448893 WD 200 1 200 3.288798 0.32081320 NWD 612.5 1 612.5 10.07194 0.19432760 Pure error 486.5 8 60.8125 Total 3032.5
8.
International Journal of
Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 9, September (2014), pp. 336-346 © IAEME 342 Table.7: ANOVA for Temp at HAZ 10. PREDICTION OF RESPONSES BY REGRESSION MODEL Table.8: Predicted values source Sum of squares DF Mean square F value P value model 1961.875 7 280.2679 1.430623 0.44330748 N 231.125 1 231.125 1.179774 0.47371849 W 1035.125 1 1035.125 5.283779 0.26123236 D 21.125 1 21.125 0.1078322 0.79801249 NW 66.125 1 66.125 0.337534 0.66493950 ND 55.125 1 55.125 0.2813847 0.68951297 WD 253.125 1 253.125 1.297245 0.45869807 NWD 300.125 1 300.125 1.531983 0.43261963 Pure error 1567.25 8 195.9062 Total 3529.125 Weld no Tool speed rpm Weld speed Mm/min Pin dia mm Temp TN Temp THAZ 0C 1 460 24 6 51.75 38.00 2 1130 24 6 63.25 48.75 3 460 65 6 75.75 60.75 4 1130 65 6 87.25 71.50 5 460 24 8 51.75 41.25 6 1130 24 8 63.25 52.00 7 460 65 8 75.75 64.00 8 1130 65 8 87.25 74.75
9.
International Journal of
Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 9, September (2014), pp. 336-346 © IAEME 11. MODELLING USING ARTIFICIAL NEURAL NETWORK Figure 2: FFNN to predict temperature A typical feed forward network is shown in fig 2. This network is used in the present research for the modelling of the process. To model the process 3 input parameters viz , tool rotational speed , weld speed , tool pin diameter were considered and their effect on temperature developed during friction stir welding were studied , so for our modelling the input and the output layer of net work had 3,and 7 neurons respectively. The goal was kept as 0.01. Figure 3: Performance plot Figure 4: Fit plot 343 Tool rotational speed Weld speed Tool pin diameter Temp at nugget Temp at HAZ
10.
International Journal of
Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 9, September (2014), pp. 336-346 © IAEME 12. COMPARISON OF ANN, MATH. MODEL AND EXPERIMENT VALUES 344 Table 9: Comparison of predicted temperature values Weld Run ANN %ERROR MATH. MODEL %ERROR EXPERIMENT VALUE Temp TN 7 69.43 +6.82% 75.75 +16.53% 65 8 99.12 -8.22% 87.25 -19.21% 108 Temp THAZ 0C 7 61.00 +5.17% 64.00 +10.34% 58 8 85.61 -6.94% 74.75 -18.75% 92 13. MICROSTRUCTURAL ANALYSIS OF AA1100 Microscopic Examination using Radical 40X-800X Metallurgical Microscope With 5mp Camera was carried out before and after welding. ASTM E- 3 standard testing procedure followed. Etchant 25% Nitric Acid used for surface preparation. The image obtained at Magnification - 250 X was given below Figure 5: Microstructure of Base metal Figure 6: Microstructure of weld nugget
11.
International Journal of
Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 9, September (2014), pp. 336-346 © IAEME 345 14. RESULTS AND DISCUSSIONS Direct effect of process variables C1 460 1130 85 80 75 70 65 60 Main Effects Plot for C7 Data Means C2 C3 24 65 6 8 Me an C1 460 1130 70 65 60 55 50 45 Main Effects Plot for C8 Data Means C2 C3 24 65 6 8 M e a n Figure 7: Effect of TRS, WS D on Figure 8: Effect of TRS, WS D on Temp at weld Temp at HAZ Interaction Plot for C8 Data Means 24 65 6 8 80 60 40 80 60 40 C1 C2 C3 C1 460 1130 C2 24 65 Interaction Plot for C7 Data Means 24 65 6 8 90 75 60 90 75 60 C1 C2 C3 C1 460 1130 C2 24 65 Figure 9: Interaction effect at HAZ Figure 10: Interaction effect at weld 15. CONCLUSIONS 1. The tool rotational speed has positive effect (11.5%) on temperature at weld nugget. The weld speed has 24% positive effect on temperature. When weld speed is increased temperature at weld nugget is also increased. Tool pin diameter has no effect on temperature at weld nugget. Interaction effect of tool speed and weld speed is less (1.5%). Interaction effect of tool speed and pin diameter has 12.5 % positive effect on temperature. Interaction of weld speed and pin diameter has 10% positive effect on temperature. Interaction of tool speed, weld speed and pin diameter increases weld nugget temperature. 2. When tool rotational speed and weld speed increases , the temperature at heat affected zone is increased. When these parameters decreased the temperature also decreased. Interaction values also prove that input parameters were directly proportional to temperature at heat affected zone.
12.
International Journal of
Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 9, September (2014), pp. 336-346 © IAEME 346 3. Increase in tool rotation speed causes more heat input which, in turn, enlarges the TMAZ and HAZ. Increasing the weld speed reduces the heat input resulting in smaller TMAZ and HAZ which leads to greater tensile strength. REFERENCE [1] The welding of aluminium by N.R. Mandal [2] The Design and Analysis of Experiments by Monte Gomery [3] Investigation on Interaction Effects of Tool Geometry and Welding Speed on Tensile Strength of Friction Stir Welded AA1080 Joints 1Vinod Kumar, 2Sunil Kumar IJRMET Vol. 3, Issue 2, May - Oct 2013 [4] Modeling the Effects of Tool Probe Geometries and Process Parameters on Friction Stirred Aluminium Welds H. K. Mohanty1, D.Venkateswarlu1, M. M. Mahapatra1,*, Pradeep Kumar1, N. R. Mandal2 Journal of Mechanical Engineering and Automation 2012, 2(4): 74- 79 [5] http://www.globalspec.com/industrial-directory/1100_h14_aluminum [6] Microstructure and tensile strength of friction stir welded joints between interstitial free steel and commercially pure aluminiumS. Kundua,c,, D. Roy b, R. Bholac, D. Bhattacharjee b B. Mishrac, S Chatterjeea. [7] Heat Transfer in Friction Stir Welding—Experimental and Numerical Studies YuJ.Chao Mem. ASME X. Qi W. Tang. [8] Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints A. K. Lakshminarayanan, V. Balasubramanian, Centre for Materials Joining Research (CEMAJOR), Department of Manufacturing Engineering, Annamalai University, Annamalai Nagar-608 002, Tamil Nadu, India. [9] Artificial neural network application to thefriction stir welding of modelling plates Hasan Okuyucu a, Adem Kurt a,*, Erol Arcaklioglu b. [10] D.Muruganandam and Dr.Sushil lal Das, “Friction Stir Welding Process Parameters For Joining Dissimilar Aluminum Alloys”, International Journal of Mechanical Engineering Technology (IJMET), Volume 2, Issue 2, 2011, pp. 25 - 38, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. [11] D. Kanakaraja, P. Hema and K. Ravindranath, “Comparative Study on Different Pin Geometries of Tool Profile in Friction Stir Welding using Artificial Neural Networks”, International Journal of Mechanical Engineering Technology (IJMET), Volume 4, Issue 2, 2013, pp. 245 - 253, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. [12] Dalip Kumar, Antariksha Verma, Sankalp Kulshrestha and Prithvi Singh, “Microstructure and Mechanical Properties of Mild Steel-Copper Joined by Friction Welding”, International Journal of Mechanical Engineering Technology (IJMET), Volume 4, Issue 5, 2013, pp. 295 - 300, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. [13] C.Devanathan, A.Murugan and A.Suresh Babu, “Optimization of Process Parameters in Friction Stir Welding of AL 6063”, International Journal of Design and Manufacturing Technology (IJDMT), Volume 4, Issue 2, 2013, pp. 42 - 48, ISSN Print: 0976 – 6995, ISSN Online: 0976 – 7002.
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