Experimental and numerical study for the automatic gma welding process

ACKNOWLEDGMENTS I would like to thank those who helped me during the course of my study at the Mokpo National University, South Korea First, I would like to thank specifically my supervisor, Professor Kim Ill Soo for his scholarships, guidance, critical feedback, and valuable advice during this study I would like also to express my special indebtedness to Professor Park Chang Eun, Professor Yoon Han Yong, Dr Jang Kyeung Cheun and Dr Lee Dong Gill who provided expertise that greatly enhance this thesis I would also like to express a very special thanks to the members in my research group at the Welding and Automatic Control Laboratory, especially Mr Kim Hak Hyoung, Mr Seo Joo Hwan, Miss Shim Ji Yeon and Mr Jeong Jae Won for all their help on the experiment and valuable discussions in the laboratory Finally, on this occasion allow me to express my gratitude and appreciation to my elder sister, Doan Thi Quynh Nga, my wife Pham Thi Giang Lam, and my bosom friend Hua Huu Tho for all their support and understanding during the course of this study i Experimental and Numerical Study for the Automatic GMA Welding Process Doan The Thao Department of Mechanical Engineering Graduate School of Mokpo National University (Supervised by Professor Kim Ill Soo) Generally, GMA (Gas Metal Arc) welding process is currently one of the most popular welding methods To improve the welded quality and reliability as well as to increase productivity, the selection of the optimal process parameters for the robotic GMA welding process has been required as setting welding condition to archive the desired bead geometry For this reason, it is need to know the interrelationship between the process parameters and bead geometry as welding quality On the other hand, the welding deformations in all stages of a welding process for achieving the required precision of welded structure must be taken into account and as a result, it is required to predict the welding distortions at the early stages of welded structure design by using a simulation Therefore, a FEM (Finite ii Element Method) model with a suitable moving heat source, which can accurately simulate the thermal histories of GMA welding process, needs to be developed Furthermore, the determination of trajectory planning and continuous motion of welding torch without colliding with any obstacles for robotic welding system of complex structures should be considered It is better way to build a robotic welding system using the simulation tools in order to identify some of design and planning problems at an early stage The sequent experiments based on full factorial design have been conducted with two levels of five process parameters to obtain bead geometry of butt and lab joint type using a GMA welding process Four empirical models have been developed: linear, curvilinear, interaction and a proposed intelligent model Regression analysis was employed for optimization of the coefficients of linear, curvilinear, and interaction model, while GA (Genetic Algorithm) was utilized to estimate the coefficients of intelligent model Not only the fitting of these models were checked and compared by using variance test, but also the prediction on bead geometry using the developed models were carried out based on the additional experiments The thermal analysis models for automatic finding parameters of a moving heat source have also been proposed An algorithm for the combining between GA and the FEM was obtained and verified based on Goldak’s work and additional experiments for multi-pass butt and fillet welded joint with dissimilar thickness In this algorithm, GA was effectively employed to estimate the parameters of Goldak’s double ellipsoidal model iii Furthermore, not only the initial kinematic simulation model with six degrees of freedom for Faraman AM1 welding robot has been developed using CATIA V5 software, but also the forward and inverse kinematic equations also be obtained by using conventional numerical methods The output results computed from simulation model were employed to compare with calculated results from kinematic equations to validate the simulation one iv TABLE OF CONTENTS Contents Page ACKNOWLEDGEMENTS i ABSTRACT ii TABLE OF CONTENTS v LIST OF FIGURES ix LIST OF TABLES xiv NOMENCLATURE xv ACRONYMS .xix PUBLICATIONS xxi CHAPTER 1: INTRODUCTION 1.1 MOTIVATION 1.2 SCOPE OF THE RESEARCH WORK 1.3 THESIS ORGANIZATION CHAPTER 2: LITERATURE SURVEY 2.1 INTRODUCTION 2.2 FULL FACTORIAL DESIGN AND EMPIRICAL MODELS 10 2.2.1 Full factorial design 10 2.2.2 Linear model 11 2.2.3 Curvilinear model 12 2.2.4 Interaction model 12 2.3 DESIGN OF EXPERIMENTS AND OPTIMIZATION PROCEDURE 13 v 2.4 GOVERNING EQUATION FOR TRANSIENT HEAT TRANSFER ANALYSIS 18 2.5 MOVING HEAT SOURCE MODELS 19 2.5.1 Models for moving heat source 19 2.5.2 Heat source and FEM models 23 2.6 CATIA SOFTWARE AND APPLICATIONS 28 2.7 ROBOTICS WELDING SYSTEMS 31 CHAPTER 3: PREDICTION ON TOP-BEAD WIDTH FOR THE BUTT WELD USING A GENETIC ALGORITHM 35 3.1 INTRODUCTION 35 3.2 EXPERIMENTAL WORK 36 3.3 DEVELOPMENT OF EMPIRICAL MODELS 39 3.3.1 Selection of mathematical model 39 3.3.2 Developed the mathematical model 40 3.3.2.1 Genetic algorithm 40 3.3.2.2 Development of empirical models 43 3.4 RESULTS AND DISCUSSION 47 3.4.1 The verification of the developed models 47 3.4.2 The effects of process parameters 50 3.5 CONCLUSSION 61 CHAPTER 4: PREDICTING BEAD GEOMETRY FOR LAB JOINT USING AN INTERACTION MODEL 62 4.1 INTRODUCTION 62 4.2 EXPERIMENTAL WORK 63 vi 4.3 DEVELOPMENT OF EMPIRICAL MODELS 64 4.3.1 Bead width 65 4.3.2 Bead height 68 4.4 RESULTS AND DISCUSSION 70 4.4.1 ANOVA analysis for developed models 70 4.4.2 The accurate prediction of models 71 4.4.3 Effects of main process parameters 74 4.4.4 Effects of interaction for process parameters 76 4.5 CONCLUSSION 79 CHAPTER 5: DEVELOPMENT OF NUMERICAL MODEL 80 5.1 INTRODUCTION 80 5.2 DEVELOPMENT OF FEM MODEL USING A GA 80 5.3 VERIFICATION OF THE DEVELOPED GA-FEM MODEL 85 5.3.1 Verification of batch mode model 85 5.3.2 Verification of the GA-FEM model 88 5.4 WELDING CONDITIONS AND DIMENSIONAL DETAILS OF ADDITIONAL EXPERIMENTS 94 5.5 DEVELOPMENT OF FEM MODEL 97 5.5.1 Assumptions 97 5.5.2 Governing equation 98 5.5.3 Thermal physical properties of material 98 5.5.4 Mesh generation 99 5.6 RESULTS AND DISCUSSION .102 5.7 CONCLUSSION 113 vii CHAPTER 6: SIMULATION MODEL FOR ROBOTIC WELDING 114 6.1 INTRODUCTION 114 6.2 CATIA SOFTWARE 115 6.3 KINEMATICS 116 6.3.1 D-H (Denavit-Hartenberg) convention 116 6.3.2 Forward kinematics .120 6.3.3 Inverse kinematics 125 6.3.3.1 Solving for θ1 .125 6.3.3.2 Solving for θ3 .128 6.3.3.3 Solving for θ2 .129 6.3.3.4 Solving for θ4 .131 6.3.3.5 Solving for θ5 .132 6.3.3.6 Solving for θ6 .133 6.4 DEVOLOPEMENT OF SIMULATION MODEL 135 6.5 RESULTS AND DISCUSSION .139 6.6 CONCLUSION .142 CHAPTER 7: CONCLUSIONS .143 7.1 CONCLUSIONS 143 7.2 SUGGESTION FOR FUTURE WORK 145 REFERENCES .147 viii LIST OF FIGURES Figure 2.1 Page Coordinate system used for the FEM analysis of disc model according to Krutz and Segerlind [36] 21 2.2 Goldak’s double ellipsoidal heat source model [37] 22 2.3 Structure and interfaces of the system according to Hackel [89] 34 3.1 Input and output parameters of the GMA welding process 36 3.2 Configuration of butt welding specimen 38 3.3 Butt welding specimen 38 3.4 Measurement of top-bead width 39 3.5 Flow chart for the GA [25] 42 3.6 Comparison on the fitting of three developed models 48 3.7 The accurate prediction of three developed models 49 3.8 The effect of welding speed on top-bead width 52 3.9 The effect of welding voltage on top-bead width 52 3.10 The effect of arc current on top-bead width 53 3.11 The effect of tip gap on top-bead width 53 3.12 The effect of gas flow rate on top-bead width 54 3.13 Interaction effect of welding voltage and welding speed on topbead width 55 3.14 Interaction effect of gas flow rate and welding speed on top-bead width 55 3.15 Interaction effect of welding speed and tip gap on top-bead width 56 ix 3.16 Interaction effect of welding speed and arc current on top-bead width 56 3.17 Interaction effect of tip gap and gas flow rate on top-bead width 57 3.18 Interaction effect of gas flow rate and welding voltage on topbead width 57 3.19 Interaction effect of tip gap and welding voltage on top-bead width 58 3.20 Interaction effect of gas flow rate and arc current on top-bead width 58 3.21 Interaction effect of tip gap and arc current on top-bead width 59 3.22 Interaction effect of arc current and welding voltage on top-bead width 59 4.1 Schematic diagram for measurement of bead geometry 64 4.2 Comparison of measured and calculated results for bead width 71 4.3 Comparison of measured and calculated results for bead height 71 4.4 Predictable accuracy of the developed models for bead width 73 4.5 Predictable accuracy of the developed models for bead height 74 4.6 Effect of welding voltage on bead geometry 75 4.7 Effect of arc current on bead geometry 75 4.8 Effect of welding speed on bead geometry 76 x be run in parallel Therefore, parallel computing for the 3-D welding analysis models should be considered In a flexible automated welding system for manufacturing some of the complex products, the simulation of manufacturing process and off-line programming is an essential approach to reduce the cost and archive better quality Generally, an automatic welding system includes some of the components such as the robot and the controller, power source, positioned, sensors, track (gantry or column) and peripheral equipment In order to achieve the expected quality, the weld pool shape during welding should be sensing and control for real time For this reason, some high speed cameras and developing algorithms that can quickly detect the weld pool shape need to be considered when developing an automatic welding system 146 REFERENCES [1] Kim, D., Kang, M and Rhee, S (2005) Determination of optimal welding conditions with a 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