Modeling and Optimization in Manufacturing

Catalin I. Pruncu, PhD, is Research Associate in the Department of Mechanical Engineering at Imperial College in London, United Kingdom. He received his doctorate in Design Mechanics and Biomechanics from Politecnico di Bari in Italy in 2013.Jun Jiang, PhD, is a Lecturer of Mechanics of Materials Division in the Department of Mechanical Engineering at Imperial College London, UK. He received his DPhil from Oxford University in 2013 and joined Imperial College as postdoctoral researcher. Dr. Jiang’s research focuses on developing novel manufacturing techniques through the understanding of micro-thermomechanical behaviors for lightweight alloys and solar cells.

• Preface xiiiHistory of Traditional/Advanced Manufacturing 1Esther T. Akinlabi, Michael C. Agarana, and Stephen A. Akinlabi1 Introduction 12 Progress in Manufacturing 33 Overview of Advanced Manufacturing 54 Environmental Impact and Significance 65 Economic Importance of Advanced Manufacturing 76 Sustainability of Advanced Manufacturing 97 Trend of Advanced Manufacturing (AM) 108 Summary 11References 121 Modeling and Optimization in Manufacturing by Hydroforming and Stamping 13Hakim Naceur and Waseem Arif1.1 Introduction 131.2 Recent Advances in Stamping and Hydroforming Simulation 141.2.1 Fast Nonlinear Procedures in Stamping and Hydroforming 161.2.1.1 Geometrical Mapping Algorithm 171.2.1.2 Radial Length Development Algorithm 171.2.1.3 Orthogonal Length Unfolding Algorithm 171.2.2 Multistage Inverse Method for Stamping and Hydroforming 201.2.2.1 Generation of Intermediate Configurations 211.2.2.2 Integration of Stress States 221.2.2.3 Procedure for Mapping Fields between Two Configurations 221.2.2.4 Application to the Demeri Cylindrical Cup 241.2.3 Improved Inverse Method for Stamping and Hydroforming 271.2.3.1 Basic Idea 271.2.3.2 Deformation Path Prediction 271.2.3.3 Consideration of Bending Moments 271.2.3.4 Bending and Unbending Problem 291.2.3.5 Application to the Square Box of Numisheet93 301.3 Optimization of Stamping and Hydroforming Parameters 311.3.1 Mathematical Optimization Problem 331.3.2 Shape Optimization of the Initial Blank 331.3.3 Optimization of Addendum Surfaces of Stamped Parts 341.3.4 Optimization of Drawbead Restraining Forces 361.3.5 Optimization of Tool Geometry 381.3.6 Optimization of Material Parameters 391.3.7 Optimization of Hydroforming Process Parameters 411.4 Future Outlooks 431.5 Conclusions 44References 452 Numerical Simulation Techniques in Casting Process 49Qingyan Xu and Cong Yang2.1 Introduction 492.2 Numerical Models 502.2.1 Heat Transfer Model 502.2.1.1 Heat Conduction 502.2.1.2 Heat Convection 512.2.1.3 Heat Radiation 512.2.1.4 Heat Conduction Partial Differential Equation 522.2.1.5 Finite Difference Method for Solving Heat Transfer Problem 522.2.2 Fluid Flow Model 532.2.2.1 Continuity Equation 542.2.2.2 Navier–Stokes Equation 542.2.2.3 Numerical Algorithms 542.2.2.4 Free Surface Track 552.2.3 Stress Simulation Model 562.2.3.1 Thermal Elastoplastic Model 572.2.3.2 Numerical Solution 572.2.4 Microstructure Simulation Model 582.2.4.1 The Nucleation Model 582.2.4.2 The Cellular Automaton (CA) Method 592.2.4.3 The Phase-Field Method 592.2.5 Initial and Boundary Conditions 612.2.5.1 Initial Conditions 612.2.5.2 Boundary Conditions 612.3 Modeling Casting Process and Optimization 622.3.1 Mold Filling Simulation 622.3.1.1 Cylinder Head Cover Filling Simulation 622.3.1.2 Aircraft Cabin Door Casting Simulation 632.3.2 Solidification Simulation 642.3.2.1 Comparison Study of DS Solidification Simulation 672.3.2.2 Processing Parameter Optimization Using Solidification Simulation 692.3.3 Stress Simulation 702.3.3.1 Hollow Bar Stress Simulation 712.3.3.2 Turbine Blade Stress Simulation 712.3.4 Casting Microstructure Simulation 742.3.4.1 Casting Grain Structure Simulation 742.3.4.2 Dendrite Microstructure Simulation 762.4 Conclusion 79Acknowledgments 79References 803 Modeling and Optimization Process in Milling 83Bogdan A. Chirita3.1 Milling 833.1.1 Introduction 833.1.2 Lightweight Alloys Machining 853.2 Modeling and Simulation of Milling 863.2.1 Response Surface Methodology 873.2.1.1 Application of RSM to Machining 883.2.1.2 Response Surface Methodology Applied to Investigate the Cutting Force in Face Milling of AZ61A Magnesium Alloy Parts 893.2.2 Fuzzy Logic 983.2.2.1 Application of Fuzzy Method in Machining 1013.2.2.2 Example of Fuzzy Logic Applied to Predict Surface Roughness in Magnesium Milling 1023.3 Conclusion 106References 1064 Modeling and Optimization of the Abrasive Water Jet Cutting Process 113Popan I. Alexandru4.1 Introduction 1134.2 Purpose and Methods 1154.3 Description of Experimental Setup 1184.4 Modeling and Optimization Process 1204.4.1 Mathematical Modeling 1204.4.2 Effect of Machining Parameters on Quality Characteristics 1264.5 Process Optimization 1274.6 Conclusions 129References 1305 Modeling and Optimization in Manufacturing by Laser 133Manuela Pacella5.1 Introduction 1335.2 Analytical Modeling of Laser Processing 1345.2.1 Process Parameters in Laser Micromachining 1355.2.2 Case Study – Topographical Modeling of Laser Micromachining 1375.2.3 Case Study – Modeling the Recoil Pressure in the Evaporation Stage of Laser Micromachining 1405.3 Experimental Optimization of Laser Micromachining of Complex 3D Freeform Surfaces 1445.3.1 Experimental Optimization of Channels/Grooves-like Structures 1445.3.2 Experimental Optimization of 3D Complex Freeform Surfaces 1475.4 Experimental Characterization of the Subsurface Integrity of Complex 3D Freeform Surfaces Post Laser Manufacturing 1515.5 Concluding Remarks 154References 1556 Introduction to Isostatic Pressing and Its Optimization 157Gautam R. Vadolia, K. Premjit Singh, Manoj K. Gupta, Bharat Doshi, and Vikas Rathore6.1 Introduction 1576.2 HIP Technology Positioning 1596.3 HIP Production Process for Powder Metallurgy 1596.3.1 Melting and Atomization 1606.3.2 Canning 1616.3.3 Hot Isostatic Pressing (HIP) Process 1636.3.3.1 Pressure Vessel 1646.3.3.2 Furnace 1646.3.3.3 Gas Handling 1656.3.3.4 Controls 1656.3.3.5 Auxiliary Systems 1666.3.4 Post Processing 1666.4 Recent Developments in HIP Equipment and HIP Processes 1666.4.1 Availability of Higher Size Equipment 1676.4.2 Equipment with Uniform Rapid Cooling (URC) 1676.4.3 Equipment with Uniform Rapid Quenching (URQ) 1686.4.4 Gas Impregnation Equipment, Insulation HIP, High-Pressure HIP [50, 55] 1686.4.5 Sinter-HIP 1686.4.6 HIP for Nuclear Waste Treatment 1696.4.7 Preheating HIP Equipment 1706.4.8 Atmosphere Control HIP Treatment 1706.4.9 Densal HIP Process 1706.4.10 Liquid HIP (LHIP) 1716.5 HIP Applications 1716.5.1 Densification of Product 1726.5.2 Consolidation of Powder for Near Net Shape (NNS) 1736.5.3 Cladding and Diffusion Bonding 1746.6 Typical HIP Installations 1756.7 Optimization in HIP 1766.7.1 Optimization of Process Parameters 1776.7.2 Optimization of Container/Canister Size 1796.7.2.1 Macroscopic Approach 1796.7.2.2 Microscopic Approach 1826.7.2.3 Soft Computing 1836.8 Summary 184References 1867 Modeling and Optimization Algorithms in Rapid Prototyping, Submerged Arc Welding, and Turning 193Munish K. Gupta, Mozammel Mia, Nancy Gupta, Sunpreet Singh, Ankush Choudhary, Muhammad Jamil, Aqib M. Khan, Krzysztof Nadolny, Wojciech Kapłonek, Danil Y. Pimenov, and Catalin I. Pruncu7.1 Introduction 1937.2 Evolutionary Algorithms 1957.2.1 Particle Swarm Optimization 1957.2.2 Classical Bacteria Foraging Optimization 1967.2.3 Self-Adaptive Bacteria Foraging Optimization 1977.2.4 Invasive Weed Optimization 1987.2.5 Artificial Bee Colony Optimization 1987.3 Experimental Conditions 1997.3.1 Fused Deposition Modeling 1997.3.1.1 Technical Specifications for FDM 2007.3.1.2 Optimization Model for FDM 2007.3.2 Submerged Arc Welding 2017.3.2.1 Technical Specifications for SAW 2017.3.2.2 Optimization Model for SAW 2027.3.3 Turning of Inconel-800 Alloy 2027.3.3.1 Technical Specifications for Turning 2037.3.3.2 Optimization Model for Turning 2037.4 Results and Discussion 2037.4.1 Parameters Initialization for Proposed Algorithms 2037.4.2 Optimization 2067.4.2.1 Optimization of FDM Parameters 2067.4.2.2 Optimization of SAW Parameters 2077.4.2.3 Optimization of Turning Parameters 2097.5 Best Optimization Model 2107.6 Conclusions 210Compliance with Ethical Standards 211References 2118 Optimization of Deposition Parameters in Plasma Spray Coatings 217Keshavamurthy Ramaiah, Naveena Bettahalli Eswaregowda, Vijay Tambrallimath, and Prabhakar Kuppahalli8.1 Introduction 2178.1.1 Effect of Plasma Spray Process Parameters 2208.1.1.1 Plasma Power 2218.1.1.2 Plasma Gas 2228.1.1.3 Carrier Gas 2228.1.1.4 Mass Flow Rate of Powder 2238.1.1.5 Stand-Off-Distance 2248.1.1.6 Spraying Angle 2258.1.1.7 Angle of Powder Injection 2258.1.2 Powder-Related Variables 2258.1.2.1 Substrate-Related Variables 2278.2 APS Process Optimization and Modeling 2288.3 Case Study 2308.4 Conclusions 231References 2329 Modeling and Optimization Methods in Forming Processes 237Catalin I. Pruncu, Jun Jiang, and Jianguo Lin9.1 Introduction 2379.2 Materials Used in Metal Forming Processes 2389.3 Sheet Manufacturing 2399.4 Metal Forming Challenges 2399.4.1 Friction in Metal Forming 2439.4.2 Tool Wear of Metal Forming 2459.4.3 Coatings in Metal Forming 2469.4.4 Micromechanics Modeling of Micro-Forming Processes 2479.5 Conclusions 247References 24810 Advances in Manufacturing, Laser Additive Techniques: Case Study 253Esther T. Akinlabi, Michael C. Agarana, and Stephen A. Akinlabi10.1 Introduction 25310.2 Advanced Manufacturing Technology 26210.2.1 Additive Manufacturing 26210.2.1.1 Electron Beam 26310.2.1.2 Laser-Based AM Process 26310.2.2 Advances in Additive Manufacturing 26410.2.3 Future Trends in Additive Manufacturing 26510.2.3.1 Robotics 26510.2.3.2 Manufacturing in the 4th Industrial Revolution 26610.2.3.3 Modeling in Manufacturing 26710.3 Applications of Laser Additive Techniques 26810.3.1 Application to Aerospace 26810.3.2 Application to Medicine 27010.3.3 Application to Fine Arts 27110.4 Sustainability of Advanced Manufacturing Techniques 27310.4.1 Sustainability of Process and Equipment 27310.4.2 Sustainability of Applications 27510.4.2.1 Advantages 27610.4.2.2 Disadvantages 27610.4.3 Comparative Advantage Sustainability 27710.5 Mathematical Methods for Advanced Manufacturing 27910.5.1 Mathematical Techniques 27910.5.2 Operational Research Techniques 28010.5.2.1 Applications of Waiting Line Theory 28110.5.2.2 Application of Game Theory 28110.5.2.3 Simulation and Monte Carlo Technique 28210.5.2.4 Dynamic Programming 28310.5.3 Heuristic Techniques 28410.5.4 Statistical Techniques 28410.6 Theoretical Analysis of Laser Additive Manufacturing 28410.6.1 Modeling and Simulation of Laser Additive Manufacturing 28410.6.2 Future Research Direction in Theoretical Additive Manufacturing 28810.7 Technology Challenges 28810.7.1 Challenges in Advanced Manufacturing 28810.7.2 Challenges in Laser Additive Manufacturing 29010.7.3 Suggested Solutions to Technology Challenges 29310.8 Summary 294References 295Index 303