Computer Modeling for Injection Molding

Computer Modeling for Injection Molding
اسم المؤلف
Huamin Zhou
التاريخ
17 أكتوبر 2019
المشاهدات
التقييم
(لا توجد تقييمات)
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Computer Modeling for Injection Molding
Simulation, Optimization, and Control
Edited by
Huamin Zhou
Huazhong University of Science and Technology, Wuhan, Hubei, China
Contents
Preface Xiii
Contributors Xv
Part I Background 1
1 Introduction 3
Huamin Zhou
1.1 Introduction of Injection Molding, 3
1.1.1 The injection molding process, 3
1.1.2 Importance of molding quality, 3
1.2 Factors Influencing Quality, 5
1.2.1 Molding polymer, 5
1.2.2 Plastic product, 6
1.2.3 Injection mold, 7
1.2.4 Process conditions, 7
1.2.5 Injection molding machine, 8
1.2.6 Interrelationship, 9
1.3 Computer Modeling, 10
1.3.1 Review of computer applications, 11
1.3.2 Computer modeling in quality enhancement, 11
1.3.3 Numerical simulation, 13
1.3.4 Optimization, 14
1.3.5 Process control, 15
1.4 Objective of This Book, 17
References, 18
2 Background 25
Huamin Zhou
2.1 Molding Materials, 25
2.1.1 Rheology, 25
2.1.2 Thermal properties, 27
vvi CONTENTS
2.1.3 PVT behavior, 29
2.1.4 Morphology, 30
2.2 Product Design, 31
2.2.1 Wall thickness, 31
2.2.2 Draft, 32
2.2.3 Parting plane, 32
2.2.4 Sharp corners, 33
2.2.5 Undercuts, 33
2.2.6 Bosses and cored holes, 33
2.2.7 Ribs, 33
2.3 Mold Design, 34
2.3.1 Mold cavity, 34
2.3.2 Parting plane, 35
2.3.3 Runner system, 36
2.3.4 Cooling system, 37
2.4 Molding Process, 37
2.4.1 The molding cycle, 38
2.4.2 Flow in the cavity, 40
2.4.3 Orientation, 41
2.4.4 Residual stresses, shrinkage, and warpage, 41
2.5 Process Control, 43
2.5.1 Characteristics of injection molding as a batch process, 45
2.5.2 Typical control problems in injection molding, 45
References, 47
PART II SIMULATION 49
3 Mathematical Models for the Filling and Packing Simulation 51
Huamin Zhou, Zixiang Hu, and Dequn Li
3.1 Material Constitutive Relationships and Viscosity Models, 51
3.1.1 Newtonian fluids, 51
3.1.2 Generalized Newtonian fluids, 52
3.1.3 Viscoelastic fluids, 54
3.2 Thermodynamic Relationships, 56
3.2.1 Constant specific volume, 57
3.2.2 Spencer–Gilmore model, 57
3.2.3 Tait model, 57
3.3 Thermal Properties Model, 58
3.4 Governing Equations for Fluid Flow, 59
3.4.1 Mass conservation equation, 59
3.4.2 Momentum conservation equation, 60
3.4.3 Energy conservation equation, 62
3.4.4 General transport equation, 64
3.5 Boundary Conditions, 65
3.5.1 Pressure boundary conditions, 66
3.5.2 Temperature boundary conditions, 66
3.5.3 Slip boundary condition, 66
3.6 Model Simplifications, 67
3.6.1 Hele–shaw model, 67
3.6.2 Governing equations for the filling phase, 68
3.6.3 Governing equations for the packing phase, 69
References, 69CONTENTS vii
4 Numerical Implementation for the Filling and Packing Simulation 71
Huamin Zhou, Zixiang Hu, Yun Zhang, and Dequn Li
4.1 Numerical Methods, 71
4.1.1 Finite difference method, 72
4.1.2 Finite volume method, 76
4.1.3 Finite element method, 85
4.1.4 Mesh-less methods, 95
4.2 Tracking of Moving Melt Fronts, 101
4.2.1 Overview, 101
4.2.2 FAN, 104
4.2.3 VOF, 105
4.2.4 Level set methods, 110
4.3 Methods for Solving Algebraic Equations, 113
4.3.1 Overview, 113
4.3.2 Direct methods, 114
4.3.3 Iterative methods, 116
4.3.4 Parallel computing, 121
References, 125
5 Cooling Simulation 129
Yun Zhang and Huamin Zhou
5.1 Introduction, 129
5.2 Modeling, 131
5.2.1 Cycle-averaged temperature field, 131
5.2.2 Cycle-averaged boundary conditions, 132
5.2.3 Coupling calculation procedure, 134
5.2.4 Calculating cooling time, 135
5.3 Numerical Implementation Based on Boundary Element Method, 136
5.3.1 Boundary integral equation, 136
5.3.2 Numerical implementation, 138
5.4 Acceleration Method, 143
5.4.1 Analysis of the coefficient matrix, 143
5.4.2 The approximated sparsification method, 144
5.4.3 The splitting method, 145
5.4.4 The fast multipole boundary element method, 146
5.4.5 Results and discussion, 148
5.5 Simulation for Transient Mold Temperature Field, 150
References, 154
6 Residual Stress and Warpage Simulation 157
Fen Liu, Lin Deng, and Huamin Zhou
6.1 Residual Stress Analysis, 157
6.1.1 Development of residual stress, 157
6.1.2 Model prediction, 159
6.1.3 Numerical simulation, 163
6.1.4 Case study, 165
6.2 Warpage Simulation, 170
6.2.1 Development of warpage, 172
6.2.2 Model prediction, 173
6.2.3 Implementation with surface model, 182
6.2.4 Case study, 186
References, 190viii CONTENTS
7 Microstructure and Morphology Simulation 195
Huamin Zhou, Fen Liu, and Peng Zhao
7.1 Types of Polymeric Systems, 195
7.1.1 Thermoplastics and thermosets, 195
7.1.2 Amorphous and crystalline polymers, 196
7.1.3 Blends and composites, 196
7.2 Crystallization, 196
7.2.1 Fundamentals, 196
7.2.2 Modeling, 197
7.2.3 Case study, 202
7.3 Phase Morphological Evolution in Polymer Blends, 203
7.3.1 Fundamentals, 205
7.3.2 Modeling, 207
7.3.3 Case study, 213
7.4 Orientation, 214
7.4.1 Molecular orientation, 215
7.4.2 Fiber orientation, 216
7.4.3 Case study, 218
7.5 Numerical Implementation, 220
7.5.1 Coupled procedure, 220
7.5.2 Stable scheme of the FEM, 221
7.5.3 Formulations of the velocity and pressure equations, 222
7.5.4 Formulations of temperature and microstructure equations, 223
7.6 Microstructure-Property Relationships, 224
7.6.1 Effect of crystallinity on property, 224
7.6.2 Effect of phase morphology on property, 225
7.6.3 Effect of orientation on property, 226
7.7 Multiscale Modeling and Simulation, 228
7.7.1 Molecular scale methods, 229
7.7.2 Microscale methods, 229
7.7.3 Meso/macroscale methods, 230
7.7.4 Multiscale strategies, 231
References, 231
8 Development and Application of Simulation Software 237
Zhigao Huang, Zixiang Hu, and Huamin Zhou
8.1 Development History of Injection Molding Simulation Models, 237
8.1.1 One-dimensional models, 238
8.1.2 2.5D models, 238
8.1.3 Three-dimensional models, 240
8.2 Development History of Injection Molding Simulation Software, 240
8.3 The Process of Performing Simulation Software, 243
8.3.1 Geometry modeling, 244
8.3.2 Selection of material, 245
8.3.3 Setting processing parameters, 246
8.4 Application of Simulation Results, 246
8.4.1 Dynamic display of melt flow front, 246
8.4.2 Cavity pressure, 246
8.4.3 Pressure at injection location, 247
8.4.4 Polymer temperature, 247
8.4.5 Shear rate, 247
8.4.6 Shear stress, 247CONTENTS ix
8.4.7 Weld lines, 247
8.4.8 Air traps, 248
8.4.9 Shrinkage index, 250
8.4.10 Cooling evaluation, 250
8.4.11 Warpage prediction, 251
References, 251
PART III OPTIMIZATION 255
9 Noniterative Optimization Methods 257
Peng Zhao, Yuehua Gao, Huamin Zhou, and Lih-Sheng Turng
9.1 Taguchi Method, 258
9.1.1 Orthogonal arrays, 258
9.1.2 Analysis of the S/N ratio, 259
9.1.3 Analysis of variance, 259
9.1.4 Taguchi technology, 259
9.2 Gray Relational Analysis, 260
9.2.1 Data preprocessing, 260
9.2.2 Gray relational coefficient and gray relational grade, 260
9.3 Expert Systems, 261
9.3.1 Knowledge base, 262
9.3.2 Inference engine, 263
9.4 Case-Based Reasoning, 266
9.4.1 Case representation, 266
9.4.2 Case retrieval, 267
9.4.3 Case adaptation, 267
9.5 Fuzzy Systems, 268
9.5.1 Fuzzy theory, 269
9.5.2 Fuzzy inference, 272
9.5.3 A fuzzy system for part defect correction, 274
9.6 Injection Molding Applications, 274
9.6.1 Review of noniteration optimization methods, 274
9.6.2 Application of the taguchi method, 276
9.6.3 Application of case-based reasoning and fuzzy systems, 278
References, 281
10 Intelligent Optimization Algorithms 283
Yuehua Gao, Peng Zhao, Lih-Sheng Turng, and Huamin Zhou
10.1 Genetic Algorithms, 283
10.1.1 Chromosome representation, 284
10.1.2 Selection, 284
10.1.3 Crossover and mutation operations, 284
10.1.4 Fitness function and termination, 285
10.2 Simulated Annealing Algorithms, 285
10.2.1 The fundamentals of the simulated annealing algorithm, 286
10.2.2 Optimum design algorithm for simulated annealing, 287
10.3 Particle Swarm Algorithms, 287
10.3.1 General procedures, 287
10.3.2 Determination of parameters, 288
10.4 Ant Colony Algorithms, 289
10.5 Hill Climbing Algorithms, 290x CONTENTS
10.5.1 General procedure, 290
10.5.2 Flow path generation with hill climbing algorithms, 290
References, 291
11 Optimization Methods Based on Surrogate Models 293
Yuehua Gao, Lih-Sheng Turng, Peng Zhao, and Huamin Zhou
11.1 Response Surface Method, 294
11.1.1 RSM theory, 294
11.1.2 Modeling error estimation, 295
11.1.3 Optimization process using RSM, 295
11.2 Artificial Neural Network, 296
11.2.1 Back propagation network, 296
11.2.2 BPN training process, 298
11.2.3 Optimization process based on ANN, 298
11.3 Support Vector Regression, 298
11.3.1 SVR theory, 299
11.3.2 Lagrange multipliers, 300
11.3.3 Kernel function, 300
11.3.4 Selection of SVR parameters, 301
11.4 Kriging Model, 301
11.4.1 Kriging model theory, 301
11.4.2 The correlation function, 302
11.4.3 Optimization design based on the kriging surrogate model, 302
11.5 Gaussian Process, 304
11.6 Injection Molding Applications of Optimization Methods Based on
Surrogate Models, 305
11.6.1 Application of the ANN model, 305
11.6.2 Application of the SVR model, 307
11.6.3 Application of the kriging model, 309
References, 312
PART IV PROCESS CONTROL 313
12 Feedback Control 315
Yi Yang and Furong Gao
12.1 Traditional Feedback Control, 315
12.2 Adaptive Control Strategy, 316
12.3 Model Predictive Control Strategy, 318
12.3.1 GPC design for barrel temperature control, 320
12.3.2 GPC controller parameter tuning, 321
12.3.3 Experimental test results, 322
12.4 Optimal Control Strategy, 322
12.4.1 TOC for barrel temperature start-up control, 323
12.4.2 Simulation results, 324
12.4.3 Experimental test results, 329
12.5 Intelligent Control Strategy, 329
12.5.1 Fuzzy injection velocity controller, 330
12.5.2 Fuzzy feed forward controller, 333
12.5.3 Test with different conditions, 333
12.6 Summary of Advanced Feedback Control, 335
References, 337CONTENTS xi
13 Learning Control 339
Yi Yang and Furong Gao
13.1 Learning Control, 339
13.1.1 Learning control for injection velocity profiling, 340
13.2 Two-Dimensional (2D) Control, 345
13.2.1 2D control of packing pressure, 346
13.3 Conclusions, 350
References, 352
14 Multivariate Statistical Process Control 355
Yuan Yao and Furong Gao
14.1 Statistical Process Control, 355
14.2 Multivariate Statistical Process Control, 356
14.2.1 Principal component analysis, 356
14.2.2 PCA-based process monitoring and fault diagnosis, 357
14.2.3 Normalization, 358
14.3 MSPC for Batch Processes, 358
14.4 MSPC for Injection Molding Process, 359
14.4.1 Phase-based sub-PCA, 360
14.4.2 Sub-PCA for batch processes with uneven operation durations, 361
14.4.3 Sub-PCA with limited reference data, 363
14.4.4 Applications, 365
14.5 Conclusions, 373
References, 373
15 Direct Quality Control 377
Yi Yang and Furong Gao
15.1 Review of Product Weight Control, 377
15.2 Methods, 378
15.2.1 Weight prediction using PCR model, 378
15.2.2 Overall weight control scheme and feedback adjustment, 379
15.3 Experimental Results and Discussion, 380
15.3.1 Factor screening experiment, 380
15.3.2 PCR modeling of product weight, 382
15.3.3 Closed-loop weight control based on PCR model, 387
15.4 Conclusions, 389
References, 389
INDEX
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