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 |  موضوع: كتاب Polymer Processing Society الخميس 21 نوفمبر 2019, 4:46 pm | |
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أخوانى فى الله
أحضرت لكم كتاب
Polymer Processing Society
Yi Yang , Xi Chen , Ningyun Lu , Furong Gao
Progress in Polymer Processing Series
Y. Yang / X. Chen / N. Lu / F. Gao
Injection Molding Process Control, Monitoring, and Optimization
T. Kanai / G. A. Campbell
Film Processing Advances
T. Kanai / G. A. Campbell
Film Processing
I. Manas-Zloczower
Mixing and Compounding of Polymers, 2nd Edition
M. R. Kamal / A. I. Isayev / S.-J. Liu
Injection Molding
J. L. White / H. Potente
Screw Extrusion
W. E. Baker / C. E. Scott / G.-H. Hu
Reactive Polymer Blending
I. M. Ward / P. D. Coates / M. M. Dumoulin
Solid Phase Processing of Polymers
R. S. Davé / A. C. Loos
Processing of Composites
و المحتوى كما يلي :
Contents
Foreword VII
1 Injection Molding: Background 1
1.1 Plastic Materials and Properties .1
1.1.1 Plastics Classification 3
1.1.1.1 Molecular Structure .3
1.1.1.2 Processability 5
1.1.1.3 Method of Synthesis .5
1.1.1.4 Monomer(s) in Molecules 7
1.1.2 Structural Characteristics of Plastic 7
1.1.2.1 Molecular Weight and Distribution .8
1.1.2.2 Degrees of Crystallinity .10
1.1.2.3 Orientation .11
1.1.3 Basic Rheology Terminology .12
1.1.4 Non-Newtonian Flow: Phenomenon and Constitutive Equations 15
1.1.4.1 Normal Stress Differences in Shear Flows 15
1.1.4.2 Viscoelastic Behavior .17
1.1.4.3 Viscoelastic Models 19
1.1.4.4 Extensional (Elongation) Flow 21
1.1.4.5 Polymer Melt Constitutive Equations for Viscous Flow .22
1.1.4.6 Power Law Constitutive Equation .23
1.1.4.7 Effects of Temperature and Pressure on Viscosity .26
1.1.4.8 Effect of Temperature on Viscosity 26
1.2 Plastics Processing Technologies 29
1.2.1 Extrusion .29
1.2.2 Blow Molding 31
1.2.3 Injection Molding Machine, Process, and Key Variables 32
1.2.3.1 Injection Molding Machine and Process .32
1.2.3.2 Injection Molding Key Process Variables .35
References .37XII Contents
2 Feedback Control Algorithms Developed for Continuous Processes 39
2.1 Introduction of Feedback Control Background 39
2.2 Traditional Feedback Control: PID .41
2.3 Adaptive Control .44
2.3.1 Model Estimation 45
2.3.2 Pole-Placement Controller Design .46
2.3.3 Solving the Diophantine Equation 47
2.3.4 Injection Velocity Adaptive Control Result 48
2.3.4.1 Antiwindup Estimation .50
2.3.4.2 Adaptive Feed-Forward Control .52
2.3.4.3 Cycle-to-Cycle Adaptation .57
2.3.4.4 Adaptive Control Results with Different Conditions 58
2.4 Model Predictive Control 61
2.4.1 Basic Principle of MPC and GPC 61
2.4.2 Model Order Determination 64
2.4.3 Comparison with Pole-Placement Control 65
2.4.4 GPC Control with Different Conditions 68
2.5 Fuzzy Systems in Injection Molding Control 70
2.5.1 Fuzzy Inference System Background 70
2.5.2 Fuzzy V/P Switch-Over 71
2.5.3 Fuzzy V/P System Experimental Test 76
2.5.4 Further Improvement .76
References .80
3 Learning Type Control for the Injection Molding Process 83
3.1 Learning Type Control Background .83
3.2 Basic Iterative Learning Control 85
3.2.1 PID-Type ILC 85
3.2.2 Time-Delay Consideration .86
3.2.3 P-Type ILC for Injection Velocity 87
3.2.4 P-Type ILC for Packing Pressure 88
3.3 Optimal Iterative Learning Control 90
3.3.1 Problem Formulation .91
3.3.2 Optimal Iterative Learning Controller .92
3.3.3 Robust and Convergence Analysis 94
3.3.4 Selection of the Weighting Matrices .96
3.3.5 Simulation Results .97
3.3.6 Experimental Results of Optimal ILC .101
References 106Contents XIII
4 Two-Dimensional Control Algorithms 109
4.1 Two-Dimensional Control Background 109
4.2 Two-Dimensional Generalized Predictive Iterative Learning Control .112
4.2.1 2D-GPILC Control Algorithm 112
4.2.2 Injection Velocity Control with 2D-GPILC .116
4.3 Two-Dimensional Dynamic Matrix Control 121
4.3.1 Problem Formulation 121
4.3.2 Controller Design .122
4.3.2.1 2D Equivalent Model with Repetitive Nature 123
4.3.2.2 2D Prediction Model .123
4.3.2.3 Cost Function and Control Law 125
4.3.2.4 Analysis of Convergence and Robustness .127
4.3.2.4.1 Model of the Closed-Loop Control System .128
4.3.2.4.2 Tracking Error and Convergence Conditions .130
4.3.2.4.3 Robustness Analysis .133
4.3.3 Simulation Illustration .136
4.3.3.1 Case 1: Convergence Test .138
4.3.3.2 Case 2: Repetitive Disturbances .140
4.3.3.3 Case 3: Nonrepetitive Disturbances 142
4.3.4 Experimental Test of 2D-DMC .144
References 148
5 Statistical Process Monitoring (SPM) of Injection Molding: Basics 149
5.1 Process Monitoring 149
5.2 Statistical Process Monitoring (SPM) .150
5.2.1 Data Collection and Preprocessing .154
5.2.2 Construction of Nominal Statistical Model .155
5.2.3 Application of Statistical Models .157
5.3 Multivariate Statistical Analysis Methods for SPM 158
5.3.1 Principal Component Analysis and Partial Least Squares 158
5.3.2 PCA/PLS-Based Statistical Process Monitoring .160
5.3.3 Multiway PCA/PLS 162
5.3.4 Multiway PCA/PLS-Based Batch Process Monitoring 165
5.4 Challenges in Monitoring Injection Molding Process 166
5.4.1 Multiple Operation Phases 166
5.4.2 Within-Batch and Batch-to-Batch Dynamics 168
5.4.3 Unequal Batch Length .169
References 170XIV Contents
6 Phase-Based SPM Strategies 173
6.1 Introduction 173
6.2 Phase-Division-Based Sub-PCA Modeling and Monitoring 175
6.2.1 Overview 175
6.2.2 Data Normalization 176
6.2.3 Phase Recognition and Division .177
6.2.4 Phase PCA Modeling .180
6.2.5 Statistics and Control Limits 181
6.2.6 Online Process Monitoring .182
6.2.7 Summary 183
6.3 Application of Phase-Based SPM to Injection Molding .184
6.3.1 Experimental Setup .184
6.3.2 Result Analysis of Phase Division and Modeling 185
6.3.3 Result Analysis of Process Monitoring and Fault Diagnosis 187
6.4 Improved Phase-Based SPM for Unequal-Length Batch Processes .193
6.4.1 Overview 193
6.4.2 Data Normalization 194
6.4.3 Phase Recognition and Division .196
6.4.4 Sub-PCA Modeling Procedure .198
6.4.5 Process Monitoring Procedure 199
6.4.6 Summary 200
6.5 Application of Improved Phase-Based SPM to Injection Molding 202
6.5.1 Experimental Setup .202
6.5.2 Result Analysis of Phase Division and Modeling 203
6.5.3 Result Analysis of Process Monitoring and Fault Diagnosis 205
6.5.3.1 Monitoring of a Normal Batch .205
6.5.3.2 Monitoring of Faulty Batches .207
References 211
7 Phase-Based Quality Improvement Strategies 213
7.1 Introduction 213
7.2 Phase-Based Process Analysis and End-Product Quality Prediction
(Method A) .214
7.2.1 Phase-Based PLS Modeling .214
7.2.2 Phase-Based Quality-Related Process Analysis .217
7.2.3 Online Quality Prediction .219
7.3 Application of Phase PLS Model (Method A) to Injection Molding .220
7.3.1 Experimental Setup .220
7.3.2 Illustration of Phase-Based Process Analysis .222
7.3.2.1 Phase Division 222
7.3.2.2 Process Analysis in the Critical-to-Surface Phase .224Contents XV
7.3.2.3 Process Analysis in Critical-to-Dimension Phases 225
7.3.3 Illustration of Phase-Based Quality Prediction .228
7.4 Phase-Based Process Analysis and End-Product Quality Prediction
(Method B) .232
7.4.1 Critical Phase Identification 232
7.4.2 Key Variable Selection Based on Variable-Wise Unfolding 235
7.4.3 Phase-Based PLS Modeling Algorithm 239
7.4.4 Online Quality Prediction .241
7.5 Application of Phase PLS Model (Method B) to Injection Molding 242
7.5.1 Illustration of Correlation Analysis .242
7.5.2 Results of Quality Prediction 246
References 250
8 In-Mold Capacitive Transducer for Injection Molding Process 251
8.1 Fundamentals of Capacitive Transducers 252
8.2 Dielectric Properties of Polymers 255
8.3 Principle and Preliminary Tests of Capacitive Transducer in Injection
Mold 257
8.4 Design of In-Mold Capacitive Transducer 260
8.4.1 Mold Base Design .260
8.4.2 Mold Insert Design 263
8.4.3 Capacitance Measurement 264
8.5 Applications in Melt Flow Detection during Filling Stage 266
8.5.1 Detection of Filling Start .266
8.5.2 Detection of V/P Transfer .267
8.5.3 Detection of melt flow during filling 269
8.6 Applications for the Packing and Cooling Stages .279
8.6.1 Guide to Packing Pressure Setting .279
8.6.2 Detection of Gate Freezing-Off Time 282
8.6.3 Solidification Rate Monitoring .284
8.7 Online Part Weight Prediction Using the Capacitive Transducer 287
References 292
9 Profile Setting of Injection Velocity 295
9.1 Constant Melt-Front-Velocity Strategy 295
9.2 Scheme Based on Average-flow-length 301
9.3 Neural Network Model of Average-flow-length .302
9.3.1 Inputs and Output of the Neural Network Model .302
9.3.2 Architecture of the Neural Network Model 303
9.3.3 Training Algorithm 305
9.3.4 Data Collection of Training and Validation Samples .306XVI Contents
9.3.5 Model Performance 308
9.4 Profiling Strategy via Optimization .317
9.5 Parabolic Velocity Profile .319
9.6 Piece-Wise Ramp Velocity Profile 323
9.7 Conclusions 325
References 326
10 Profile Setting of Packing Pressure 329
10.1 Online Autodetection of Gate Freezing-Off Point 329
10.1.1 Gate Freezing-Off Detection .330
10.1.2 Development of Autodetection System 333
10.1.3 Tests of Constant Packing Pressure Cases .337
10.1.4 Tests of Varying Packing Pressure Profile Cases 342
10.1.4.1 Online Detection Results of Step Pressure Profile .342
10.1.4.2 Online Detection Results of Ramp Pressure Profile .343
10.2 Influence of Packing Profile on Part Quality .346
10.2.1 Constant Packing Profile .348
10.2.2 Ramp Packing Profile 351
10.2.3 Step-Change Packing Profile 359
10.2.4 Summary 363
10.3 Profiling of Packing Pressure 364
10.3.1 Profiling Rules 364
10.3.2 Online Profiling of Constant Packing Pressure .365
10.3.3 Ramp Profile for Specific Thickness Distribution .368
10.4 Conclusions 370
References 370
11 Parameter Setting for the Plastication Stage 371
11.1 Visual Barrel System Development .372
11.2 Plastication Behavior 373
11.2.1 Melting Behavior 373
11.2.2 Processing Condition Effects 380
11.3 Neural Network Modeling of Melt Temperature .384
11.4 Optimal Parameter Setting for the Plastication Stage 385
References 389
Subject Index 391
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