Atjaunināt sīkdatņu piekrišanu

E-grāmata: Computer Modeling for Injection Molding: Simulation, Optimization, and Control

Edited by
  • Formāts: EPUB+DRM
  • Izdošanas datums: 30-Nov-2012
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118444917
Citas grāmatas par šo tēmu:
  • Formāts - EPUB+DRM
  • Cena: 155,79 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Bibliotēkām
Computer Modeling for Injection Molding: Simulation, Optimization, and ControlPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 30-Nov-2012
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118444917
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

Chinese engineers contribute to a systematic and comprehensive introductory textbook on computer modeling for injection molding, the most important process in manufacturing plastic products. After setting out the background, they consider such topics as mathematical models and numerical implementation for filling and packing simulation, simulating residual stress and warpage, developing and applying simulation software, non-iterative optimization methods, optimization methods based on surrogate models, and multivariate statistical process control. Annotation ©2013 Book News, Inc., Portland, OR (booknews.com)

This book covers a wide range of applications and uses of simulation and modeling techniques in polymer injection molding, filling a noticeable gap in the literature of design, manufacturing, and the use of plastics injection molding. The authors help readers solve problems in the advanced control, simulation, monitoring, and optimization of injection molding processes. The book provides a tool for researchers and engineers to calculate the mold filling, optimization of processing control, and quality estimation before prototype molding.

Recenzijas

Overall, this book can be recommended for a reader interested in getting an overall idea of the contribution of computer science to injection molding, or to a researcher looking for an updated review of the latest applications of numerical techniques to this technology.  (Materials Views, 22 October  2013)

Preface xiii
Contributors xv
PART I BACKGROUND
1(48)
1 Introduction
3(22)
Huamin Zhou
1.1 Introduction of Injection Molding
3(2)
1.1.1 The injection molding process
3(1)
1.1.2 Importance of molding quality
3(2)
1.2 Factors Influencing Quality
5(5)
1.2.1 Molding polymer
5(1)
1.2.2 Plastic product
6(1)
1.2.3 Injection mold
7(1)
1.2.4 Process conditions
7(1)
1.2.5 Injection molding machine
8(1)
1.2.6 Interrelationship
9(1)
1.3 Computer Modeling
10(7)
1.3.1 Review of computer applications
11(1)
1.3.2 Computer modeling in quality enhancement
11(2)
1.3.3 Numerical simulation
13(1)
1.3.4 Optimization
14(1)
1.3.5 Process control
15(2)
1.4 Objective of This Book
17(8)
References
18(7)
2 Background
25(24)
Huamin Zhou
2.1 Molding Materials
25(6)
2.1.1 Rheology
25(2)
2.1.2 Thermal properties
27(2)
2.1.3 PVT behavior
29(1)
2.1.4 Morphology
30(1)
2.2 Product Design
31(3)
2.2.1 Wall thickness
31(1)
2.2.2 Draft
32(1)
2.2.3 Parting plane
32(1)
2.2.4 Sharp corners
33(1)
2.2.5 Undercuts
33(1)
2.2.6 Bosses and cored holes
33(1)
2.2.7 Ribs
33(1)
2.3 Mold Design
34(3)
2.3.1 Mold cavity
34(1)
2.3.2 Parting plane
35(1)
2.3.3 Runner system
36(1)
2.3.4 Cooling system
37(1)
2.4 Molding Process
37(6)
2.4.1 The molding cycle
38(2)
2.4.2 Flow in the cavity
40(1)
2.4.3 Orientation
41(1)
2.4.4 Residual stresses, shrinkage, and warpage
41(2)
2.5 Process Control
43(6)
2.5.1 Characteristics of injection molding as a batch process
45(1)
2.5.2 Typical control problems in injection molding
45(2)
References
47(2)
PART II SIMULATION
49(206)
3 Mathematical Models for the Filling and Packing Simulation
51(20)
Huamin Zhou
Zixiang Hu
Dequn Li
3.1 Material Constitutive Relationships and Viscosity Models
51(5)
3.1.1 Newtonian fluids
51(1)
3.1.2 Generalized Newtonian fluids
52(2)
3.1.3 Viscoelastic fluids
54(2)
3.2 Thermodynamic Relationships
56(2)
3.2.1 Constant specific volume
57(1)
3.2.2 Spencer-Gilmore model
57(1)
3.2.3 Tait model
57(1)
3.3 Thermal Properties Model
58(1)
3.4 Governing Equations for Fluid Flow
59(6)
3.4.1 Mass conservation equation
59(1)
3.4.2 Momentum conservation equation
60(2)
3.4.3 Energy conservation equation
62(2)
3.4.4 General transport equation
64(1)
3.5 Boundary Conditions
65(2)
3.5.1 Pressure boundary conditions
66(1)
3.5.2 Temperature boundary conditions
66(1)
3.5.3 Slip boundary condition
66(1)
3.6 Model Simplifications
67(4)
3.6.1 Hele-shaw model
67(1)
3.6.2 Governing equations for the filling phase
68(1)
3.6.3 Governing equations for the packing phase
69(1)
References
69(2)
4 Numerical Implementation for the Filling and Packing Simulation
71(58)
Huamin Zhou
Zixiang Hu
Yun Zhang
Dequn Li
4.1 Numerical Methods
71(30)
4.1.1 Finite difference method
72(4)
4.1.2 Finite volume method
76(9)
4.1.3 Finite element method
85(10)
4.1.4 Mesh-less methods
95(6)
4.2 Tracking of Moving Melt Fronts
101(12)
4.2.1 Overview
101(3)
4.2.2 FAN
104(1)
4.2.3 VOF
105(5)
4.2.4 Level set methods
110(3)
4.3 Methods for Solving Algebraic Equations
113(16)
4.3.1 Overview
113(1)
4.3.2 Direct methods
114(2)
4.3.3 Iterative methods
116(5)
4.3.4 Parallel computing
121(4)
References
125(4)
5 Cooling Simulation
129(28)
Yun Zhang
Huamin Zhou
5.1 Introduction
129(2)
5.2 Modeling
131(5)
5.2.1 Cycle-averaged temperature field
131(1)
5.2.2 Cycle-averaged boundary conditions
132(2)
5.2.3 Coupling calculation procedure
134(1)
5.2.4 Calculating cooling time
135(1)
5.3 Numerical Implementation Based on Boundary Element Method
136(7)
5.3.1 Boundary integral equation
136(2)
5.3.2 Numerical implementation
138(5)
5.4 Acceleration Method
143(7)
5.4.1 Analysis of the coefficient matrix
143(1)
5.4.2 The approximated sparsification method
144(1)
5.4.3 The splitting method
145(1)
5.4.4 The fast multipole boundary element method
146(2)
5.4.5 Results and discussion
148(2)
5.5 Simulation for Transient Mold Temperature Field
150(7)
References
154(3)
6 Residual Stress and Warpage Simulation
157(38)
Fen Liu
Lin Deng
Huamin Zhou
6.1 Residual Stress Analysis
157(13)
6.1.1 Development of residual stress
157(2)
6.1.2 Model prediction
159(4)
6.1.3 Numerical simulation
163(2)
6.1.4 Case study
165(5)
6.2 Warpage Simulation
170(25)
6.2.1 Development of warpage
172(1)
6.2.2 Model prediction
173(9)
6.2.3 Implementation with surface model
182(4)
6.2.4 Case study
186(4)
References
190(5)
7 Microstructure and Morphology Simulation
195(42)
Huamin Zhou
Fen Liu
Peng Zhao
7.1 Types of Polymeric Systems
195(1)
7.1.1 Thermoplastics and thermosets
195(1)
7.1.2 Amorphous and crystalline polymers
196(1)
7.1.3 Blends and composites
196(1)
7.2 Crystallization
196(7)
7.2.1 Fundamentals
196(1)
7.2.2 Modeling
197(5)
7.2.3 Case study
202(1)
7.3 Phase Morphological Evolution in Polymer Blends
203(11)
7.3.1 Fundamentals
205(2)
7.3.2 Modeling
207(6)
7.3.3 Case study
213(1)
7.4 Orientation
214(6)
7.4.1 Molecular orientation
215(1)
7.4.2 Fiber orientation
216(2)
7.4.3 Case study
218(2)
7.5 Numerical Implementation
220(4)
7.5.1 Coupled procedure
220(1)
7.5.2 Stable scheme of the FEM
221(1)
7.5.3 Formulations of the velocity and pressure equations
222(1)
7.5.4 Formulations of temperature and microstructure equations
223(1)
7.6 Microstructure-Property Relationships
224(4)
7.6.1 Effect of crystallinity on property
224(1)
7.6.2 Effect of phase morphology on property
225(1)
7.6.3 Effect of orientation on property
226(2)
7.7 Multiscale Modeling and Simulation
228(9)
7.7.1 Molecular scale methods
229(1)
7.7.2 Microscale methods
229(1)
7.7.3 Meso/macroscale methods
230(1)
7.7.4 Multiscale strategies
231(1)
References
231(6)
8 Development and Application of Simulation Software
237(18)
Zhigao Huang
Zixiang Hu
Huamin Zhou
8.1 Development History of Injection Molding Simulation Models
237(3)
8.1.1 One-dimensional models
238(1)
8.1.2 2.5D models
238(2)
8.1.3 Three-dimensional models
240(1)
8.2 Development History of Injection Molding Simulation Software
240(3)
8.3 The Process of Performing Simulation Software
243(3)
8.3.1 Geometry modeling
244(1)
8.3.2 Selection of material
245(1)
8.3.3 Setting processing parameters
246(1)
8.4 Application of Simulation Results
246(9)
8.4.1 Dynamic display of melt flow front
246(1)
8.4.2 Cavity pressure
246(1)
8.4.3 Pressure at injection location
247(1)
8.4.4 Polymer temperature
247(1)
8.4.5 Shear rate
247(1)
8.4.6 Shear stress
247(1)
8.4.7 Weld lines
247(1)
8.4.8 Air traps
248(2)
8.4.9 Shrinkage index
250(1)
8.4.10 Cooling evaluation
250(1)
8.4.11 Warpage prediction
251(1)
References
251(4)
PART III OPTIMIZATION
255(58)
9 Noniterative Optimization Methods
257(26)
Peng Zhao
Yuehua Gao
Huamin Zhou
Lih-Sheng Turng
9.1 Taguchi Method
258(2)
9.1.1 Orthogonal arrays
258(1)
9.1.2 Analysis of the S/N ratio
259(1)
9.1.3 Analysis of variance
259(1)
9.1.4 Taguchi technology
259(1)
9.2 Gray Relational Analysis
260(1)
9.2.1 Data preprocessing
260(1)
9.2.2 Gray relational coefficient and gray relational grade
260(1)
9.3 Expert Systems
261(5)
9.3.1 Knowledge base
262(1)
9.3.2 Inference engine
263(3)
9.4 Case-Based Reasoning
266(2)
9.4.1 Case representation
266(1)
9.4.2 Case retrieval
267(1)
9.4.3 Case adaptation
267(1)
9.5 Fuzzy Systems
268(6)
9.5.1 Fuzzy theory
269(3)
9.5.2 Fuzzy inference
272(2)
9.5.3 A fuzzy system for part defect correction
274(1)
9.6 Injection Molding Applications
274(9)
9.6.1 Review of noniteration optimization methods
274(2)
9.6.2 Application of the taguchi method
276(2)
9.6.3 Application of case-based reasoning and fuzzy systems
278(3)
References
281(2)
10 Intelligent Optimization Algorithms
283(10)
Yuehua Gao
Peng Zhao
Lih-Sheng Turng
Huamin Zhou
10.1 Genetic Algorithms
283(2)
10.1.1 Chromosome representation
284(1)
10.1.2 Selection
284(1)
10.1.3 Crossover and mutation operations
284(1)
10.1.4 Fitness function and termination
285(1)
10.2 Simulated Annealing Algorithms
285(2)
10.2.1 The fundamentals of the simulated annealing algorithm
286(1)
10.2.2 Optimum design algorithm for simulated annealing
287(1)
10.3 Particle Swarm Algorithms
287(2)
10.3.1 General procedures
287(1)
10.3.2 Determination of parameters
288(1)
10.4 Ant Colony Algorithms
289(1)
10.5 Hill Climbing Algorithms
290(3)
10.5.1 General procedure
290(1)
10.5.2 Flow path generation with hill climbing algorithms
290(1)
References
291(2)
11 Optimization Methods Based on Surrogate Models
293(20)
Yuehua Gao
Lih-Sheng Turng
Peng Zhao
Huamin Zhou
11.1 Response Surface Method
294(2)
11.1.1 RSM theory
294(1)
11.1.2 Modeling error estimation
295(1)
11.1.3 Optimization process using RSM
295(1)
11.2 Artificial Neural Network
296(2)
11.2.1 Back propagation network
296(2)
11.2.2 BPN training process
298(1)
11.2.3 Optimization process based on ANN
298(1)
11.3 Support Vector Regression
298(3)
11.3.1 SVR theory
299(1)
11.3.2 Lagrange multipliers
300(1)
11.3.3 Kernel function
300(1)
11.3.4 Selection of SVR parameters
301(1)
11.4 Kriging Model
301(3)
11.4.1 Kriging model theory
301(1)
11.4.2 The correlation function
302(1)
11.4.3 Optimization design based on the kriging surrogate model
302(2)
11.5 Gaussian Process
304(1)
11.6 Injection Molding Applications of Optimization Methods Based on Surrogate Models
305(8)
11.6.1 Application of the ANN model
305(2)
11.6.2 Application of the SVR model
307(2)
11.6.3 Application of the kriging model
309(3)
References
312(1)
PART IV PROCESS CONTROL
313(78)
12 Feedback Control
315(24)
Yi Yang
Furong Gao
12.1 Traditional Feedback Control
315(1)
12.2 Adaptive Control Strategy
316(2)
12.3 Model Predictive Control Strategy
318(4)
12.3.1 GPC design for barrel temperature control
320(1)
12.3.2 GPC controller parameter tuning
321(1)
12.3.3 Experimental test results
322(1)
12.4 Optimal Control Strategy
322(7)
12.4.1 TOC for barrel temperature start-up control
323(1)
12.4.2 Simulation results
324(5)
12.4.3 Experimental test results
329(1)
12.5 Intelligent Control Strategy
329(6)
12.5.1 Fuzzy injection velocity controller
330(3)
12.5.2 Fuzzy feed forward controller
333(1)
12.5.3 Test with different conditions
333(2)
12.6 Summary of Advanced Feedback Control
335(4)
References
337(2)
13 Learning Control
339(16)
Yi Yang
Furong Gao
13.1 Learning Control
339(6)
13.1.1 Learning control for injection velocity profiling
340(5)
13.2 Two-Dimensional (2D) Control
345(5)
13.2.1 2D control of packing pressure
346(4)
13.3 Conclusions
350(5)
References
352(3)
14 Multivariate Statistical Process Control
355(22)
Yuan Yao
Furong Gao
14.1 Statistical Process Control
355(1)
14.2 Multivariate Statistical Process Control
356(2)
14.2.1 Principal component analysis
356(1)
14.2.2 PCA-based process monitoring and fault diagnosis
357(1)
14.2.3 Normalization
358(1)
14.3 MSPC for Batch Processes
358(1)
14.4 MSPC for Injection Molding Process
359(14)
14.4.1 Phase-based sub-PCA
360(1)
14.4.2 Sub-PCA for batch processes with uneven operation durations
361(2)
14.4.3 Sub-PCA with limited reference data
363(2)
14.4.4 Applications
365(8)
14.5 Conclusions
373(4)
References
373(4)
15 Direct Quality Control
377(14)
Yi Yang
Furong Gao
15.1 Review of Product Weight Control
377(1)
15.2 Methods
378(2)
15.2.1 Weight prediction using PCR model
378(1)
15.2.2 Overall weight control scheme and feedback adjustment
379(1)
15.3 Experimental Results and Discussion
380(9)
15.3.1 Factor screening experiment
380(2)
15.3.2 PCR modeling of product weight
382(5)
15.3.3 Closed-loop weight control based on PCR model
387(2)
15.4 Conclusions
389(2)
References
389(2)
Index 391
HUAMIN ZHOU, PhD, is Vice Dean of the School of Materials Science and Engineering and Vice Director of the State Key Laboratory of Materials Processing and Die & Mould Technology at the Huazhong University of Science and Technology, Wuhan, China. Dr. Zhou has published more than 200 peer-reviewed papers. His research examines polymer processing, numerical simulation, and process optimization and control.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97811184449176e.html
Keywords: