Injection Molding Process Control, Monitoring, and Optimization

Name: Injection Molding Process Control, Monitoring, and Optimization
Brand: Hanser Publications
Price: 199.99 EUR
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Yi Yang Xi Chen Ningyun Lu Furong Gao(Author)

Hanser Publications (Publisher)

1st Edition

Published on 8. August 2016

413 pages

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978-1-56990-593-7 (ISBN)

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Content

Intro
Foreword
Contents
1 Injection Molding: Background
1.1 Plastic Materials and Properties
1.1.1 Plastics Classification
1.1.1.1 Molecular Structure
1.1.1.2 Processability
1.1.1.3 Method of Synthesis
1.1.1.4 Monomer(s) in Molecules
1.1.2 Structural Characteristics of Plastic
1.1.2.1 Molecular Weight and Distribution
1.1.2.2 Degrees of Crystallinity
1.1.2.3 Orientation
1.1.3 Basic Rheology Terminology
1.1.4 Non-Newtonian Flow: Phenomenon and Constitutive Equations
1.1.4.1 Normal Stress Differences in Shear Flows
1.1.4.2 Viscoelastic Behavior
1.1.4.3 Viscoelastic Models
1.1.4.4 Extensional (Elongation) Flow
1.1.4.5 Polymer Melt Constitutive Equations for Viscous Flow
1.1.4.6 Power Law Constitutive Equation
1.1.4.7 Effects of Temperature and Pressure on Viscosity
1.1.4.8 Effect of Temperature on Viscosity
1.2 Plastics Processing Technologies
1.2.1 Extrusion
1.2.2 Blow Molding
1.2.3 Injection Molding Machine, Process, and Key Variables
1.2.3.1 Injection Molding Machine and Process
1.2.3.2 Injection Molding Key Process Variables
References
2 Feedback Control Algorithms Developed for?Continuous Processes
2.1 Introduction of Feedback Control?Background
2.2 Traditional Feedback Control: PID
2.3 Adaptive Control
2.3.1 Model Estimation
2.3.2 Pole-Placement Controller Design
2.3.3 Solving the Diophantine Equation
2.3.4 Injection Velocity Adaptive Control Result
2.3.4.1 Antiwindup Estimation
2.3.4.2 Adaptive Feed-Forward Control
2.3.4.3 Cycle-to-Cycle Adaptation
2.3.4.4 Adaptive Control Results with Different Conditions
2.4 Model Predictive Control
2.4.1 Basic Principle of MPC and GPC
2.4.2 Model Order Determination
2.4.3 Comparison with Pole-Placement Control
2.4.4 GPC Control with Different Conditions
2.5 Fuzzy Systems in Injection Molding Control
2.5.1 Fuzzy Inference System Background
2.5.2 Fuzzy V/P Switch-Over
2.5.3 Fuzzy V/P System Experimental Test
2.5.4 Further Improvement
References
3 Learning Type Control for the Injection Molding Process
3.1 Learning Type Control Background
3.2 Basic Iterative Learning Control
3.2.1 PID-Type ILC
3.2.2 Time-Delay Consideration
3.2.3 P-Type ILC for Injection Velocity
3.2.4 P-Type ILC for Packing Pressure
3.3 Optimal Iterative Learning Control
3.3.1 Problem Formulation
3.3.2 Optimal Iterative Learning Controller
3.3.3 Robust and Convergence Analysis
3.3.4 Selection of the Weighting Matrices
3.3.5 Simulation Results
3.3.6 Experimental Results of Optimal ILC
References
4 Two-Dimensional Control Algorithms
4.1 Two-Dimensional Control Background
4.2 Two-Dimensional Generalized Predictive Iterative Learning Control
4.2.1 2D-GPILC Control Algorithm
4.2.2 Injection Velocity Control with 2D-GPILC
4.3 Two-Dimensional Dynamic Matrix Control
4.3.1 Problem Formulation
4.3.2 Controller Design
4.3.2.1 2D Equivalent Model with Repetitive Nature
4.3.2.2 2D Prediction Model
4.3.2.3 Cost Function and Control Law
4.3.2.4 Analysis of Convergence and Robustness
4.3.2.4.1 Model of the Closed-Loop Control System
4.3.2.4.2 Tracking Error and Convergence Conditions
4.3.2.4.3 Robustness Analysis
4.3.3 Simulation Illustration
4.3.3.1 Case?1: Convergence Test
4.3.3.2 Case?2: Repetitive Disturbances
4.3.3.3 Case?3: Nonrepetitive Disturbances
4.3.4 Experimental Test of 2D-DMC
References
5 Statistical Process Monitoring (SPM) of Injection Molding: Basics
5.1 Process Monitoring
5.2 Statistical Process Monitoring (SPM)
5.2.1 Data Collection and Preprocessing
5.2.2 Construction of Nominal Statistical Model
5.2.3 Application of Statistical Models
5.3 Multivariate Statistical Analysis Methods for SPM
5.3.1 Principal Component Analysis and Partial Least Squares
5.3.2 PCA/PLS-Based Statistical Process Monitoring
5.3.3 Multiway PCA/PLS
5.3.4 Multiway PCA/PLS-Based Batch Process Monitoring
5.4 Challenges in Monitoring Injection Molding Process
5.4.1 Multiple Operation Phases
5.4.2 Within-Batch and Batch-to-Batch Dynamics
5.4.3 Unequal Batch Length
References
6 Phase-Based SPM?Strategies
6.1 Introduction
6.2 Phase-Division-Based Sub-PCA Modeling and Monitoring
6.2.1 Overview
6.2.2 Data Normalization
6.2.3 Phase Recognition and Division
6.2.4 Phase PCA Modeling
6.2.5 Statistics and Control Limits
6.2.6 Online Process Monitoring
6.2.7 Summary
6.3 Application of Phase-Based SPM to?Injection Molding
6.3.1 Experimental Setup
6.3.2 Result Analysis of Phase Division and Modeling
6.3.3 Result Analysis of Process Monitoring and Fault Diagnosis
6.4 Improved Phase-Based SPM for?Unequal-Length Batch Processes
6.4.1 Overview
6.4.2 Data Normalization
6.4.3 Phase Recognition and Division
6.4.4 Sub-PCA Modeling Procedure
6.4.5 Process Monitoring Procedure
6.4.6 Summary
6.5 Application of Improved Phase-Based SPM to Injection Molding
6.5.1 Experimental Setup
6.5.2 Result Analysis of Phase Division and Modeling
6.5.3 Result Analysis of Process Monitoring and Fault Diagnosis
6.5.3.1 Monitoring of a Normal Batch
6.5.3.2 Monitoring of Faulty Batches
References
7 Phase-Based Quality?Improvement Strategies
7.1 Introduction
7.2 Phase-Based Process Analysis and End-Product Quality Prediction (Method?A)
7.2.1 Phase-Based PLS Modeling
7.2.2 Phase-Based Quality-Related Process Analysis
7.2.3 Online Quality Prediction
7.3 Application of Phase PLS Model (Method?A) to Injection Molding
7.3.1 Experimental Setup
7.3.2 Illustration of Phase-Based Process Analysis
7.3.2.1 Phase Division
7.3.2.2 Process Analysis in the Critical-to-Surface Phase
7.3.2.3 Process Analysis in Critical-to-Dimension Phases
7.3.3 Illustration of Phase-Based Quality Prediction
7.4 Phase-Based Process Analysis and End-Product Quality Prediction (Method?B)
7.4.1 Critical Phase Identification
7.4.2 Key Variable Selection Based on Variable-Wise Unfolding
7.4.3 Phase-Based PLS Modeling Algorithm
7.4.4 Online Quality Prediction
7.5 Application of Phase PLS Model (Method?B) to Injection Molding
7.5.1 Illustration of Correlation Analysis
7.5.2 Results of Quality Prediction
References
8 In-Mold Capacitive Transducer for Injection Molding Process
8.1 Fundamentals of Capacitive Transducers
8.2 Dielectric Properties of Polymers
8.3 Principle and Preliminary Tests of Capacitive Transducer in Injection Mold
8.4 Design of In-Mold Capacitive Transducer
8.4.1 Mold Base Design
8.4.2 Mold Insert Design
8.4.3 Capacitance Measurement
8.5 Applications in Melt Flow Detection during Filling Stage
8.5.1 Detection of Filling Start
8.5.2 Detection of V/P Transfer
8.5.3 Detection of melt flow during filling
8.6 Applications for the Packing and Cooling Stages
8.6.1 Guide to Packing Pressure Setting
8.6.2 Detection of Gate Freezing-Off Time
8.6.3 Solidification Rate Monitoring
8.7 Online Part Weight Prediction Using the?Capacitive Transducer
References
9 Profile Setting of?Injection Velocity
9.1 Constant Melt-Front-Velocity Strategy
9.2 Scheme Based on Average-flow-length
9.3 Neural Network Model of?Average-flow-length
9.3.1 Inputs and Output of the Neural Network Model
9.3.2 Architecture of the Neural Network Model
9.3.3 Training Algorithm
9.3.4 Data Collection of Training and Validation Samples
9.3.5 Model Performance
9.4 Profiling Strategy via Optimization
9.5 Parabolic Velocity Profile
9.6 Piece-Wise Ramp Velocity Profile
9.7 Conclusions
References
10 Profile Setting of?Packing Pressure
10.1 Online Autodetection of Gate Freezing-Off Point
10.1.1 Gate Freezing-Off Detection
10.1.2 Development of Autodetection System
10.1.3 Tests of Constant Packing Pressure Cases
10.1.4 Tests of Varying Packing Pressure Profile Cases
10.1.4.1 Online Detection Results of Step Pressure Profile
10.1.4.2 Online Detection Results of Ramp Pressure Profile
10.2 Influence of Packing Profile on?Part?Quality
10.2.1 Constant Packing Profile
10.2.2 Ramp Packing Profile
10.2.3 Step-Change Packing Profile
10.2.4 Summary
10.3 Profiling of Packing Pressure
10.3.1 Profiling Rules
10.3.2 Online Profiling of Constant Packing Pressure
10.3.3 Ramp Profile for Specific Thickness Distribution
10.4 Conclusions
References
11 Parameter Setting for the Plastication Stage
11.1 Visual Barrel System Development
11.2 Plastication Behavior
11.2.1 Melting Behavior
11.2.2 Processing Condition Effects
11.3 Neural Network Modeling of?Melt?Temperature
11.4 Optimal Parameter Setting for?the?Plastication Stage
References
Subject Index
Leere Seite

Chapter 1 INJECTION MOLDING: MACHINE AND PROCESS 1.1 Introduction of general polymer processing 1.2 Polymer material and characteristics 1.3 Injection molding machine 1.4 Injection molding process Chapter 2 EVOLUTION OF SYSTEMS TECHNOLOGIES IN INJECTION MOLDING 2.1 Introduction of systems technology 2.2 Evolution of process control technology in injection molding 2.3 Evolution of process monitoring technology in injection molding 2.4 Evolution of process optimization technology in injection molding Control Chapter 3 FEEDBACK CONTROL ALGORITHMS DEVELOPED FOR CONTINUOUS PROCESSES 3.1 Introduction of feedback control background 3.2 Traditional feedback control: PID 3.3 Adaptive control 3.4 Model predictive control: GPC 3.5 Optimal control 3.6 Fuzzy and Artificial Neural Networks control Chapter 4 LEARNING TYPE CONTROL DEVELOPED FOR REPETITIVE PROCESS 4.1 Introduction of learning type control background 4.2 Basic iterative learning control 4.3 Optimal iterative learning control Chapter 5 TWO-DIMENSIONAL CONTROL ALGORITHMS 5.1 Introduction of two-dimensional control background 5.2 Two-dimensional robust control 5.3 Two-dimensional optimal control 5.4 Two-dimensional model predictive control Chapter 6 FAULT-TOLERANT CONTROL IN INJECTION MOLDING 6.1 Introduction of fault-tolerant control injection molding 6.2 Active fault-tolerant control with sensor failure: an example Monitoring Chapter 7 STATISTICAL PROCESS MONITORING (SPM) 7.1 Process monitoring for continuous processes 7.2 Process monitoring for batch processes Chapter 8 SPM BASED ON PHASE RECOGNITION AND HARD PARTITION STRATEGY 8.1 Data-driven phase recognition and phase partition 8.2 Phase-based SPM in injection molding 8.3 SPM with limited modeling data 8.4 Quality-oriented process monitoring Chapter 9 SPM BASED ON SOFT PHASE PARTITION STRATEGY 9.1 Transition-based soft phase partition 9.2 Soft phase-based SPM in injection molding 9.3 Dissimilarity analysis based process monitoring 9.4 Dissimilarity analysis based nonlinear process monitoring 9.5 Adaptive process monitoring in injection molding Chapter 10 TWO-DIMENSIONAL MODEL-BASED PROCESS MONITORING & FAULT DIAGNOSIS 10.1 Two dimensional dynamic principal component analysis (2D-DPCA) 10.2 2D-DPCA based process monitoring 10.3 2D-DPCA based fault diagnosis Optimization Chapter 11 IN-MOLD CAPACITIVE TRANSDUCER FOR INJECTION MOLDING PROCESS 11.1 Principle of capacitive transducers 11.2 Hardware design of the in-mold capacitive transducer 11.3 Detection of the melt flow in filling stage 11.4 Detection of in-mold status in packing & cooling stage 11.5 Application for online part weight prediction and fault detection Chapter 12 OPTIMAL PROFILING FOR FILLING STAGE 12.1 Introduction 12.2 Constant melt-front-velocity strategy 12.3 Soft-sensor development of average-flow-length 12.4 Uniform filling based on optimization 12.5 Optimal profile of injection velocity for different mold shapes Chapter 13 OPTIMAL PROFILING FOR PACKING STAGE 13.1 Introduction 13.2 Online auto-detection of gate freezing-off point 13.3 Influence of packing profile on part quality 13.4 Profiling of packing pressure Chapter 14 PARAMETER SETTING FOR PLASTICATION STAGE 14.1 Introduction 14.2 Neural network modeling of melt temperature 14.3 Optimal parameter setting Chapter 15 MODEL FREE OPTIMIZATION FOR PART QUALITY CONTROL 15.1 Model free optimization strategy for injection molding process 15.2 Gradient-based algorithm 15.3 Non-gradient-based algorithm 15.4 Part weight control via MFO 15.5 Focal length control via MFO

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Injection Molding Process Control, Monitoring, and Optimization

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