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Injection molding is the most versatile, flexible, and dynamic plastics production operation. It has been used to manufacture products from practically all thermoplastic polymers, blends, composites, and nanocomposites. A critical aspect for the success of the process depends on understanding and control of the various steps of the injection molding process and the thermo-mechanical history experienced by the polymer throughout the process. Moreover, it is important to understand the impact of this history on the characteristics of the final product.<br />As many of the above interactions and concepts are complex, it is very important to develop a monitoring strategy that permits the identification of the status and responses of the critical process variables. Overall, a successful injection molding process must be coupled to a successful process monitoring, optimization, and control strategy.<br />Injection Molding: Process Control, Monitoring, and Optimization represents a major contribution to the science and practice of injection molding by satisfying the critical needs of injection molding machine manufacturers, mold and product designers, and molders. Moreover, the book will be helpful to researchers and teachers in the fields of injection molding and process control.<br />Contents:<br />Injection Molding: Background<br />Feedback Control Algorithms Developed for Continuous Processes<br />Learning Type Control for the Injection Molding Process<br />Two-Dimensional Control Algorithms<br />Statistical Process Monitoring of Injection Molding: Basics<br />Phase-Based SPM Strategies<br />Phase-Based Quality Improvement Strategies<br />In-Mold Capacitive Transducer for Injection Molding Process<br />Profile Setting of Injection Velocity<br />Profile Setting of Packing Pressure<br />Parameter Setting for the Plastication Stage

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

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