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Intelligent e-monitoring of plastic injection molding machines.

Lau Hau Yu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 79-83). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.iv / Table of Contents --- p.vi / Chapter Chapter 1: --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Objective --- p.4 / Chapter Chapter 2: --- Literature Survey --- p.6 / Chapter 2.1 --- Plastic Injection Molding Process --- p.6 / Chapter 2.2 --- Monitoring and Diagnosis Methods --- p.10 / Chapter 2.3 --- Remote Monitoring --- p.12 / Chapter Chapter 3: --- Monitoring Methods --- p.15 / Chapter 3.1 --- Predict nozzle pressure and part weight using the Radial Basis Function Neural Network --- p.15 / Chapter 3.1.1 --- Motivation --- p.15 / Chapter 3.1.2 --- Background --- p.15 / Chapter 3.1.3 --- Hybrid RBF neural network --- p.17 / Chapter 3.1.4 --- Estimation of nozzle pressure --- p.21 / Chapter 3.1.5 --- Estimation of part weight: The two steps and one step methods --- p.22 / Chapter 3.2 --- Short shot Monitoring using Similarity --- p.25 / Chapter 3.2.1 --- Background --- p.25 / Chapter 3.2.2 --- The Dissimilarity Approach --- p.26 / Chapter 3.3 --- Parameter Resetting using Support Vector Machine (SVM) and Virtual Search Method (VSM) --- p.27 / Chapter 3.3.1 --- Background --- p.27 / Chapter 3.3.2 --- Support Vector Regression --- p.27 / Chapter 3.3.3 --- SVM Parameters Resetting using Virtual Search Method (VSM) --- p.31 / Chapter 3.4 --- Experiments and Results --- p.33 / Chapter 3.4.1 --- Introduction to Design of Experiment (DOE) --- p.33 / Chapter 3.4.2 --- Set-points selection based on Design of Experiment (DOE) --- p.34 / Chapter 3.4.3 --- Nozzle pressure estimation --- p.40 / Chapter 3.4.4 --- Part weight prediction using the One Step Method --- p.47 / Chapter 3.4.5 --- Similarity Monitoring using estimated nozzle pressure --- p.49 / Chapter 3.4.6 --- Similarity Monitoring using ram position --- p.54 / Chapter 3.4.7 --- Parameter Resetting using SVM and VSM --- p.61 / Chapter Chapter 4: --- The Remote Monitoring and Diagnosis System (RMDS) --- p.63 / Chapter 4.1 --- Introduction to the Remote Monitoring and Diagnosis System --- p.63 / Chapter 4.2 --- Starting Use of the Software --- p.65 / Chapter 4.3 --- Properties and Channel Settings --- p.66 / Chapter 4.3.1 --- Statistic Process Control (SPC) --- p.69 / Chapter 4.3.2 --- Settings --- p.71 / Chapter 4.3.3 --- Viewing the signals --- p.72 / Chapter 4.3.4 --- Short shot monitoring --- p.73 / Chapter 4.3.5 --- Data management --- p.73 / Chapter Chapter 5: --- Coeclusions and Future Works --- p.76 / References --- p.79 / Appendix A: Machine settings in the experiment --- p.84 / Appendix B: Measured part weight in the part weight prediction experiment --- p.86 / Appendix C: Measured part weight in the similarity monitoring experiment --- p.87 / Appendix D: Results of Parameters Resetting Experiment --- p.88 / Appendix E: List of figures --- p.89 / Appendix F: List of tables --- p.91

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_324866
Date January 2004
ContributorsLau, Hau Yu., Chinese University of Hong Kong Graduate School. Division of Automation and Computer-Aided Engineering.
Source SetsThe Chinese University of Hong Kong
LanguageEnglish, Chinese
Detected LanguageEnglish
TypeText, bibliography
Formatprint, vii, 91 leaves : ill. ; 30 cm.
RightsUse of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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