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

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING / Includes bibliographical references. / by Francis Abbott Waldman. / M.S.

Dissertation (Ph. D.)--University of Akron, Dept. of Polymer Engineering, 2009. / "May, 2009." Title from electronic dissertation title page (viewed 11/27/2009) Advisor, Avraam I. Isayev; Committee members, James L. White, Erol Sancaktar, Kevin Kreider, Minel J. Braun; Department Chair, Sadhan C. Jana; Dean of the College, Stephen Cheng; Dean of the Graduate School, George R. Newkome. Includes bibliographical references.

published_or_final_version / Mechanical Engineering / Master / Master of Philosophy

The thermomechanical history experienced by plastics melts and solids during processing leads to the development of microstructure, which directly influences the ultimate properties of the plastic articles. Therefore, besides the predictions of thermomechanical history, the prediction of microstructure represents an important aspect of the modeling of plastics processing. / A comprehensive model has been developed to describe the behaviour of thermoplastic polymer melts during the injection molding process. The model, which deals with the three stages of the process (filling, packing and cooling), employs a viscoelastic constitutive equation and incorporates crystallization kinetics. All the properties of the material employed are based on experimental data. The model predicts fill times, velocity, temperature and pressure distributions, the distribution of shear and normal stresses, and stress relaxation. The predictions of the model are compared to experimental data relating to pressure distributions and variation with time, fill times, and the distributions of crystallinity, orientation and tensile modulus.

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