Rheological Modeling for Production of High Quality Polymeric
The fundamental defect inherent to the thermoforming technology is wall-thickness variation of the products due to inadequate thermal processing during production of polymer. A nonlinear viscoelastic rheological model is implemented for developing the process model. This model describes deformation process of a sheet in thermoforming process. Because of relaxation pause after plug-assist stage and also implementation of two stage thermoforming process have minor wall-thickness variation and consequently better mechanical properties of polymeric articles. For model validation, a comparative analysis of the theoretical and experimental data is presented. Keywords—High-quality polymeric article, Thermal Processing, Rheological model, Minor wall-thickness variation.
Rheological modeling of plug‐assist thermoforming
Solving problems for thermoforming processes in the production of axisymmetric thin walled plastics is investigated in this research work. A nonlinear viscoelastic rheological model with a new strain energy function is suggested for improvement of physical properties of final product. For model validation, a quantitative relation between stress and technical parameters of plug-assist thermoforming is determined by comparison of theoretical and experimental results. This process with the proposed rheological model could be suggested for prevention from some technical defects such as wall thickness variations, physical instability during inflation-shrinkage, and warpage exhibited in the final part of a polymeric sheet thermoforming. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 4148–4152, 2006
Early Stage Diagnosis of Parkinson’s Disease Using HOS Features of EEG Signals
Parkinson’s disease (PD) is a common neurodegenerative disease that causes involuntary muscle movements and tremor among other symptoms. One approach to diagnosing this disease is by analyzing the electroencephalography (EEG) signals of the patients. However, due to the complexity of this type of signal, more advanced feature extraction and classification methods are required. The goal of this study is to combine six well-known features in EEG analysis with eight higher order statistical features and use them for classification of early stage PD (1st and 2nd stage) from a healthy control group. After extracting the required features, eight classifiers are employed to classify the signals.