Experimental Modeling of Twin-Screw Extrusion Processes to Predict Properties of Extruded Composites

Twin-screw extrusion is used to compound fillers into a polymer matrix in order to improve the properties of the final product. The resultant properties of the composite are determined by the operating conditions used during extrusion processing. Changes in the operating conditions affect the physics of the melt flow, inducing unique composite properties. In the following work, the Residence Stress Distribution methodology has been applied to model both the stress behavior and the property response of a twin-screw compounding process as a function of the operating conditions. The compounding of a pigment into a polymer melt has been investigated to determine the effect of stress on the degree of mixing, which will affect the properties of the composite. In addition, the pharmaceutical properties resulting from the compounding of an active pharmaceutical ingredient are modeled as a function of the operating conditions, indicating the physical behavior inducing the property responses.

Structure dependencies for multiscale polymer composites using extrusion processes

Multiscale reinforcement, using carbon microfibers and multi-walled carbon nanotubes, of polymer matrix composites manufactured by twin-screw extrusion is investigated for enhanced mechanical and thermal properties with an emphasis on the use of a diverging flow in the die for fluid mechanical fiber manipulation. Using fillers at different length scales (microscale and nanoscale), synergistic combinations have been identified to produce distinct mechanical and thermal behavior. Fiber manipulation has been demonstrated experimentally and computationally, and has been shown to enhance thermal conductivity significantly. Finally, a new physics driven predictive model for thermal conductivity has been developed based on fiber orientation during flow, which is shown to successfully capture composite thermal conductivity.

Quantitative and Qualitative Trade-Off Analysis of Drowsy Driver Detection Methods: Single Electrode Wearable EEG Device, Multi-Electrode Wearable EEG Device, and Head-Mounted Gyroscope

Drowsy driving impairs motorists’ ability to operate vehicles safely, endangering both the drivers and other people on the road. The purpose of the project is to find the most effective wearable device to detect drowsiness. Existing research has demonstrated several options for drowsiness detection, such as electroencephalogram (EEG) brain wave measurement, eye tracking, head motions, and lane deviations. However, there are no detailed trade-off analyses for the cost, accuracy, detection time, and ergonomics of these methods. We chose to use two different EEG headsets: NeuroSky Mindwave Mobile (single-electrode) and Emotiv EPOC (14- electrode). We also tested a camera and gyroscope-accelerometer device. We can successfully determine drowsiness after five minutes of training using both single and multi-electrode EEGs. Devices were evaluated using the following criteria: time needed to achieve accurate reading, accuracy of prediction, rate of false positives vs. false negatives, and ergonomics and portability. This research will help improve detection devices, and reduce the number of future accidents due to drowsy driving.