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Full-Text Articles in Manufacturing
The Rheological Behavior Of Carbon Nanofiber-Reinforced Thermoplastic Composites, Qiang Zeng
The Rheological Behavior Of Carbon Nanofiber-Reinforced Thermoplastic Composites, Qiang Zeng
Theses and Dissertations - UTB/UTPA
A rheological study of the thermoplastic HDPE filled with vapor grow carbon nanofiber (VGCNF) has been performed with a RheoStress 150 Tester. VGCNF having average 50 nm diameter were used. These nanofibers were purified to provide for an open network of high purity nanofibers. A rheomixer was used to disperse the nanofibers uniformly in the polyethylene matrix because of shear working conditions. The influence of fiber loading, frequency and temperature on the rheology was investigated. Studies were carried out in the temperature range of 160 to 210°C and frequency range of 0.062 to 628.3 rad/s. The rheological measurements show that …
Automated Robotic Inspection System For Electronic Manufacturing, Roberto Alejandro Balderas
Automated Robotic Inspection System For Electronic Manufacturing, Roberto Alejandro Balderas
Theses and Dissertations - UTB/UTPA
An automated robotic inspection system for electronic manufacturing has been developed to identify pin defects of IC packages mounted on printed circuit boards using surface mount technology. The automated robotic inspection system consists of two robots, a computer, a CCD camera with frame gabber for image acquisition, and a customized windows program using neural network for on-line defect identification. Gray scale images of the pins on IC packages are acquired using ambient light. The images are filtered and formatted to appropriate size, so that Matlab neural network tool could be used. The images are used to train neural networks using …
A Neural Network-Based Vision System For Automated Separation Of Onion From Clod Using Mechanical Harvester, Demian Morquin
A Neural Network-Based Vision System For Automated Separation Of Onion From Clod Using Mechanical Harvester, Demian Morquin
Theses and Dissertations - UTB/UTPA
A neural network classifier for separating clods from onions during harvesting has been developed. The separator consists of a multi-layer feedforward network that maps textural features computed from gray-scale images of onions and clods into the correct object. Texture features were computed from co-occurrence matrices that specify the spatial relationship of pixel values in an image. The textural features selected for this application consist of homogeneity, contrast, variance, and energy. The network was trained using the back-propagation algorithm. Based on the textural features classification, the effect of changing the network configuration on separation effectiveness was investigated. Factors including network topology …