Session D: 3:30PM – 5PM
Engineering. Session D – Oral Presentations, Ballroom, Henriksen, Alumni House
SESSION D (3:20-5:00PM)
Location: Henriksen Room, Alumni House
Post-Aggregation Data Visualization
Todd Lainhart, Brigham Young University
Faculty Mentor Yuri Hovanski, Brigham Young University
SESSION D 3:30-3:45PM
Henriksen Room (1st floor), Alumni House
Engineering
Industry 4.0 is the current initiative being implemented in manufacturing lines. Part of the initiative is to connect the different systems and protocols of the factory together into a single connection that can communicate with all of them. This connection allows for data from various machines and systems to be stored together. This communication includes the aggregation, visualization, and implementation of data. Only through visualization and implementation does data become valuable. As manufacturing lines collect data, the volume of it can quickly become hard to decipher and collate by hand. Thus, a system of visualization is needed to enable workers and managers to gain value from the data stored. The research describes a possible solution and use for collected data through visualization and implementation. This study is done using PTC Inc. products, specifically through an established ThingWorx server with Kepware connections to Allen-Bradley and Siemens PLCs. Kepware is a system-connection software that translates different machine protocols and sends the collected data to ThingWorx, a web-based platform that aggregates the information sent to it. The data was visualized on a single platform called a dashboard in various formats such as tables, graphs, current value displays, and state-based displays. As there are several different formats of data continuously being collected, different implementations included a table that kept track of information from current and previous orders, a pareto chart that keeps track of defect type counts, and graphs that depict uptime and throughput. Because these visualizations are all contained within the same platform, a user can quickly use each capability without needing to move between screens or locations. This allows for greater efficiency and a faster response to errors and problems.
Neuromorphic Computing with Randomly Assembled Nanowire Networks
David Reinhardt, Brigham Young University
Faculty Mentor David Wingate, Brigham Young University
SESSION D 3:50-4:05PM
Henriksen Room (1st floor), Alumni House
Engineering
Neuromorphic computing utilizes analog electrical circuits that mimic biologic neural networks. This form of computing shows promise of outperforming classical computers at a fraction of the power draw. Recently, metallic nanowire networks have been investigated as potential neuromorphic computing devices, often composed of polymer-coated silver nanowires deposited onto a flat surface. Due to their 2D geometry, these nanowire networks are unable to accurately reflect the complexity of 3D biological neural networks. 3D nano