Digital Twin News
Digital Twin Resources
November 3, 2017
Digital twins—virtual representations of real-world products, equipment, and processes—are considered a critical component of the Industry 4.0 transformation. They’re brought to life with a mix of simulation technologies, embedded sensors, and data analytics.
In this LIVE online roundtable moderated by DE’s Senior Editor Kenneth Wong, industry experts discuss
- How digital twins can be visualized in AR-VR hardware;
- How to develop and implement sensor strategies to maintain digital twins;
- How to make good use of the data collected from the field.
To attend, register HERE.
About the Digital Twin Panelists
Alexis Macklin, Analyst, Greenlight Insights
Alexis Macklin is a leading new voice on emerging technology trends. She brings her diverse perspectives and keen insights to the research of growing use of virtual reality (VR) and augmented reality (AR) technologies. A sought after speaker, Macklin has delivered presentations to Digital Hollywood and Upload Collective. Recently, she delivered a briefing on the growth of the VR industry at Game Developer Conference (GDC) 2017. She is the author of “Preparing for an Augmented World,” a subscriber-accessible publication from Greenlight Insights. Macklin is a graduate of Arizona State University.
Michael Fry, Director, Manufacturing Systems Engineering Consulting Practice, CIMdata
Michael Fry is the Manufacturing Systems Engineering Practice Director, for CIMdata. Fry has broad industry experience servicing clients from aerospace & defense, shipbuilding, automotive, and electronics industries. Prior to his consulting career, he was an aerospace design engineer with Beech Aircraft and Martin Marietta. He has recently worked at IBM in the predictive analytics discipline working on projects implementing the Internet of Things (IoT) and Industry 4.0 practices to provide data collection for cognitive computing and decision-making. This work was applied to machine tool operations, drone imaging and video capture, as well as jet engine failure prediction.