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Marcus Francisco

dblp:339/6542 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0001-6002-4491ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 50% Smart cities and intelligent transportation · 50%
Computer networks
1 paper
Internet of things and sensor networks · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Smart cities and intelligent transportation
digital twin
0.612022
A Campus Prototype of Interactive Digital Twin in Cyber Manufacturing · SenSys 2022
Computational science and engineering › manufacturing automation › manufacturing
smart manufacturing
0.612022
A Campus Prototype of Interactive Digital Twin in Cyber Manufacturing · SenSys 2022
Internet of things and sensor networks
wireless sensor network
0.612022
A Campus Prototype of Interactive Digital Twin in Cyber Manufacturing · SenSys 2022

Methods — techniques the papers use, named apart from their topics

web interface · 1.1arduino · 1.1ESP32 · 1.1
YearPublicationVenuePosition
2022 A Campus Prototype of Interactive Digital Twin in Cyber Manufacturing
abstract
Smart manufacturing and Industry 4.0 are bringing disruptive changes to the manufacturing sector. Smart manufacturing increases productivity, creates safer conditions for workers, and simplifies product customization, all while decreasing business expenses [2]. To this end, we have created a flexible three-component architecture for remote machine management, using it to build a digital twin prototype of a Creality Ender-3 Pro 3D printer located on the University at Buffalo campus. This twin provides users with the ability to monitor and control the machine from anywhere in the world through a web interface. Our system improves upon existing technologies, such as Octoprint [1], through the addition of twin views. It also relies upon cheaper components, using the Arduino and ESP32 rather than the Raspberry Pi. Finally, existing technologies tend to focus on one specific type of machine. In contrast, our framework is flexible, capable of supporting many different machines.
Matthew Rubino, Michelle Weng, Shardul Saptarshi, Marcus Francisco, Alex Francisco, Chi Zhou 0004, Hongyue Sun, Wenyao Xu
SenSys5