Cloud-Based Digital Twin for Industrial Robotics

Author(s):  
Timon Hoebert ◽  
Wilfried Lepuschitz ◽  
Erhard List ◽  
Munir Merdan
Author(s):  
Igor M. Verner ◽  
Dan Cuperman ◽  
Sergei Gamer ◽  
Alex Polishuk

<span style="font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;">This study explores an opportunity to engage first-year engineering students in practice with a modern industrial robot Baxter and provides training in spatial skills. We developed a laboratory exercise in which the students operate the robot to perform spatial manipulations of objects. We implemented the exercise on a digital twin of Baxter in the Gazebo virtual environment. The digital twin was calibrated to mimic the physical properties of the Baxter and correctly simulate its spatial manipulations with oriented cubes. The exercise was delivered to a class of 25 students as part of the robotics workshop in the Introduction to Industrial Engineering course. We administered a post-workshop questionnaire with focus on the analysis of the learning outcomes and students' spatial difficulties. The students noted that the workshop and particularly the exercise effectively exposed them to industrial robotics and raised their spatial awareness in robot operation.</span>


Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1006-P
Author(s):  
BENYAMIN GROSMAN ◽  
ANIRBAN ROY ◽  
DI WU ◽  
NEHA PARIKH ◽  
LOUIS J. LINTEREUR ◽  
...  

2019 ◽  
Vol 2019 (1) ◽  
pp. 27-30 ◽  
Author(s):  
Andreas Deuter ◽  
◽  
Florian Pethig ◽  
Keyword(s):  

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice


2020 ◽  
Author(s):  
Dedy Ariansyaha ◽  
Iñigo Fernàndez del Amo ◽  
John Ahmet Erkoyuncu ◽  
Merwan Agha ◽  
Dominik Bulka ◽  
...  
Keyword(s):  

2020 ◽  
Author(s):  
Ali Al-Yacoubb ◽  
Will Eaton ◽  
Melanie Zimmer ◽  
Achim Buerkle ◽  
Dedy Ariansyaha ◽  
...  

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