Details
| Original language | English |
|---|---|
| Article number | 100462 |
| Journal | Journal of Industrial Information Integration |
| Volume | 33 |
| Early online date | 3 Apr 2023 |
| Publication status | Published - Jun 2023 |
Abstract
The simulation of production processes using a digital twin can be utilized for prospective planning, analysis of existing systems or process-parallel monitoring. In all cases, the digital twin offers manufacturing companies room for improvement in production and logistics processes leading to cost savings. However, many companies, especially small and medium-sized enterprises, do not apply the technology, because the generation of a digital twin in a built environment is cost-, time- and resource-intensive and IT expertise is required. These obstacles will be overcome by generating a digital twin using a scan of the shop floor and subsequent object recognition. This paper describes the approach with multiple steps, parameters, and data which must be acquired in order to generate a digital twin automatically. It is also shown how the data is processed to generate the digital twin and how object recognition is integrated into it. An overview of the entire process chain is given as well as results in an application case.
Keywords
- Artificial intelligence, Digital factory, Digital twin, Indoor object acquisition, Object recognition, Simulation
ASJC Scopus subject areas
- Engineering(all)
- Industrial and Manufacturing Engineering
- Decision Sciences(all)
- Information Systems and Management
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In: Journal of Industrial Information Integration, Vol. 33, 100462, 06.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Automated generation of digital twin for a built environment using scan and object detection as input for production planning
AU - Sommer, Markus
AU - Stjepandić, Josip
AU - Stobrawa, Sebastian
AU - Soden, Moritz von
N1 - Funding Information: The research project “DigiTwin - Efficient Generation of a Digital Twin in the Manufactoring” is supported by the German Federal Ministry of Education and Research (BMBF) within the Framework Concept ”KMU innovativ”. Authors are responsible for the contents of this publication.
PY - 2023/6
Y1 - 2023/6
N2 - The simulation of production processes using a digital twin can be utilized for prospective planning, analysis of existing systems or process-parallel monitoring. In all cases, the digital twin offers manufacturing companies room for improvement in production and logistics processes leading to cost savings. However, many companies, especially small and medium-sized enterprises, do not apply the technology, because the generation of a digital twin in a built environment is cost-, time- and resource-intensive and IT expertise is required. These obstacles will be overcome by generating a digital twin using a scan of the shop floor and subsequent object recognition. This paper describes the approach with multiple steps, parameters, and data which must be acquired in order to generate a digital twin automatically. It is also shown how the data is processed to generate the digital twin and how object recognition is integrated into it. An overview of the entire process chain is given as well as results in an application case.
AB - The simulation of production processes using a digital twin can be utilized for prospective planning, analysis of existing systems or process-parallel monitoring. In all cases, the digital twin offers manufacturing companies room for improvement in production and logistics processes leading to cost savings. However, many companies, especially small and medium-sized enterprises, do not apply the technology, because the generation of a digital twin in a built environment is cost-, time- and resource-intensive and IT expertise is required. These obstacles will be overcome by generating a digital twin using a scan of the shop floor and subsequent object recognition. This paper describes the approach with multiple steps, parameters, and data which must be acquired in order to generate a digital twin automatically. It is also shown how the data is processed to generate the digital twin and how object recognition is integrated into it. An overview of the entire process chain is given as well as results in an application case.
KW - Artificial intelligence
KW - Digital factory
KW - Digital twin
KW - Indoor object acquisition
KW - Object recognition
KW - Simulation
UR - http://www.scopus.com/inward/record.url?scp=85151636435&partnerID=8YFLogxK
U2 - 10.1016/j.jii.2023.100462
DO - 10.1016/j.jii.2023.100462
M3 - Article
AN - SCOPUS:85151636435
VL - 33
JO - Journal of Industrial Information Integration
JF - Journal of Industrial Information Integration
SN - 2452-414X
M1 - 100462
ER -