TY - JOUR
T1 - Data Acquisition Framework for spatio-temporal analysis of path-based welding applications
AU - Safronov, Georgij
AU - Theisinger, Heiko
AU - Sahlbach, Vasco
AU - Braun, Christoph
AU - Molzer, Andreas
AU - Thies, Anabelle
AU - Schuba, Christian
AU - Shirazi, Majid
AU - Reindl, Thomas
AU - Hänel, Albrecht
AU - Engelhardt, Philipp
AU - Ihlenfeldt, Steffen
AU - Mayr, Peter
N1 - Publisher Copyright:
© 2024 The Authors.
PY - 2024
Y1 - 2024
N2 - The use of digital technologies in industrial manufacturing reduces operational costs and improves production quality. The “Framework for Spatiotemporal Production Data Acquisition” (PathSense) aims to improve access and usability of production data by applying a common data ecosystem. Through integrating operational technology (OT) and information technology (IT), PathSense supports decision-making and process optimization. The framework uses the concept of digital shadows to collect critical information from sensors within the continuous manufacturing processes, e.g., welding or gluing lines. Spatial and temporal digital shadows are developed in accordance with human spatial cognition to support sophisticated-yet intuitive-human-computer interactions without the need for advanced data science skills. The backbone of the framework consists of robust data pipelines with a setup incorporating modern IT protocols, such as OPC UA and MQTT, to support the efficient acquisition and management of data. The paper addresses the challenges associated with the combination of IT and OT in cyber-physical systems, stressing modern complex data-intensive manufacturing as one exemplary domain to be tackled by scalable and secure data architectures. The paper identifies two major future directions: refining data integration processes and embedding advanced machine learning algorithms to enable automated data analysis and improve process quality monitoring. In summary, PathSense proposes a data-driven approach for quality inspection in manufacturing, which may eventually enhance industry practices and move towards data-driven decisions and increased operational flexibility.
AB - The use of digital technologies in industrial manufacturing reduces operational costs and improves production quality. The “Framework for Spatiotemporal Production Data Acquisition” (PathSense) aims to improve access and usability of production data by applying a common data ecosystem. Through integrating operational technology (OT) and information technology (IT), PathSense supports decision-making and process optimization. The framework uses the concept of digital shadows to collect critical information from sensors within the continuous manufacturing processes, e.g., welding or gluing lines. Spatial and temporal digital shadows are developed in accordance with human spatial cognition to support sophisticated-yet intuitive-human-computer interactions without the need for advanced data science skills. The backbone of the framework consists of robust data pipelines with a setup incorporating modern IT protocols, such as OPC UA and MQTT, to support the efficient acquisition and management of data. The paper addresses the challenges associated with the combination of IT and OT in cyber-physical systems, stressing modern complex data-intensive manufacturing as one exemplary domain to be tackled by scalable and secure data architectures. The paper identifies two major future directions: refining data integration processes and embedding advanced machine learning algorithms to enable automated data analysis and improve process quality monitoring. In summary, PathSense proposes a data-driven approach for quality inspection in manufacturing, which may eventually enhance industry practices and move towards data-driven decisions and increased operational flexibility.
KW - AI
KW - data acquisition
KW - defect detection
KW - digital twin
KW - MQTT
KW - OPC UA
KW - PathSense
KW - spatiotemporal analysis
KW - welding process
UR - http://www.scopus.com/inward/record.url?scp=85213039884&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2024.10.295
DO - 10.1016/j.procir.2024.10.295
M3 - Conference article
AN - SCOPUS:85213039884
SN - 2405-8971
VL - 58
SP - 1644
EP - 1652
JO - IFAC Proceedings Volumes (IFAC-PapersOnline)
JF - IFAC Proceedings Volumes (IFAC-PapersOnline)
IS - 27
T2 - 18th IFAC Workshop on Time Delay Systems, TDS 2024
Y2 - 2 October 2023 through 5 October 2023
ER -