2024
Supyen, Kritkorn; Mathur, Abhishek; Boroukhian, Tina; Wicaksono, Hendro
Streamlining Manufacturing Resource Digitization for Digital Twins Through Ontologies and Object Detection Techniques Proceedings Article
In: International Conference on Dynamics in Logistics, pp. 419–430, Springer Nature Switzerland Cham 2024.
Abstract | Links | BibTeX | Tags: computer vision, digital twins, machine learning, ontologies, semantic web
@inproceedings{supyen2024streamlining,
title = {Streamlining Manufacturing Resource Digitization for Digital Twins Through Ontologies and Object Detection Techniques},
author = {Kritkorn Supyen and Abhishek Mathur and Tina Boroukhian and Hendro Wicaksono},
url = {https://link.springer.com/chapter/10.1007/978-3-031-56826-8_32},
doi = {https://doi.org/10.1007/978-3-031-56826-8_32},
year = {2024},
date = {2024-04-03},
urldate = {2024-04-03},
booktitle = {International Conference on Dynamics in Logistics},
pages = {419–430},
organization = {Springer Nature Switzerland Cham},
abstract = {Digital twins play an essential role in manufacturing companies to adopt Industry 4.0. However, their uptake has been lagging, especially in European manufacturing firms. This can be attributed to the absence of automated methods for digitizing physical manufacturing resources and creating digital representations accessible and processable by both humans and computers. Our research addresses this challenge by automating the digitization of manufacturing resources captured on the shop floor. We employ object detection techniques on a set of images and align the results with an ontology that standardizes the semantic description of digital representations. This research aims to accelerate digital transformation for manufacturing companies, providing digital representations to their physical resources. The ontology-based digital representation fosters interoperability among diverse equipment and machines from various vendors. It enables the automated deployment of digital twins, improving the efficiency of planning and control of manufacturing systems.
},
keywords = {computer vision, digital twins, machine learning, ontologies, semantic web},
pubstate = {published},
tppubtype = {inproceedings}
}
Digital twins play an essential role in manufacturing companies to adopt Industry 4.0. However, their uptake has been lagging, especially in European manufacturing firms. This can be attributed to the absence of automated methods for digitizing physical manufacturing resources and creating digital representations accessible and processable by both humans and computers. Our research addresses this challenge by automating the digitization of manufacturing resources captured on the shop floor. We employ object detection techniques on a set of images and align the results with an ontology that standardizes the semantic description of digital representations. This research aims to accelerate digital transformation for manufacturing companies, providing digital representations to their physical resources. The ontology-based digital representation fosters interoperability among diverse equipment and machines from various vendors. It enables the automated deployment of digital twins, improving the efficiency of planning and control of manufacturing systems.