2025
Boroukhian, Tina; Supyen, Kritkorn; Samson, Jhealyn Bautista; Bashyal, Atit; Wicaksono, Hendro
Integrating 3D object detection with ontologies for accurate digital twin creation in manufacturing systems Journal Article
In: The International Journal of Advanced Manufacturing Technology, vol. 140, no. 9, pp. 4679–4711, 2025.
Abstract | Links | BibTeX | Tags: data science, deep learning, digital twins, image processing, industry 4.0, machine learning, manufacturing, object detection, ontologies, semantic web
@article{boroukhian2025integrating,
title = {Integrating 3D object detection with ontologies for accurate digital twin creation in manufacturing systems},
author = {Tina Boroukhian and Kritkorn Supyen and Jhealyn Bautista Samson and Atit Bashyal and Hendro Wicaksono},
doi = {https://doi.org/10.1007/s00170-025-16548-x},
year = {2025},
date = {2025-10-01},
urldate = {2025-10-01},
journal = {The International Journal of Advanced Manufacturing Technology},
volume = {140},
number = {9},
pages = {4679–4711},
publisher = {Springer London},
abstract = {The digitization of manufacturing resources through digital twins (DTs) enhances operational efficiency and resource management. Ontologies play a key role in maintaining semantic consistency within DT systems. However, existing ontology-based approaches face challenges, including limited adaptability, integration of heterogeneous data—such as 3D images—and high manual effort in ontology development. These limitations hinder the scalability of DT implementations. Traditional 2D imaging often lacks spatial accuracy in complex manufacturing environments, causing inefficiencies and higher costs. Integrating richer data with intelligent frameworks is crucial for improving production and adaptability. The proposed study addresses these challenges by introducing a methodology that integrates existing ontologies with advanced 3D object detection models. The proposed approach employs two fully automated pipelines: one for detecting manufacturing resources from 3D images and another for mapping them into ontologies, ensuring seamless integration into DT frameworks. By leveraging established ontologies, the methodology enhances interoperability, reduces implementation complexity, and facilitates scalable deployment of DT systems across various industrial applications. Additionally, a comparative analysis of multiple advanced 3D detection models provides valuable insights to guide the selection of optimal solutions for diverse industrial settings. Experimental results show that YOLOv8 achieved the highest performance, with 91% classification accuracy, 86% precision, 81% recall, and the fastest inference time of 0.66 s. For ontology population, four machine labels—Robot, MillingMachine, BandSaw, and Lathe—were successfully integrated using a semantic similarity-based mapping strategy, enabling automated class creation and merging. This innovative framework sets a new benchmark for DT implementations, offering enhanced accuracy, efficiency, and semantic coherence in modern manufacturing.},
keywords = {data science, deep learning, digital twins, image processing, industry 4.0, machine learning, manufacturing, object detection, ontologies, semantic web},
pubstate = {published},
tppubtype = {article}
}
The digitization of manufacturing resources through digital twins (DTs) enhances operational efficiency and resource management. Ontologies play a key role in maintaining semantic consistency within DT systems. However, existing ontology-based approaches face challenges, including limited adaptability, integration of heterogeneous data—such as 3D images—and high manual effort in ontology development. These limitations hinder the scalability of DT implementations. Traditional 2D imaging often lacks spatial accuracy in complex manufacturing environments, causing inefficiencies and higher costs. Integrating richer data with intelligent frameworks is crucial for improving production and adaptability. The proposed study addresses these challenges by introducing a methodology that integrates existing ontologies with advanced 3D object detection models. The proposed approach employs two fully automated pipelines: one for detecting manufacturing resources from 3D images and another for mapping them into ontologies, ensuring seamless integration into DT frameworks. By leveraging established ontologies, the methodology enhances interoperability, reduces implementation complexity, and facilitates scalable deployment of DT systems across various industrial applications. Additionally, a comparative analysis of multiple advanced 3D detection models provides valuable insights to guide the selection of optimal solutions for diverse industrial settings. Experimental results show that YOLOv8 achieved the highest performance, with 91% classification accuracy, 86% precision, 81% recall, and the fastest inference time of 0.66 s. For ontology population, four machine labels—Robot, MillingMachine, BandSaw, and Lathe—were successfully integrated using a semantic similarity-based mapping strategy, enabling automated class creation and merging. This innovative framework sets a new benchmark for DT implementations, offering enhanced accuracy, efficiency, and semantic coherence in modern manufacturing.
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.
Habtemichael, Noah; Wicaksono, Hendro; Valilai, Omid Fatahi
NFT based Digital Twins for Tracing Value Added Creation in Manufacturing Supply Chains Journal Article
In: Procedia Computer Science, vol. 232, pp. 2841–2846, 2024.
Abstract | Links | BibTeX | Tags: blockchain, digital twins, manufacturing, supply chain management
@article{habtemichael2024nft,
title = {NFT based Digital Twins for Tracing Value Added Creation in Manufacturing Supply Chains},
author = {Noah Habtemichael and Hendro Wicaksono and Omid Fatahi Valilai},
url = {https://www.sciencedirect.com/science/article/pii/S1877050924002771},
doi = {https://doi.org/10.1016/j.mex.2024.102868},
year = {2024},
date = {2024-03-20},
urldate = {2024-01-01},
journal = {Procedia Computer Science},
volume = {232},
pages = {2841–2846},
publisher = {Elsevier},
abstract = {The globalization of products and markets increases the distance between the origin of products and consumers. This leads to a condition where customers don't have information about the origins of their products. Thus, traceability has become an essential sub-system of manufacturing supply chain management. However, due to globalization and complexity of supply chain interactions among the suppliers and manufacturing enterprises, it is hard to pinpoint the exact contributions of different actors in a supply chain. Integrated supply network structure with suitable visibility and usage of real time data transfer is another area of great importance. This paper focuses on how NFT (Non-Fungible Token) coupled with smart contracts could utilize blockchain to make it easier to track the products in a supply chain. Explaining how NFT's could help in tracking the contributions of different stakeholders in a supply chain by tracking the product throughout the entire process of sourcing, production, and sale by using a digital twin. In a manufacturing supply chain enabled by NFT Technology, whenever raw materials are transferred and processed through the supply chain, an NFT would be attached to its digital twin which will capture the created values. Each NFT can easily and uniquely be known by its data stored. Data would be updated based on real time information and will enable the stakeholders to trace the product information about how much each company has contributed to the produced products. The data stored in the form of a smart contract in the blockchain prevents the data being entered from being destroyed, eliminated, or changed without permission. Thus, there is a secure data flow among different stakeholders.
},
keywords = {blockchain, digital twins, manufacturing, supply chain management},
pubstate = {published},
tppubtype = {article}
}
The globalization of products and markets increases the distance between the origin of products and consumers. This leads to a condition where customers don't have information about the origins of their products. Thus, traceability has become an essential sub-system of manufacturing supply chain management. However, due to globalization and complexity of supply chain interactions among the suppliers and manufacturing enterprises, it is hard to pinpoint the exact contributions of different actors in a supply chain. Integrated supply network structure with suitable visibility and usage of real time data transfer is another area of great importance. This paper focuses on how NFT (Non-Fungible Token) coupled with smart contracts could utilize blockchain to make it easier to track the products in a supply chain. Explaining how NFT's could help in tracking the contributions of different stakeholders in a supply chain by tracking the product throughout the entire process of sourcing, production, and sale by using a digital twin. In a manufacturing supply chain enabled by NFT Technology, whenever raw materials are transferred and processed through the supply chain, an NFT would be attached to its digital twin which will capture the created values. Each NFT can easily and uniquely be known by its data stored. Data would be updated based on real time information and will enable the stakeholders to trace the product information about how much each company has contributed to the produced products. The data stored in the form of a smart contract in the blockchain prevents the data being entered from being destroyed, eliminated, or changed without permission. Thus, there is a secure data flow among different stakeholders.
2023
Wicaksono, Hendro; Nisa, Mehr Un; Vijaya, Annas
In: 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 0528–0532, IEEE 2023.
Abstract | Links | BibTeX | Tags: digital twins, explainable AI, interoperability, ontologies, semantic web, transportation
@inproceedings{wicaksono2023towards,
title = {Towards Intelligent and Trustable Digital Twin Asset Management Platform for Transportation Infrastructure Management Using Knowledge Graph and Explainable Artificial Intelligence (XAI)},
author = {Hendro Wicaksono and Mehr Un Nisa and Annas Vijaya},
doi = {https://doi.org/10.1109/IEEM58616.2023.10406401},
year = {2023},
date = {2023-12-18},
urldate = {2023-01-01},
booktitle = {2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)},
pages = {0528–0532},
organization = {IEEE},
abstract = {In the transportation sector, implementing digital twins is part of the digitization measure to improve resource efficiency in infrastructure management. However, the use of digital twins is still limited due to challenges such as a lack of shared understanding of digital twin models, complex model integration, security issues, lack of access to essential data, and high costs due to inefficient business models. This research develops an asset management platform suitable for Small and Medium Enterprises (SMEs) for the cross-company, secure, and intuitive collaborative management of digital twin assets. It can be achieved by developing an ontology-based semantic model of the assets, explainable machine learning (XAI), and a scenario-based intelligent search and discovery mechanism.},
keywords = {digital twins, explainable AI, interoperability, ontologies, semantic web, transportation},
pubstate = {published},
tppubtype = {inproceedings}
}
In the transportation sector, implementing digital twins is part of the digitization measure to improve resource efficiency in infrastructure management. However, the use of digital twins is still limited due to challenges such as a lack of shared understanding of digital twin models, complex model integration, security issues, lack of access to essential data, and high costs due to inefficient business models. This research develops an asset management platform suitable for Small and Medium Enterprises (SMEs) for the cross-company, secure, and intuitive collaborative management of digital twin assets. It can be achieved by developing an ontology-based semantic model of the assets, explainable machine learning (XAI), and a scenario-based intelligent search and discovery mechanism.
Reinhold, Ylva; Valilai, Omid Fatahi; Wicaksono, Hendro
Will Industry 4.0 Applications Help in Designing Sustainable Forest Management? A Conceptual Framework of Connected Networks in Novel Sectors Proceedings Article
In: 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 0918–0922, IEEE 2023.
Abstract | Links | BibTeX | Tags: artificial intelligence, design science research, digital twins, industry 4.0, sustainability
@inproceedings{reinhold2023will,
title = {Will Industry 4.0 Applications Help in Designing Sustainable Forest Management? A Conceptual Framework of Connected Networks in Novel Sectors},
author = {Ylva Reinhold and Omid Fatahi Valilai and Hendro Wicaksono},
doi = {https://doi.org/10.1109/IEEM58616.2023.10406763},
year = {2023},
date = {2023-12-18},
urldate = {2023-01-01},
booktitle = {2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)},
pages = {0918–0922},
organization = {IEEE},
abstract = {Using Industry 4.0 technologies creates new opportunities in many fields. This paper examines the potential of such technologies for the forest sector. Existing research mainly proposes solutions to collect and analyze data on specific topics. This research aims to create a model combining different data inputs to draw a comprehensive picture of forest conditions by closing the gap between science and policymaking. With the help of a pre-defined set of indicators, the output is communicable across sectors and countries while maintaining practicability on a local level. The model evaluation has been completed according to the Design Science Research (DSR) Guidelines proposed by Hevner, et al., which prospected good chances of adoptability. With the successful implementation of the model, ways of decision-making for sustainable forest management could be revolutionized.
},
keywords = {artificial intelligence, design science research, digital twins, industry 4.0, sustainability},
pubstate = {published},
tppubtype = {inproceedings}
}
Using Industry 4.0 technologies creates new opportunities in many fields. This paper examines the potential of such technologies for the forest sector. Existing research mainly proposes solutions to collect and analyze data on specific topics. This research aims to create a model combining different data inputs to draw a comprehensive picture of forest conditions by closing the gap between science and policymaking. With the help of a pre-defined set of indicators, the output is communicable across sectors and countries while maintaining practicability on a local level. The model evaluation has been completed according to the Design Science Research (DSR) Guidelines proposed by Hevner, et al., which prospected good chances of adoptability. With the successful implementation of the model, ways of decision-making for sustainable forest management could be revolutionized.
Sukaridhoto, Sritrusta; Prayudi, Agus; Rasyid, Muhammad Udin Harun Al; Wicaksono, Hendro
Internet of Things Platform as a Service for Building Digital Twins and Blockchain Proceedings Article
In: Intelligent Systems Conference, pp. 616–635, Springer Nature Switzerland Cham 2023.
BibTeX | Tags: blockchain, digital twins
@inproceedings{sukaridhoto2023internet,
title = {Internet of Things Platform as a Service for Building Digital Twins and Blockchain},
author = {Sritrusta Sukaridhoto and Agus Prayudi and Muhammad Udin Harun Al Rasyid and Hendro Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Intelligent Systems Conference},
pages = {616–635},
organization = {Springer Nature Switzerland Cham},
keywords = {blockchain, digital twins},
pubstate = {published},
tppubtype = {inproceedings}
}
2020
Falah, Muhammad Fajrul; Sukaridhoto, Sritrusta; Rasyid, Muhammad Udin Harun Al; Wicaksono, Hendro
Design of virtual engineering and digital twin platform as implementation of cyber-physical systems Journal Article
In: Procedia Manufacturing, vol. 52, pp. 331–336, 2020.
BibTeX | Tags: cyber physical system, digital twins, virtual engineering, virtual reality
@article{falah2020design,
title = {Design of virtual engineering and digital twin platform as implementation of cyber-physical systems},
author = {Muhammad Fajrul Falah and Sritrusta Sukaridhoto and Muhammad Udin Harun Al Rasyid and Hendro Wicaksono},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Procedia Manufacturing},
volume = {52},
pages = {331–336},
publisher = {Elsevier},
keywords = {cyber physical system, digital twins, virtual engineering, virtual reality},
pubstate = {published},
tppubtype = {article}
}
Ro’fatulhaq, Hudzaifah; Wicaksono, Sandy Arif; Falah, Muhammad Fajrul; Sukaridhoto, Sritrusta; Zainuddin, Muhammad Agus; Rante, Hestiasari; Rasyid, Muhammad Udin Harun Al; Wicaksono, Hendro
Development of Virtual Engineering Platform for Online Learning System Proceedings Article
In: 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM), pp. 185–192, IEEE 2020.
BibTeX | Tags: augmented reality, digital twins, education, virtual engineering, virtual reality
@inproceedings{ro2020development,
title = {Development of Virtual Engineering Platform for Online Learning System},
author = {Hudzaifah Ro’fatulhaq and Sandy Arif Wicaksono and Muhammad Fajrul Falah and Sritrusta Sukaridhoto and Muhammad Agus Zainuddin and Hestiasari Rante and Muhammad Udin Harun Al Rasyid and Hendro Wicaksono},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)},
pages = {185–192},
organization = {IEEE},
keywords = {augmented reality, digital twins, education, virtual engineering, virtual reality},
pubstate = {published},
tppubtype = {inproceedings}
}