Evaluation of Machine Tool Digital Twin for machining operations in industrial environment

Armendia, Mikel; Cugnon, Frédéric; Berglind, Luke; Öztürk, Erdem; Gil, Guillermo Orrego; Selmi, Jaouher

doi:10.1016/j.procir.2019.04.040

Cited by 51 publications

(13 citation statements)

References 8 publications

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“…The articles from the other countries combined follow the same trend as those presented (Zhang et al, 2019), (Sepasgozar et al, 2019), ), (Kaurene et al, 2021, (Singh et al, 2021), (Khajavi et al, 2019), (Bruynseels et al, 2018), (Vrabičet al, 2018), (Armendia et al, 2019), (Pizzolato et al, 2019), (Bányai et al, 2019), (Kuts et al, 2019), the use of new technologies in the implementation of DT in processes, management and service. From our analysis, it is possible to indicate that the consolidation of a wide application of DT in HM undergoes the appropriation and adaptation of the applications that are being carried out and the adjustment to the specificities of HM.…”

Section: Discussionmentioning

confidence: 56%

A bibliometric analysis of the relationship between Digital Twins and Health Management: based on the Web of Science (WoS) platform

Ribeiro¹,

Costa²,

Cruz³

2022

RSD

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Amidst the development of Industry 4.0, the appropriation of digital tools applied to production and manufacturing of activities represents a challenge for managers in other areas. Digital Twin (DT) technology is based on the integration of different "traditional" tools, such as simulation modeling and sensors, and aims to increase the performance of any process that can be represented virtually. With the increase in population, the demand for more efficient and universal Health Management (HM) has become a challenge of the 21st century. This study aims to analyze the relationship between the field of knowledge DT and HM and their interactions. A bibliometric review was performed using the Web of Science database through the Bibliometrix package and the VOSviewer application to evaluate studies, applications and identify research clusters and future trends. Our study indicates that the applications of DT in HM are focused on the diagnosis and monitoring of chronic diseases and that, so far, there is not a critical mass of knowledge that consolidates a general theory of application of DT and HM. This study identifies a relational hotspot between the integration of a DT in the optimization of resource management and patient care.

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Section: Discussionmentioning

confidence: 56%

A bibliometric analysis of the relationship between Digital Twins and Health Management: based on the Web of Science (WoS) platform

Ribeiro¹,

Costa²,

Cruz³

2022

RSD

View full text Add to dashboard Cite

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“…The authors of [20] present a data-driven framework for the prediction of cutting forces. In [21], a digital twin model that is suited for machine tool design and optimisation is presented, whereas [22] presents a digital twin driven-data flow framework for cutting tools. There is a tremendous benefit of the Digital Twin for applications in the industry, but there is still a lack of case-studies which apply the concepts in practice [6], especially for maintenance and monitoring applications.…”

Section: Literature Review: Digital Twins For Monitoring Of a Machine...mentioning

confidence: 99%

Development and deployment of a digital twin for monitoring of an adaptive clamping mechanism, used for high performance composite machining

Weckx

Meyers

Jordens

et al. 2022

IET Collab Intel Manufact

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In this work, we present a cloud‐based digital twin for monitoring of a clamping technology for machining of composite parts. Supporting large and/or freeform composite parts is crucial to avoid bending during drilling. Bending of the part will lead to delamination and frayed edges of the drilled holes. The new active clamping technology allows to realise a stabilised fixture, localised in the area where the drilling occurs, to avoid bending. This significantly improves the quality of the drilled holes. The clamping device is equipped with an IoT edge device, with a bidirectional communication to the cloud. The cloud‐based digital twin analyses the quality of the drilled holes based on computer vision, monitors the drill wear and detects incorrect operation of the active clamping device. All data is stored in the cloud. By means of a knowledge graph, which acquires and integrates information into an ontology and provides a central information access, it will be easier for a data scientist to query this data and to gain new insights in the operation of the drill with active clamping device. The full deployment occurs on the Microsoft Azure cloud platform. This transforms the standard machine into an Industry 4.0 compliant machine.

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“…For instance, the surface quality of parts can be estimated in parallel to the process within a GPU-enabled (graphical processing unit) material removal simulation with a subsequent virtual measurement, which significantly reduces the latency between machining and the detection of defective parts [ 166 , 167 ]. A sufficiently accurate virtual representation of the machining process [ 26 , 168 , 169 , 170 , 171 ] also enables cause-and-effect analysis and, therefore, robust process design and control [ 172 , 173 , 174 , 175 , 176 ] as well as the exploitation of potential process productivity [ 177 ]. Further cases of production processes with knowledge-based approaches include welding [ 178 , 179 ], injection molding [ 180 , 181 ], linear winding [ 182 ], tape laying [ 183 ], metal forming [ 184 ], laser polishing [ 185 ], automated fiber placement [ 186 ], sheet molding compound [ 187 ], fused filament fabrication [ 188 ], and metal additive manufacturing (AM) [ 189 , 190 , 191 , 192 , 193 ].…”

Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

189

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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Evaluation of Machine Tool Digital Twin for machining operations in industrial environment

Cited by 51 publications

References 8 publications

A bibliometric analysis of the relationship between Digital Twins and Health Management: based on the Web of Science (WoS) platform

A bibliometric analysis of the relationship between Digital Twins and Health Management: based on the Web of Science (WoS) platform

Development and deployment of a digital twin for monitoring of an adaptive clamping mechanism, used for high performance composite machining

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

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