Micro Manufacturing Unit – Creating Digital Twin Objects with Common Engineering Software

Lohtander, Mika; Garcia, Elodie; Lanz, Minna; Volotinen, Jarno; Ratava, Juho; Kaakkunen, Jarno

doi:10.1016/j.promfg.2018.10.071

Cited by 19 publications

(11 citation statements)

References 11 publications

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“…Instead of relying on cloud computation, integration of edge, fog, and cloud infrastructure is necessary to distribute the responsibility of data processing. The challenge related to enormous data acquisition, analysis, limited awareness of methodology and modeling is still unresolved [227]. In terms of healthcare, edge, fog, cloud, AI-ML algorithms, and big data analytics holds importance in processing data for monitoring, diagnosis, selection of best surgical method, comparison with hundreds of previous patients, and predictive analysis.…”

Section: Future Researchmentioning

confidence: 99%

Digital Twin Perspective of Fourth Industrial and Healthcare Revolution

2022

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Digital Twin (DT) is bringing revolution to our lives by a digital representation of the physical system. DT is the creation of the joint usage of various technologies like Cyber-Physical System (CPS), Internet of Things (IoT), Big Data, Edge Computing (EC), Artificial Intelligence (AI), and Machine Learning (ML), etc. DTs are established to optimize a wide range of applications of industry, healthcare, smart cities, smart homes, etc. It is still in its early development stages. This paper fills the gaps by combining the extensive information on technologies utilized in the creation of DT in industry and healthcare. The paper focuses on studying the characteristics of DT, communication technologies and tools utilized in the creation of DT models, reference models, standards, and the researcher's recent work in smart manufacturing and healthcare. Challenges and open issues that need attention are also discussed.

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Section: Future Researchmentioning

confidence: 99%

Digital Twin Perspective of Fourth Industrial and Healthcare Revolution

2022

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“…This increases the amount of the information needed, not only because of control or inter-microprocesses communications, 61 but also due to microfeatures or microparts involved in Computer Aided Process Planning. 62 Additionally, visualization issues 63 have been raised in literature. All these add up to the complexity of the computational system, requiring even a cloud infrastructure in cases where local intelligence has been exhausted by the requirements of microprocessing, either on processing, or design.…”

Section: State Of the Artmentioning

confidence: 99%

Designing a digital twin for micromanufacturing processes

Stavropoulos

Papacharalampopoulos

2022

Advances in Mechanical Engineering

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Micromanufacturing raises some interesting, yet demanding and complex issues regarding modeling and data handling. Since utilizing both of these methods seems to be the key for process optimization, and preferably in real time, their coexistence is not a straightforward task, mainly due to interoperability and computational times. This work is a first step towards creating a roadmap for the design and the implementation of digital twins, provided that the digital twin offers both process optimization and tacit knowledge aggregation towards digital twin reusability. This involves the cooperation of different mathematical tools, an automated information manipulator and a human operator. Towards presenting such a roadmap and illustrating the choice of a workflow, a classification of the potentially useful tools is also presented herein. After that, a method to connect the tools through a software platform is discussed in order for a digital twin to be implemented. Finally, numerical examples are given to prove the fact that information reduction is needed in order for the proposed data manipulation effort (in terms of both processing and storing) to be minimized.

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“…Implementation of DT still faces challenges including lack of detailed methodology and standards, difficulties in collecting and storing large amounts of data [44,70,71], developing data acquisition system, synchronisation problems, modelling of a complex system, lack of awareness, resistance of companies to adopting the technology [59], and difficulties in constructing, understanding, controlling, and simulating real-time changes in the system. High-fidelity models are required to simulate and test the product or process in a virtual environment by reducing development time and cost [26,72] The issue of high investment cost and data security is still a hindrance to many companies making DT part of their daily life [55].…”

Section: Challenges In the Implementation Of Digital Twinmentioning

confidence: 99%

“…One study [14] has demonstrated the application of DT in the production system to optimise the safety and ergonomics of the working environment. Similarly, it has the advantage of improving the level of automation, adaptability, and flexibility [35,59,61,62], operational efficiency [63], cost reduction [49], solving problems of regulatory difficulties [50], and the creation of new revenues adding product features and business models [64]. Production optimisation [24] is another key function of DT where many researchers are currently focussing.…”

Section: Utilising Digital Twin In Production Phasesmentioning

confidence: 99%

“…Estimation of grasping point locations on objects [14] System/unit Optical object tracking [3] Process Smart manufacturing [16] Process Augmented reality industrial solutions [17] Process Decision support [24,29,55] System Aircraft predictive maintenance [4,49] System Maintenance [16,27] Process System integration [57] System Smart factory and cyber-physical production systems [6] System Optimisation of plant and machinery [46,47] Component Human-robot collaborative system [52,58] System Micro-manufacturing [26,59] MELESSE ET AL.…”

Section: Componentmentioning

confidence: 99%

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Digital Twin models in industrial operations: State‐of‐the‐art and future research directions

Melesse

Pasquale

Riemma

2021

IET Collab Intel Manufact

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A Digital Twin is a virtual representation of a physical product, asset, process, system, or service that allows us to understand, predict, and optimise their performance for better business outcomes. Recently, the use of Digital Twin in industrial operations has attracted the attention of many scholars and industrial sectors. Despite this, there is still a need to identify its value in industrial operations mainly in production, predictive maintenance, and after-sales services. Similarly, the implementation of a Digital Twin still faces many challenges. In response, a systematic literature review and analysis of 41 papers published between 2016 and 11 July 2020 have been carried out to examine recently published works in the field. Future research directions in the area are also highlighted. The result reveals that, regardless of the challenges, the role of Digital Twin in the advancement of industrial operations, especially production and predictive maintenance is highly significant. However, its role in after-sales services remains limited. Insights are offered for research scholars, companies, and practitioners to understand the current state-of-the-art and challenges, and to indicate future research possibilities in the field. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Micro Manufacturing Unit – Creating Digital Twin Objects with Common Engineering Software

Cited by 19 publications

References 11 publications

Digital Twin Perspective of Fourth Industrial and Healthcare Revolution

Digital Twin Perspective of Fourth Industrial and Healthcare Revolution

Designing a digital twin for micromanufacturing processes

Digital Twin models in industrial operations: State‐of‐the‐art and future research directions

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