Approach for an Update Method for Digital Factory Models

Hellmuth, René; Wehner, Florian; Giannakidis, Alexandros

doi:10.1016/j.procir.2020.03.042

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…18) (except for image-based/operation phase), reference can be made to a large number of literature sources. It is noticeable that the only approach for updating digital building models via photogrammetry (image-based) of existing buildings in the operation phase is the one of Hellmuth et al (2020). Another approach considering image-based updating during the operational phase is the publication by Klein et al (2012).…”

Section: Built Heritage Modelingmentioning

confidence: 99%

Update approaches and methods for digital building models – literature review

Hellmuth¹

2022

ITcon

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A combination of systematic and bibliometric literature review is applied. For bibliometric analysis a citation network analysis, keyword cluster analysis, burst detection analysis and the total citation score are evaluated. So far, literature mainly deals with creation of digital building models. However, in the operational phase of a building, updating digital building models is essential in order to use the digital building models for applications like maintenance, facility management and conversion planning. In this paper approaches and methods to update digital building data are examined systematically to reflect the current state of scientific knowledge. This will identify and compare update approaches for digital building models. Nine interrelated research areas can be identified that deal with updating digital building information. Based on the body of knowledge on the topic "Updating of digital building models", research gaps and poorly represented research areas are identified. One gap in research is the use of photogrammetry to update individual buildings during their operational phase. In addition to strong research areas such as geoscience, which has produced update approaches over the years, areas like factory planning have been identified which hardly consider approaches to updating as-built data. Only the most important and largest research areas involved have been considered. A more detailed examination would be possible, but would go beyond the scope of this paper. This paper is the first to analyse updating approaches in a detailed, systematic, and bibliographic manner. The holistic view of the topic of updating approaches of digital building models helps to get an overview of existing approaches. Especially because in building operation up-to-date digital plan documents of the building are increasingly needed, this overview is necessary.

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Section: Built Heritage Modelingmentioning

confidence: 99%

Update approaches and methods for digital building models – literature review

Hellmuth¹

2022

ITcon

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“…The megatrend toward the volatile market environment and individual customer demand poses new challenges, primarily for production systems and management in industrial enterprises, which can significantly influence the profitability and productivity of manufacturing. Within the various presented concepts and frameworks [ 34 , 35 , 36 , 37 , 38 , 39 ], automated production systems, including mixed reality assistance systems [ 40 , 41 ], could be rapidly modularized [ 42 ] and reconfigured [ 43 , 44 , 45 ], enhanced with AI [ 46 , 47 ] and sensors [ 48 , 49 ] and, in combination with cloud and edge computing [ 50 ], transformed into distributed control systems, while detailed production environments can be generated and updated in the form of 3D point clouds [ 51 , 52 , 53 , 54 , 55 , 56 ]. Based on these infrastructures, DT demonstrates the capability of handling increasingly complex operational problems, such as production planning and scheduling [ 57 , 58 , 59 , 60 ], production monitoring and control [ 61 , 62 , 63 , 64 , 65 , 66 ], quality control and management [ 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ], as well as logistics […”

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

162

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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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“…Traditionally, this approach is based on the surveyor's knowledge, aiming to fulfill all requirements regarding density, and coverage but also efficiency in execution time and therefore cost. To enable the implementation of a Digital Twin (Boje et al 2020), especially in frequently changing industrial scenes (Hellmuth, Wehner, and Giannakidis 2020;Shellshear, Berlin, and Carlson 2015), this process should be as precise and robust as possible, when data capture is not a one-shot measure but a recurring part of the process.…”

Section: Introductionmentioning

confidence: 99%