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دسته بندی:
مدیریت پروژه - Project Management
سال انتشار:
2020
عنوان انگلیسی مقاله:
Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems
ترجمه فارسی عنوان مقاله:
مدیریت داده های همکاری مشترک زوج دیجیتال برای سیستم های تولید مواد افزودنی فلز
منبع:
Sciencedirect - Elsevier - Journal of Manufacturing Systems,Corrected proof,doi:10.1016/j.jmsy.2020.05.010
نویسنده:
Chao Liua,*, Léopold Le Rouxa, Carolin Körnerb, Olivier Tabastec, Franck Lacana, Samuel Bigota
چکیده انگلیسی:
Metal Additive Manufacturing (AM) has been attracting a continuously increasing attention due to its great
advantages compared to traditional subtractive manufacturing in terms of higher design flexibility, shorter
development time, lower tooling cost, and fewer production wastes. However, the lack of process robustness,
stability and repeatability caused by the unsolved complex relationships between material properties, product
design, process parameters, process signatures, post AM processes and product quality has significantly impeded
its broad acceptance in the industry. To facilitate efficient implementation of advanced data analytics in metal
AM, which would support the development of intelligent process monitoring, control and optimisation, this
paper proposes a novel Digital Twin (DT)-enabled collaborative data management framework for metal AM
systems, where a Cloud DT communicates with distributed Edge DTs in different product lifecycle stages. A
metal AM product data model that contains a comprehensive list of specific product lifecycle data is developed to
support the collaborative data management. The feasibility and advantages of the proposed framework are
validated through the practical implementation in a distributed metal AM system developed in the project
MANUELA. A representative application scenario of cloud-based and deep learning-enabled metal AM layer
defect analysis is also presented. The proposed DT-enabled collaborative data management has shown great
potential in enhancing fundamental understanding of metal AM processes, developing simulation and prediction
models, reducing development times and costs, and improving product quality and production efficiency.
Keywords: Metal Additive Manufacturing | Digital Twin | data management | data model | machine learning | product lifecycle management
قیمت: رایگان
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