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1 |
Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems
مدیریت داده های همکاری مشترک زوج دیجیتال برای سیستم های تولید مواد افزودنی فلز-2020 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 |
مقاله انگلیسی |
2 |
Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding
تشخیص نقص تصویر جوش تشخیص عمیق مبتنی بر یادگیری عمیق برخط با استفاده از شبکه های عصبی همگرا برای آلیاژ آل در جوش قوس رباتیک-2019 Accurate on-line weld defects detection is still challenging for robotic welding manufacturing due to the complexity
of weld defects. This paper studied deep learning–based on-line defects detection for aluminum alloy in robotic arc
welding using Convolutional Neural Networks (CNN) and weld images. Firstly, an image acquisition system was
developed to simultaneously collect weld images, which can provide more information of the real-time weld images
from different angles including top front, top back and back seam. Then, a new CNN classification model with 11
layers based on weld image was designed to identify weld penetration defects. In order to improve the robustness and
generalization ability of the CNN model, weld images from different welding current and feeding speed were captured
for the CNN model. Based on the actual industry challenges such as the instability of welding arc, the complexity
of the welding environment and the random changing of plate gap condition, two kinds of data augmentation
including noise adding and image rotation were used to boost the CNN dataset while parameters optimization was
carried out. Finally, non-zero pixel method was proposed to quantitatively evaluate and visualize the deep learning
features. Furthermore, their physical meaning were clearly explained. Instead of decreasing the interference from arc
light as in traditional way, the CNN model has taken full use of those arc lights by combining them in a various way
to form the complementary features. Test results shows that the CNN model has better performance than our previous
work with the mean classification accuracy of 99.38%. This paper can provide some guidance for on-line
detection of manufacturing quality in metal additive manufacturing (AM) and laser welding. Keywords: Deep learning | Defects detection | Al alloy | Robotic arc welding | Convolutional neural networks | Weld images | Feature visualization |
مقاله انگلیسی |