Study on Reverse Forming Process of Tobacco Mechanical Sheet Metal with Large Curvature
Pages 122 - 130
Abstract
The development of information technology led by computers has made great changes in the industrial field. Product design is gradually moving from manual drawing to software drawing, which has the characteristics of fast drawing speed, accuracy, and timely modification when errors are found, which greatly simplifies the production process and improves the production efficiency. Reverse engineering can quickly understand or copy the production process of a product. Taking a large curvature sheet used in tobacco machinery as the research object, through 3D scanning, the measured data are reconstructed from the digital model of the real object through the software Geomagic Design X, and the error between the original and the model is obtained. This method can improve the design and manufacture of large curvature sheet metal parts, and achieve the goal of improving the detection accuracy of curved parts.
References
[1]
Elbestawi M A, and Sagherian R. 1991. Dynamic modeling for the prediction of surface errors in the milling of thin-walled sections. J. Mater. Process. Technol. 25:215-228.
[2]
Meshreki M, Kovecses J, Attia H, and Tounsi N. 2008. Dynamic modeling and analysis of thin-walled aerospace structure for fixture design in multiaxis milling. ASMEJ. Manuf. Sci. Engng. 130(3),031011,1-12
[3]
Seguy S, Dessein G, and Arnaud L, 2008. Surface roughness variation of thin wall milling, related to modal interactions. Int. J. Machine Tools Manuf. 48: 261-274
[4]
Zhao W, He N, Wu K, and He L, 2004. Optimization of cutting parameters in high speed milling of thin-walled structure components. Key Engng Mater. 259-260:814-818.
[5]
Mohammed A, Ahmed A, and Mohammed E. 2021. Reverse engineering canvas (REC): a visual tool for supporting reverse engineering activities. Int. J. Interact. Des. Manuf. 15(2-3): 249-257.
[6]
Korosec M, Duhovnik J, and Vukasinovic N. 2010. Identification and optimization of key process parameters in noncontact laser scanning for reverse engineering. Comput, Aided Design. 42: 744-48.
[7]
Durupt A, Remy S, Ducellier G, and Eynard B. 2008. From a 3D point cloud to an engineering CAD model: a knowledge-product-based approach for reverse engineering. Virtu. Phys. Proto. 3(2): 51–59.
[8]
Ouamer-Ali MI, Laroche F, Bernard A, and Remy S. 2014. Toward a methodological knowledge based approach for partial automation of reverse engineering. Proc. CIRP. 21: 270–275.
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Published In
December 2022
1396 pages
ISBN:9781450398343
DOI:10.1145/3584376
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 April 2023
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RICAI 2022
RICAI 2022: 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence
December 16 - 18, 2022
Dongguan, China
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