Abstract
The skull is a complex structure and multidimensional in nature, so for surgeons operating skull injuries is a difficult. In recent years, Additive Manufacturing is popularly known as 3D printing technology where objects can be produced in 3 dimensional formats is playing a vital role in biomedical field and especially in complex surgeries. 3D printing technology helps the surgeon to visualize the injuries on a 3D physical model. The aim of this research work is assessment of dimensional accuracy of reproducibility of cadaver skull by FDM additive manufacturing process. In this paper a cadaver skull is subjected to 3D scanning, the scanned data is obtained in .STL format. This file is then imported into Simplify 3D software which is 3D printing machine interface software for printing skull. The skull is 3D printed using Fused Deposition Modelling (FDM) 3D-Printing process. To assess the dimensional accuracy of 3D printed skull, fixed land marks are taken on cadaver and 3D printed skull with reference to standard journal. These land marks are divided into four regions (craniofacial, mid face, orbital, skull base). The distance between land marks are measured by Digital Vernier Calipers. These measurements help in assessing the dimensional accuracy of cadaver skull with 3D printed skull.
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Acknowledgements
The Authors would like to thank UGC, South Eastern Regional Office, Hyderabad, as this research is carried out as a part of UGC Minor Research Project titled “Assessment of Dimensional Accuracy of Facial (Zygomatic) bone fractures reduction using Additive Manufacturing: a Randomized Control Study” Proposal No. 1204, Letter No. F. No: 4-4/2015-16/MRP/UGC-SERO, Dt. Oct 2016 sanctioned to Dr. L. Siva Rama Krishna.
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Siva Rama Krishna, L., Balasany, U.K., Venkatesh, S.R., Potturi, A. (2020). Assessment of Dimensional Accuracy of Reproducibility of Cadaver Skull by FDM Additive Manufacturing. In: Satapathy, S., Raju, K., Molugaram, K., Krishnaiah, A., Tsihrintzis, G. (eds) International Conference on Emerging Trends in Engineering (ICETE). Learning and Analytics in Intelligent Systems, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-24314-2_78
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DOI: https://doi.org/10.1007/978-3-030-24314-2_78
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