Abstract
A novel 7-axis robot for industrial large 3D printing applications is presented in this paper, which is developed by Institute of Robotics and Automatic Information System, Nankai University. Base on the prototype, the mechanism on three-dimensional printing is deeply dissected. By investigating the reason of color distortions in 3D printing, a novel multistep compensation algorithm based on model optimization and image compensation is developed. Finally, the experimental results show the performance of the 3D printing robot platform and the proposed algorithms.
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This work was supported by the National High Technology Research and Development Program 863 (No. 2009AA04Z222), and the Tianjin Nature Science Foundation (No. 09JCZDJC23700).
Xinwei CHEN is a Ph.D. candidate in Institute of Robotics and Automatic Information System, Nankai University. His research interest covers intelligent robotic technology.
Jingtai LIU is a professor of Institute of Robotics and Automatic Information System, Nankai University. His research interests cover robotic technology, computer applications, automatic information system, intelligence science and technology.
Tao SHI is a M.S. candidate student in Institute of Robotics and Automatic Information System, Nankai University. His research interests cover intelligent robotic technology.
Haili QIAN is a Ph.D. candidate in Institute of Robotics and Automatic Information System, Nankai University. His research interest covers intelligent robotic technology.
Kuo CHEN is a B.S. candidate of the Department of Automation and Intelligence Science, Nankai University. His research interests cover intelligence science and technology.
Qing WEI is a undergraduate student of the Department of Automation and Intelligence Science, Nankai University. His research interest covers intelligence science and technology.
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Chen, X., Liu, J., Shi, T. et al. 3D printing robot: model optimization and image compensation. J. Control Theory Appl. 10, 273–279 (2012). https://doi.org/10.1007/s11768-012-1122-7
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DOI: https://doi.org/10.1007/s11768-012-1122-7