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Intelligent geometry compensation for additive manufactured oral maxillary stent by genetic algorithm and backpropagation network.
Zhang, Zifan; Xie, Deqiao; Lv, Fei; Liu, Ruikang; Yang, Youwen; Wang, Lin; Wu, Guofeng; Wang, Changjiang; Shen, Lida; Tian, Zongjun.
  • Zhang Z; College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; Institute of Additive Manufacturing (3D Printing), Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
  • Xie D; College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; Institute of Additive Manufacturing (3D Printing), Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
  • Lv F; College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; Institute of Additive Manufacturing (3D Printing), Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
  • Liu R; College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; Institute of Additive Manufacturing (3D Printing), Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
  • Yang Y; Institute of Bioadditive Manufacturing, Jiangxi University of Science and Technology, Nanchang, 330044, China.
  • Wang L; College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; Institute of Additive Manufacturing (3D Printing), Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; Nanjing Chamlion Laser Technology Co., Ltd, Nan
  • Wu G; Stomatological Digital Engineering Center, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, China.
  • Wang C; Department of Engineering and Design, University of Sussex, Brighton, BN1 9RH, United Kingdom.
  • Shen L; College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; Institute of Additive Manufacturing (3D Printing), Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China. Electronic address: ldshen@nuaa.edu.cn.
  • Tian Z; College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; Institute of Additive Manufacturing (3D Printing), Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China. Electronic address: tianzj@nuaa.edu.cn.
Comput Biol Med ; 157: 106716, 2023 05.
Article en En | MEDLINE | ID: mdl-36905868
ABSTRACT
Recently, laser powder bed fusion (LPBF) has shown great potential in advanced manufacturing. However, the rapid melting and re-solidification of the molten pool in LPBF leads to the distortion of parts, especially thin-walled parts. The traditional geometric compensation method, which is used to overcome this problem, is simply based on mapping compensation, with the general effect of distortion reduction. In this study, we used a genetic algorithm (GA) and backpropagation (BP) network to optimize the geometric compensation of Ti6Al4V thin-walled parts fabricated by LPBF. The GA-BP network method can generate free-form thin-walled structures with enhanced geometric freedom for compensation. For the GA-BP network training, an arc thin-walled structure was designed and printed by LBPF and measured via optical scanning measurements. The final distortion of the compensated arc thin-walled part based on GA-BP was reduced by 87.9% compared with PSO-BP and mapping method. The effectiveness of this GA-BP compensation method is further evaluated in an application case using new data points, and the result shows that the final distortion of the oral maxillary stent was reduced by 71%. In summary, the GA-BP-based geometric compensation proposed in this study can better reduce the distortion of thin-walled parts with higher time and cost efficiencies.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Algoritmos / Stents Tipo de estudio: Prognostic_studies Idioma: En Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Algoritmos / Stents Tipo de estudio: Prognostic_studies Idioma: En Año: 2023 Tipo del documento: Article