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A Case Study on the Framework Development of the Metal 3D Printing Control & Monitoring System

금속 3D프린팅 통합 제어 및 모니터링 시스템 개발을 위한 프레임워크에 관한 연구

  • Jeon, Byung-Ju (Department of Business Administration, Kumoh National Institute of Technology) ;
  • Lee, Sun-Kyu (Department of Business Administration, Kumoh National Institute of Technology) ;
  • Lee, Seung-Hee (Department of Business Administration, Kumoh National Institute of Technology) ;
  • Jang, Sung-Ho (Department of Industrial Engineering, Kumoh National Institute of Technology) ;
  • Jung, Goo-sang (Conception. co., Ltd.)
  • 전병주 (금오공과대학교 산업경영학과) ;
  • 이선규 (금오공과대학교 경영학과) ;
  • 이승희 (금오공과대학교 경영학과) ;
  • 장성호 (금오공과대학교 산업공학과) ;
  • 정구상 ((주)컨셉션)
  • Received : 2020.10.15
  • Accepted : 2020.11.20
  • Published : 2020.11.28

Abstract

This study present to Framework & R&D direction of the 3d printing Integrated Control & Monitoring System. To ensure this purpose, we developed integrated 3d printing control system Framework for DED & PBF and we introduce 4 monitoring system include photo diode, gas flow, acoustic and spectrometer sensors. For this study, we utilize metal 3d printing system from Conception., OKE Tech and DE&T who are still developing Metal 3D Printing Technology since 2017. In the result, we represent the latest 3D Printing Control and Monitoring System for the next 3D Printing researcher and we hope this study will be used as a basic reference and data for Cooperation between mechanic, electronic and material fields.

본 연구는 최근 새로운 제조 수단으로 각광받고 있는 3D프린팅 기술의 통합제어 시스템과 품질개선을 위한 모니터링 SW 기술개발을 위한 프레임워크 및 연구개발 방향을 제시하고자 한다. 이를 위해 본 연구에서는 금속프린팅 기술로 조명되고 있는 DED와 PBF 3D프린팅 기술의 통합 제어기술 개발 Framework와 최근 반도체 장비 등에서 큰 관심을 받고 있는 음향센서를 이용한 모니터링 기술 등 품질 개선을 위한 4가지 모니터링 기술을 제안 소개하고자 한다. 본 연구를 위하여, 국내 3D프린팅 전문기업인 (주)컨셉션, 원광이엔텍(주), (주)디이엔티 등에서 개발 중인 국내 최신 금속 3D프린팅 시스템 장비를 활용하여 연구하였으며(1KW급 Dual Laser PBF 및 DED 프린팅 시스템), 2017년 이래 지속적인 연구개발을 수행해온 경험을 바탕으로 다음세대 3D프린팅 개발자를 위한 연구초안을 제시함으로서 국내 3D프린팅 기술 발전 및 연구개발 협력을 위한 기초자료를 제시하고자 한다.

Keywords

References

  1. G. White. (2015). Industry analysis: The pros and cons of 3D printing . Retrieved from http://www.manufacturingglobal.com/
  2. T. G. Spears and S. A. Gold. (2016). "In-process sensing in selective laser melting (SLM) additive manufacturing," Integr. Mater. Manuf. Innov.
  3. J. Evans,. (2014). "DMLS: A Bumpy Road in History," Design & Motion, [Online]. Available: https://designandmotion.net/design-2/manufacturing-design/dmls-a-little-history/.[Accessed: 11-Oct-2017].
  4. T. Wohlers and T. Caffrey,. (2015). Wohlers Report 2015: 3D Printing and Additive Manufacturing State of the Industry Annual Worldwide Progress Report.
  5. American Society for Testing and Materials, "Committee F42 on Additive Manufacturing Technologies - Scope," ASTM, 2009. [Online]. Available:https://www.astm.org/COMMIT/SCOPES/F42.htm. [Accessed:01-Jan-2017].
  6. G. Tapia and A. Elwany,. (2014). "A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing," J. Manuf. Sci. Eng., vol. 136, no. 6, p. 60801, https://doi.org/10.1115/1.4028540
  7. H. S. Park, N. H. Tran, and D. S. Nguyen,. (2017). "Development of a predictive system for SLM product quality," IOP Conf. Ser. Mater. Sci. Eng., vol. 227, p. 12090,
  8. A. E. Patterson, S. L. Messimer, and P. A. Farrington,. (2017). "Overhanging Features and the SLM/DMLS Residual Stresses Problem: Review and Future Research Need," Technologies, vol. 5, no. 2, p. 15, https://doi.org/10.3390/technologies5020015
  9. Jacob, G., Donmez, A., Slotwinski, J., and Moylan, S., (2016). "Measurement of Powder Bed Density in Powder Bed Fusion Additive Manufacturing Processes," Measurement Science and Technology, Vol. 27, No. 11, Paper No. 115601.
  10. Lee, J. and Prabhu, V., (2016). "Simulation Modeling for Optimal Control of Additive Manufacturing Processes," Additive Manufacturing, Vol. 12, pp. 197-203. https://doi.org/10.1016/j.addma.2016.05.002
  11. Kamath, C., (2016). "On the Use of Data Mining Techniques to Build High-Density, Additively-Manufactured Parts," in: Information Science for Materials Discovery and Design, Springer, pp. 141-155.
  12. Tapia, G., Elwany, A., and Sang, H., (2016). "Prediction of Porosity in Metal- Based Additive Manufacturing Using Spatial Gaussian Process Models," Additive Manufacturing, Vol. 12, pp. 282-290. https://doi.org/10.1016/j.addma.2016.05.009
  13. Jeffrey., Travis (2006). LabVIEW for everyone : graphical programming made easy and fun. Kring, Jim. (3rd ed.). Upper Saddle River, NJ: Prentice Hall. ISBN 0131856723. OCLC 67361308
  14. Alessandro Ceruti., Alfredo Liverani & Tiziano Bombardi. (2017). Augmented vision and interactive monitoring in 3D printing process, International Journal on Interactive Design and Manufacturing (IJIDeM) volume 11, pp. 385-395. https://doi.org/10.1007/s12008-016-0347-y
  15. Xin Lin, Kunpeng Zhu, Jinxin Zhou, Jerry Ying Hsi Fuh, (2020). Intelligent modeling and monitoring of micro-droplet profiles in 3D printing, ISA Transactions, The Journal of Automation, Volume 105, pp. 367-376
  16. Ugandhar Delli, Shing Chang., (2018), Automated Process Monitoring in 3D Printing Using Supervised Machine Learning, Procedia Manufacturing, Volume 26, pp. 865-870 https://doi.org/10.1016/j.promfg.2018.07.111