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An application to Stereolithography of a feature recognition algorithm for manufacturability evaluation

  • Giampaolo Campana
  • Mattia Mele
Article
  • 19 Downloads

Abstract

Additive manufacturing processes are experiencing extraordinary growth in present years. Concerning the production of goods by using this technology, expertise and know-how are today relevant while process simulation needs to be extensively validated before acquiring the necessary reliability, which is already achieved and established for a number of manufacturing processes. The objective of the present work is the development of a new algorithm for feature recognition, which is the first step towards an application of rules for manufacturability to digital models. The proposed approach was specifically conceived for design for additive manufacturing (DfAM). The method starts from a graph-based representation of geometric models that is the base for the definition of new and original geometrical entities. Then, an algorithm-based process has been identified and proposed for their detection. Eventually, these geometrical entities have been used for comparison with rules and constraints of DfAM in order to point out possible critical issues for manufacturability. A self-developed plugin software was implemented for the application of proposed procedure in Computer Aided Design systems. Several applications of a set of DfAM rules are provided and tested to validate the method by means of case studies. As a conclusion, such an application demonstrated the suitability of the approach for detections of features that are relevant to an early investigation into Stereolithography manufacturability. Presented approach could be helpful during early phases of product development for detecting critical manufacturing issues and thus for realising an assistant-tool that can help designers by displaying potential solutions to overcome them. Since the very first steps of product design, this integration of manufacturing knowledge allows for a reduction of a number of potential errors occurring during product fabrication and then for a decrease of required time for product development.

Keywords

Feature recognition (FR) Knowledge based engineering (KBE) Design for manufacturing (DfM) Additive manufacturing (AM) Stereolithography (SLA) 

Notes

Acknowledgements

The authors would like to thank the MiUR (Italian Ministry of Education, University and Research). A grateful thank to 3DSystems and Cimatron for their support during the development of the work.

References

  1. Anjum, N. A., Harding, J. A., Young, R. I. M., & Case, K. (2012). Manufacturability verification through feature-based ontological product models. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 226(6), 1086–1098.CrossRefGoogle Scholar
  2. Babic, B., Nesic, N., & Miljkovic, Z. (2008). A review of automated feature recognition with rule-based pattern recognition. Computers in Industry, 59(4), 321–337.CrossRefGoogle Scholar
  3. Brousseau, E., Dimov, S., & Setchi, R. (2008). Knowledge acquisition techniques for feature recognition in CAD models. Journal of Intelligent Manufacturing, 19(1), 21–32.CrossRefGoogle Scholar
  4. Calignano, F., et al. (2017). Investigation of accuracy and dimensional limits of part produced in aluminum alloy by selective laser melting. The International Journal of Advanced Manufacturing Technology, 88, 451–458.CrossRefGoogle Scholar
  5. Chen, Y. M., Miller, R. A., & Sevenler, K. (1995). Knowledge-based manufacturability assessment: An object-oriented approach. Journal of Intelligent Manufacturing, 6(5), 321–337.CrossRefGoogle Scholar
  6. Gao, S., & Shah, J. J. (1998). Automatic recognition of interacting machining features based on minimal condition subgraph. Computer-Aided Design, 30(9), 727–739.CrossRefGoogle Scholar
  7. Gibson, I., et al. (2010). Design rules for additive manufacture. In 21st annual international solid freeform fabrication symposium, Austin, TX.Google Scholar
  8. Hague, R., Mansour, S., & Saleh, N. (2004). Material and design considerations for rapid manufacturing. International Journal of Production Research, 42(22), 4691–4708.CrossRefGoogle Scholar
  9. Han, J. H., Pratt, M., & Regli, W. C. (2000). Manufacturing feature recognition from solid models: A status report. IEEE Transactions on Robotics and Automation, 16(6), 782–796.CrossRefGoogle Scholar
  10. Hanrahan, P. (1983). Ray tracing algebraic surfaces. ACM SIGGRAPH Computer Graphics, 17(3), 83–90.CrossRefGoogle Scholar
  11. Jakubowski, J., & Peterka, J. (2014). Design for manufacturability in virtual environment using knowledge engineering. Management and Production Engineering Review, 5(1), 3–10.CrossRefGoogle Scholar
  12. Jiang, B. C., & Hsu, C. H. (2003). Development of a fuzzy decision model for manufacturability evaluation. Journal of Intelligent Manufacturing, 14(2), 169–181.CrossRefGoogle Scholar
  13. Joshi, S., & Chang, T. C. (1988). Graph-based heuristics for recognition of machined features from a 3D solid model. Computer-Aided Design, 20(2), 58–66.CrossRefGoogle Scholar
  14. Kranz, J., Herzog, D., & Emmelmann, C. (2015). Design guidelines for laser additive manufacturing of lightweight structures in TiAl6V4. Journal of Laser Applications, 27(S14001), 1–16.Google Scholar
  15. Liu, X. (2012). Modeling of additive manufacturing process relevant feature in layer based manufacturing process planning. Journal of Shanghai Jiaotong University (Science), 17(2), 241–244.CrossRefGoogle Scholar
  16. Lockett, H. (2005). A knowledge based manufacturing based manufacturing advisor for CAD. PhD thesis, Cranfield University.Google Scholar
  17. Lockett, H. L., & Guenov, M. D. (2005). Graph-based feature recognition for injection moulding based on a mid-surface approach. Computer-Aided Design, 37(2), 251–262.CrossRefGoogle Scholar
  18. Nannan, G., & Ming, C. L. (2013). Additive manufacturing: Technology, applications and research needs. Frontiers of Mechanical Engineering, 8(3), 215–243.CrossRefGoogle Scholar
  19. Nasr, E. S. A., & Kamrani, A. K. (2006). A new methodology for extracting manufacturing features from CAD system. Computers & Industrial Engineering, 51(3), 389–415.CrossRefGoogle Scholar
  20. Onwubolu, G. C. (1999). Manufacturing features recognition using backpropagation neural networks. Journal of Intelligent Manufacturing, 10, 289–299.CrossRefGoogle Scholar
  21. Öztürk, N., & Öztürk, F. (2004). Hybrid neural network and genetic algorithm based machining feature recognition. Journal of Intelligent Manufacturing, 15(3), 287–298.CrossRefGoogle Scholar
  22. Rahmani, K., & Arezoo, B. (2007). A hybrid hint-based and graph-based framework for recognition of interacting milling features. Computers in Industry, 58, 304–312.CrossRefGoogle Scholar
  23. Ranjan, R., Samant, R., & Anand, S. (2015). Design for manufacturability in additive manufacturing using a graph based approach. In Proceedings of the ASME 2015 International Manufacturing Science and Engineering Conference MSEC2015, 8–12 June 2015, Charlotte, NC.Google Scholar
  24. Shah, J. J., Anderson, D., Kim, Y. S., & Joshi, S. (2001). A discourse on geometric feature recognition from CAD models. ASME Journal of Computing and Information Science in Engineering, 1(1), 440–746.Google Scholar
  25. Thomas, D. (2009). The development of design rules for selective laser melting. PhD thesis. University of Wales.Google Scholar
  26. Thompson, M. K., et al. (2016). Design for additive manufacturing: Trends, opportunities, considerations, and constraints. CIRP Annals-Manufacturing Technology, 65(2), 737–760.CrossRefGoogle Scholar
  27. Tonhäuser, C., & Rudolph, S. (2017). Individual coffee maker design using graph-based design languages. In Design computing and cognition ’16 (pp. 513–533). Springer, Cham.Google Scholar
  28. Venkatachalam, A. R., Mellichamp, J. M., & Miller, D. M. (1993). A knowledge-based approach to design for manufacturability. Journal of Intelligent Manufacturing, 4(5), 355–366.CrossRefGoogle Scholar
  29. Wong, K. V., & Hernandez, A. (2012). A review of additive manufacturing. International Scholarly Research Network, ISRN Mechanical Engineering, pp. 1–10, Article ID 208760.  https://doi.org/10.5402/2012/208760.
  30. Zhang, X., Nassehi, A., & Newman, S. T. (2014). Feature recognition from CNC part programs for milling operations. The International Journal of Advanced Manufacturing Technology, 70, 397–412.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industrial Engineering (DIN)University of BolognaBolognaItaly

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