Towards an automated decision support system for the identification of additive manufacturing part candidates

Abstract

As additive manufacturing (AM) continues to mature, an efficient and effective method to identify parts which are eligible for AM as well as gaining insight on what values it may add to a product is needed. Prior methods are naturally developed and highly experience-dependent, which falls short for its objectiveness and transferability. In this paper, a decision support system (DSS) framework for automatically determining the candidacy of a part or assembly for AM applications is proposed based on machine learning (ML) and carefully selected candidacy criteria. With the goal of supporting efficient candidate screening in the early conceptual design stage, these criteria are further individually decoded to decisive parameters which can be extracted from digital models or resource planning databases. Over 200 existing industrial examples are manually collected and labelled as training data; meanwhile, multiple regression algorithms are tested against each AM potential to find better predictive performance. The proposed DSS framework is implemented as a web application with integrated cloud-based database and ML service, which allows advantages of easy maintenance, upgrade, and retraining of ML models. Two case studies of a hip implant and a throttle pedal are used as demonstrating examples. This preliminary work provides a promising solution for lowering the requirements of non-AM experts to find suitable AM candidates.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. ADML (Additive Design and Manufacturing Laboratory) (2019a). ADML website app. GitHub. Retrieved September 9, 2019, from https://github.com/adml-mcgill/website/tree/master/app.

  2. ADML (Additive Design and Manufacturing Laboratory) (2019b). Automated candidate detection for additive manufacturing (BETA). ADML. Retrieved September 9, 2019, from http://adml.lab.mcgill.ca/app/.

  3. Aoyagi, K., Wang, H., Sudo, H., & Chiba, A. (2019). Simple method to construct process maps for additive manufacturing using a support vector machine. Additive Manufacturing, 27, 353–362.

    Google Scholar 

  4. ASTM International F42.91. (2015). Standard terminology for additive manufacturing—general principles—terminology. West Conshohocken: ASTM International.

    Google Scholar 

  5. 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.

    Google Scholar 

  6. Baumers, M., Tuck, C., Wildman, R., Ashcroft, I., & Hague, R. (2017). Shape complexity and process energy consumption in electron beam melting: a case of something for nothing in additive manufacturing? Journal of Industrial Ecology, 21(S1), 157–167.

    Google Scholar 

  7. Bogers, M., Hadar, R., & Bilberg, A. (2016). Additive manufacturing for consumer-centric business models: Implications for supply chains in consumer goods manufacturing. Technological Forecasting and Social Change, 102, 225–239.

    Google Scholar 

  8. Booth, J. W., Alperovich, J., Chawla, P., Ma, J., Reid, T. N., & Ramani, K. (2017). The design for additive manufacturing worksheet. Journal of Mechanical Design, 139(10), 100904.

    Google Scholar 

  9. Caligiana, G., Liverani, A., Francia, D., Frizziero, L., & Donnici, G. (2017). Integrating QFD and TRIZ for innovative design. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 11(2), JAMDSM0015-JAMDSM0015.

    Google Scholar 

  10. Chaturved, A. R., Hutchinson, G. K., & Nazareth, D. L. (1992). A synergistic approach to manufacturing systems control using machine learning and simulation. Journal of Intelligent Manufacturing, 3(1), 43–57. https://doi.org/10.1007/BF01471750.

    Article  Google Scholar 

  11. Coadou, Y. (2013). Boosted decision trees and applications. EPJ Web of Conferences (EDP Sciences), 55, 02004.

    Google Scholar 

  12. Conner, B. P., Manogharan, G. P., Martof, A. N., Rodomsky, L. M., Rodomsky, C. M., Jordan, D. C., et al. (2014). Making sense of 3-D printing: creating a map of additive manufacturing products and services. Additive Manufacturing, 1, 64–76.

    Google Scholar 

  13. Dalkir, K. (2013). Knowledge management in theory and practice. Cambridge: The MIT Press.

    Google Scholar 

  14. Deppe, C., Lindemann, C., & Koch, R. (2015). Development of an economic decision support for the application of additive manufacturing in aerospace. In 2015 Annual international solid freeform fabrication symposium, Austin, Texas, USA, August 10–12.

  15. Doubrovski, Z., Verlinden, J. C., & Geraedts, J. M (2011). Optimal design for additive manufacturing: Opportunities and challenges. In ASME 2011 international design engineering technical conferences and computers and information in engineering conference (pp. 635–646). August 28–31, 2011. Washington, DC, USA.

  16. Dvorak, F., Micali, M., & Mathieug, M. (2018). Planning and scheduling in additive manufacturing. Inteligencia Artificial, 21(62), 40–52.

    Google Scholar 

  17. Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77(4), 802–813.

    Google Scholar 

  18. Facebook OpenSource (2019). React: A JavaScript library for building user interfaces. Faceook. Retrieved September 10, 2019, from https://reactjs.org/.

  19. Fera, M., Macchiaroli, R., Fruggiero, F., & Lambiase, A. (2018). A new perspective for production process analysis using additive manufacturing—complexity vs production volume. The International Journal of Advanced Manufacturing Technology, 95(1), 673–685. https://doi.org/10.1007/s00170-017-1221-1.

    Article  Google Scholar 

  20. Fontana, F., Klahn, C., & Meboldt, M. (2019). Value-driven clustering of industrial additive manufacturing applications. Journal of Manufacturing Technology Management, 30(2), 366–390.

    Google Scholar 

  21. Francis, J., & Bian, L. (2019). Deep learning for distortion prediction in laser-based additive manufacturing using big data. Manufacturing Letters, 20, 10–14.

    Google Scholar 

  22. Fraunhofer IWU (2017). Design for additive manufacturing-guidelines and case studies for metal applications. Presented in Canadian manufacturing technology show. September 25–28, 2017. Toronto, Canada.

  23. Fuentes, E. (2012). Hip replacement prosthesis. GrabCAD. Retrieved September 10, 2019 from https://grabcad.com/library/hip-replacementprosthesis.

  24. Géron, A. (2019). Hands-on machine learning with scikit-learn, keras, and tensorflow: concepts, tools, and techniques to build intelligent systems. Sebastopol: O’Reilly Media.

    Google Scholar 

  25. Ghani, K. A., Jayabalan, V., & Sugumar, M. (2002). Impact of advanced manufacturing technology on organizational structure. The Journal of High Technology Management Research, 13(2), 157–175.

    Google Scholar 

  26. Hamel, C. M., Roach, D. J., Long, K. N., Demoly, F., Dunn, M. L., & Qi, H. J. (2019). Machine-learning based design of active composite structures for 4D printing. Smart Materials and Structures, 28(6), 065005.

    Google Scholar 

  27. Hartmann, T., Moawad, A., Fouquet, F., Nain, G., Klein, J., Traon, Y. L., et al. (2017). Model-driven analytics: Connecting data, domain knowledge, and learning. arXiv preprint arXiv:1704.01320.

  28. Hasan, S., & Rennie, A. (2008). The application of rapid manufacturing technologies in the spare parts industry. In: Nineteenth annual international solid freeform fabrication (SFF) symposium, August 4–8 2008, Austin, TX, USA.

  29. Hastie, T., Tibshirani, R., Friedman, J., & Franklin, J. (2005). The elements of statistical learning: Data mining, inference and prediction. The Mathematical Intelligencer, 27(2), 83–85.

    Google Scholar 

  30. Holmström, J., Partanen, J., Tuomi, J., & Walter, M. (2010). Rapid manufacturing in the spare parts supply chain: Alternative approaches to capacity deployment. Journal of manufacturing technology management, 21(6), 687–697.

    Google Scholar 

  31. Huang, S. H., Dismukes, J. P., Shi, J., & Su, Q. (2002). Manufacturing system modeling for productivity improvement. Journal of Manufacturing Systems, 21(4), 249.

    Google Scholar 

  32. Huang, S. H., Liu, P., Mokasdar, A., & Hou, L. (2013). Additive manufacturing and its societal impact: A literature review. The International Journal of Advanced Manufacturing Technology, 67(5–8), 1191–1203.

    Google Scholar 

  33. Huang, R., Riddle, M., Graziano, D., Warren, J., Das, S., Nimbalkar, S., et al. (2016). Energy and emissions saving potential of additive manufacturing: The case of lightweight aircraft components. Journal of Cleaner Production, 135, 1559–1570.

    Google Scholar 

  34. ICTC (Information and Communications Technology Council of Canada) (2017). Additive manufacturing in Canada: the impending talent paradigm. Canada Makes. Retrieved September 9, 2019, from https://www.ictc-ctic.ca/wp-content/uploads/2017/07/ICTC-Additive-Manufacturing-ENG-Final.pdf.

  35. Joshi, D., & Ravi, B. (2010). Quantifying the shape complexity of cast parts. Computer-Aided Design and Applications, 7(5), 685–700.

    Google Scholar 

  36. Jurkovic, Z., Cukor, G., Brezocnik, M., & Brajkovic, T. (2018). A comparison of machine learning methods for cutting parameters prediction in high speed turning process. Journal of Intelligent Manufacturing, 29(8), 1683–1693. https://doi.org/10.1007/s10845-016-1206-1.

    Article  Google Scholar 

  37. Kellens, K., Mertens, R., Paraskevas, D., Dewulf, W., & Duflou, J. (2016). Environmental impact of additive manufacturing processes: Does AM contribute to a more sustainable way of part manufacturing? Procedia CIRP, 61, 582–587.

    Google Scholar 

  38. Klahn, C., Leutenecker, B., & Meboldt, M. (2014). Design for additive manufacturing–supporting the substitution of components in series products. Procedia CIRP, 21, 138–143.

    Google Scholar 

  39. Knofius, N., van der Heijden, M. C., & Zijm, W. (2016). Selecting parts for additive manufacturing in service logistics. Journal of Manufacturing Technology Management, 27(7), 915–931.

    Google Scholar 

  40. Knofius, N., van der Heijden, M. C., & Zijm, W. H. (2019). Consolidating spare parts for asset maintenance with additive manufacturing. International Journal of Production Economics, 208, 269–280.

    Google Scholar 

  41. Kruse, A., Reiher, T., & Koch, R. (2017). Integrating AM into existing companies-selection of existing parts for increase of acceptance. In Austin: 28th annual international solid freeform fabrication symposium proceedings (pp. 2575–2585). August 7–9 2017, Austin, Texas, USA.

  42. Laverne, F., Segonds, F., Anwer, N., & Marc, L. (2015). Assembly-based methods to support product innovation in design for additive manufacturing: An exploratory case study. Journal of Mechanical Design, 137(12), 121701.

    Google Scholar 

  43. Leutenecker-Twelsiek, B., Ferchow, J., Klahn, C., & Meboldt, M (2017). The experience transfer model for new technologies-application on design for additive manufacturing. In International conference on additive manufacturing in products and applications (pp. 337–346). September 13–15, Zurich, Switzerland.

  44. Li, Z., Zhang, Z., Shi, J., & Wu, D. (2019). Prediction of surface roughness in extrusion-based additive manufacturing with machine learning. Robotics and Computer-Integrated Manufacturing, 57, 488–495.

    Google Scholar 

  45. Lindemann, C., Reiher, T., Jahnke, U., & Koch, R. (2015). Towards a sustainable and economic selection of part candidates for additive manufacturing. Rapid Prototyping Journal, 21(2), 216–227.

    Google Scholar 

  46. Lovatt, A. M., & Shercliff, H. R. (1998). Manufacturing process selection in engineering design. Part 1: The role of process selection. Materials and Design, 19(5), 205–215. https://doi.org/10.1016/S0261-3069(98)00038-7.

    Article  Google Scholar 

  47. Lu, T. (2016). Towards a fully automated 3D printability checker. In 2016 IEEE International Conference on Industrial Technology (ICIT) (pp. 922–927). March 14–17, Taibei, Taiwan.

  48. Materialise (2014). 3D Print Barometer: 5 parameters that decide the success of your 3D Printing project. Materialise. Retrieved August 29, 2019, from http://3dprintbarometer.com/.

  49. Matos, F., Godina, R., Jacinto, C., Carvalho, H., Ribeiro, I., & Peças, P. (2019). Additive manufacturing: Exploring the social changes and impacts. Sustainability, 11(14), 3757.

    Google Scholar 

  50. Merkt, S., Hinke, C., Schleifenbaum, H., & Voswinckel, H. (2012). Geometric complexity analysis in an integrative technology evaluation model (ITEM) for selective laser melting (SLM). South African Journal of Industrial Engineering, 23(2), 97–105.

    Google Scholar 

  51. Microsoft Azure (2014a). Azure Machine Learning Studio: algorithm and module help. Microsoft Azure. Retrieved September 9, 2019, from https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/.

  52. Microsoft Azure (2014b). Microsoft azure machine learning studio. Microsoft. Retrieved September 7, 2019, from https://studio.azureml.net/.

  53. Miessner, H. (2015). Throttle pedal design challenge. GrabCAD. Retrieved September 9, 2019, from https://grabcad.com/library/pedal-one-microtechnologies-1.

  54. Page, T. D., Yang, S., & Zhao, Y. F (2019). Automated candidate detection for additive manufacturing: a framework proposal. In Proceedings of the design society: international conference on engineering design (pp. 679–688). August 5–8, Delft, The Netherlands.

  55. Paris, H., Mokhtarian, H., Coatanéa, E., Museau, M., & Ituarte, I. F. (2016). Comparative environmental impacts of additive and subtractive manufacturing technologies. CIRP Annals-Manufacturing Technology, 65(1), 29–32.

    Google Scholar 

  56. Patel, L. (2015). What are the main differences between TensorFlow and SciKit Learn? Quora. Retrieved December 10, 2019, https://www.quora.com/What-are-the-main-differences-between-TensorFlow-and-SciKit-Learn.

  57. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12, 2825–2830.

    Google Scholar 

  58. Penumuru, D. P., Muthuswamy, S., & Karumbu, P. (2019). Identification and classification of materials using machine vision and machine learning in the context of industry 4.0. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-019-01508-6.

    Article  Google Scholar 

  59. Priarone, P. C., & Ingarao, G. (2017). Towards criteria for sustainable process selection: On the modelling of pure subtractive versus additive/subtractive integrated manufacturing approaches. Journal of Cleaner Production, 144, 57–68. https://doi.org/10.1016/j.jclepro.2016.12.165.

    Article  Google Scholar 

  60. Raudys, S. J., & Jain, A. K. (1991). Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3, 252–264.

    Google Scholar 

  61. Reiher, T., Lindemann, C., Jahnke, U., Deppe, G., & Koch, R. (2017). Holistic approach for industrializing AM technology: From part selection to test and verification. Progress in Additive Manufacturing. https://doi.org/10.1007/s40964-017-0018-y.

    Article  Google Scholar 

  62. Report, Wohlers. (2018). Additive manufacturing and 3D printing state of the industry: annual worldwide progress report. Colorado: Fort Collins.

    Google Scholar 

  63. Rodríguez, G. G., Gonzalez-Cava, J. M., & Pérez, J. A. M. (2019). An intelligent decision support system for production planning based on machine learning. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-019-01510-y.

    Article  Google Scholar 

  64. Roe, B. P., Yang, H.-J., Zhu, J., Liu, Y., Stancu, I., & McGregor, G. (2005). Boosted decision trees as an alternative to artificial neural networks for particle identification. Nuclear Instruments & Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors, and Associated Equipment, 543(2–3), 577–584.

    Google Scholar 

  65. Rozvany, G. I. (2009). A critical review of established methods of structural topology optimization. Structural and Multidisciplinary Optimization, 37(3), 217–237.

    Google Scholar 

  66. Ryan, G., Pandit, A., & Apatsidis, D. P. J. B. (2006). Fabrication methods of porous metals for use in orthopaedic applications. Biomaterials, 27(13), 2651–2670.

    Google Scholar 

  67. Senvol LLC. (2017). 7 scenarios table to adopt additive manufacturing. Senvol. Retrieved August 29, 2019, from http://senvol.com/additive-manufacturing/7-scenarios-table/.

  68. Tang, Y., Kurtz, A., & Zhao, Y. F. (2015). Bidirectional evolutionary structural optimization (BESO) based design method for lattice structure to be fabricated by additive manufacturing. Computer-Aided Design, 69, 91–101.

    Google Scholar 

  69. Tang, Y., Mak, K., & Zhao, Y. F. (2016a). A framework to reduce product environmental impact through design optimization for additive manufacturing. Journal of Cleaner Production, 137, 1560–1572.

    Google Scholar 

  70. Tang, Y., Yang, S., & Zhao, Y. F. (2016b). Sustainable design for additive manufacturing through functionality integration and part consolidation. In S. S. Muthu & M. M. Savalani (Eds.), Handbook of sustainability in additive manufacturing (pp. 101–144). Singapore: Springer.

    Google Scholar 

  71. Tedia, S., & Williams, C. B. Manufacturability analysis tool for additive manufacturing using voxel-based geometric modeling. In 27th annual international solid freeform fabrication (SFF) symposium (pp. 3–22). August 8–10 2016, Austin, TX, USA.

  72. TensorFlow (2020). An end-to-end open source machine learning platform. TensorFlow Org. Retrieved January 20, 2020, from https://www.tensorflow.org/.

  73. Thomas, D. (2016). Costs, benefits, and adoption of additive manufacturing: a supply chain perspective. The International Journal of Advanced Manufacturing Technology, 85(5–8), 1857–1876.

    Google Scholar 

  74. Thompson, M. K., Moroni, G., Vaneker, T., Fadel, G., Campbell, R. I., Gibson, I., et al. (2016). Design for additive manufacturing: Trends, opportunities, considerations, and constraints. CIRP Annals-Manufacturing Technology, 65(2), 737–760.

    Google Scholar 

  75. Tuck, C. J., Hague, R. J., Ruffo, M., Ransley, M., & Adams, P. (2008). Rapid manufacturing facilitated customization. International Journal of Computer Integrated Manufacturing, 21(3), 245–258.

    Google Scholar 

  76. Valentan, B., Brajlih, T., Drstvensek, I., & Balic, J. (2008). Basic solutions on shape complexity evaluation of STL data. Journal of Achievements in Materials and Manufacturing Engineering, 26(1), 73–80.

    Google Scholar 

  77. Watson, J. K., & Taminger, K. M. B. (2015). A decision-support model for selecting additive manufacturing versus subtractive manufacturing based on energy consumption. Journal of Cleaner Production, 176, 1316–1322. https://doi.org/10.1016/j.jclepro.2015.12.009.

    Article  Google Scholar 

  78. Xia, Y., Liu, C., Li, Y., & Liu, N. (2017). A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Systems with Applications, 78, 225–241.

    Google Scholar 

  79. Xometry (2017). Instant quoting add-in for SOLIDWORKS and Autodesk Inventor. Retrieved September 7, 2019, from https://www.xometry.com/cad-add-in-downloads.

  80. Yang, S., Min, W., Ghibaudo, J., & Zhao, Y. F. (2019a). Understanding the sustainability potential of part consolidation design supported by additive manufacturing. Journal of Cleaner Production, 232, 722–738.

    Google Scholar 

  81. Yang, S., Page, T., & Zhao, Y. F. (2019b). Understanding the role of additive manufacturing knowledge in stimulating design innovation for novice designers. Journal of Mechanical Design, 141(2), 021703.

    Google Scholar 

  82. Yang, S., Santoro, F., Sulthan, M. A., & Zhao, Y. F. (2019c). A numerical-based part consolidation candidate detection approach with modularization considerations. Research in Engineering Design, 30(1), 63–83. https://doi.org/10.1007/s00163-018-0298-3.

    Article  Google Scholar 

  83. Yang, S., Santoro, F., & Zhao, Y. F. (2018). Towards a numerical approach of finding candidates for additive manufacturing-enabled part consolidation. Journal of Mechanical Design, 140(4), 041701–041713. https://doi.org/10.1115/1.4038923.

    Article  Google Scholar 

  84. Yang, S., & Zhao, Y. F. (2018). Additive manufacturing-enabled part count reduction: a lifecycle perspective. Journal of Mechanical Design, 140(3), 031702–031712. https://doi.org/10.1115/1.4038922.

    Article  Google Scholar 

  85. Yang, W. A. (2016). Simultaneous monitoring of mean vector and covariance matrix shifts in bivariate manufacturing processes using hybrid ensemble learning-based model. Journal of Intelligent Manufacturing, 27(4), 845–874. https://doi.org/10.1007/s10845-014-0920-9.

    Article  Google Scholar 

  86. Yao, X., Moon, S. K., & Bi, G. (2017). A hybrid machine learning approach for additive manufacturing design feature recommendation. Rapid Prototyping Journal, 23(6), 983–997.

    Google Scholar 

  87. Zhang, Y., Jedeck, S., Yang, L., & Bai, L. (2018). Modeling and analysis of the on-demand spare parts supply using additive manufacturing. Rapid Prototyping Journal. https://doi.org/10.1108/RPJ-01-2018-0027.

    Article  Google Scholar 

Download references

Acknowledgements

Financial support from the National Sciences and Engineering Research Council of Canada (Discovery Grant RGPIN 436055-2013); and McGill Engineering Doctoral Award (MEDA) is acknowledged with gratitude.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Yaoyao Fiona Zhao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yang, S., Page, T., Zhang, Y. et al. Towards an automated decision support system for the identification of additive manufacturing part candidates. J Intell Manuf 31, 1917–1933 (2020). https://doi.org/10.1007/s10845-020-01545-6

Download citation

Keywords

  • Additive manufacturing
  • Machine learning
  • Candidate identification
  • Conceptual design