Skip to main content
SpringerLink
Log in

Development of Data-Driven In-Situ Monitoring and Diagnosis System of Fused Deposition Modeling (FDM) Process Based on Support Vector Machine Algorithm

  • Regular Paper
  • Published:
International Journal of Precision Engineering and Manufacturing-Green Technology Aims and scope Submit manuscript

Abstract

Fused deposition modeling (FDM), one of representative additive manufacturing (AM) technologies, has been widely used for fabricating functional parts with geometrical complexity. However, it has suffered from degraded part quality and low process reliability and controllability. Therefore, it is of much significance to develop a monitoring and diagnosis system for the FDM process to overcome such drawbacks. In this paper, a data-driven FDM process monitoring and diagnosis system is developed by using two types of sensors – an accelerometer and an acoustic emission (AE) sensor. A large number of experimental data, collected from the accelerometers and AE sensor under healthy and faulty process states, are processed to obtain a critical feature – a root mean square (RMS). The RMS values are then used for training the FDM process monitoring and diagnosis models based on a support vector machine (SVM) algorithm and a k-fold cross validation approach. In particular, the SVM-based models for the odd- and evennumbered layers of one FDM specimen are developed. For a real-time validation in a factory floor, the non-linear SVM-based models using the acceleration signals are used for the software development. The diagnosis accuracy is better than 87.5%, and an applicability of the models is verified.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Wong, K. V. and Hernandez, A., “A Review of Additive Manufacturing,” ISRN Mechanical Engineering, Vol. 2012, Article ID.208760, 2012.

  2. Gibson, I., Rosen, D., and Stucker, B., “Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing,” Springer, 2014.

    Google Scholar 

  3. Huang, Y., Leu, M. C., Mazumder, J., and Donmez, A., “Additive Manufacturing: Current State, Future Potential, Gaps and Needs, and Recommendations,” Journal of Manufacturing Science and Engineering, Vol. 137, No. 1, Paper No. 014001, 2015.

    Article  Google Scholar 

  4. Peng, T. and Chen, C., “Influence of Energy Density on Energy Demand and Porosity of 316l Stainless Steel Fabricated by Selective Laser Melting,” International Journal of Precision Engineering and Manufacturing-Green Technology, Vol. 5, No. 1, pp. 55–62, 2018.

    Article  Google Scholar 

  5. Lee, H.-J., Song, J.-G., and Ahn, D.-G., “Investigation into the Influence of Feeding Parameters on the Formation of the FEDPowder Layer in a Powder Bed Fusion (PBF) System,” International Journal of Precision Engineering and Manufacturing, Vol. 18, No. 4, pp. 613–621, 2017.

    Article  Google Scholar 

  6. Kuo, C., Su, C., and Chiang, A., “Parametric Optimization of Density and Dimensions in Three-Dimensional Printing of Ti-6Al-4V Powders on Titanium Plates Using Selective Laser Melting,” International Journal of Precision Engineering and Manufacturing, Vol. 18, No. 11, pp. 1609–1618, 2017.

    Article  Google Scholar 

  7. Lee, H.-J., Ahn, D.-G., Song, J.-G., Kim, J.-S., and Kang, E. G., “Fabrication of Beads Using a Plasma Electron Beam and Stellite21 Powders for Additive Manufacturing,” International Journal of Precision Engineering and Manufacturing-Green Technology, Vol. 4, No. 4, pp. 453–456, 2017.

    Article  Google Scholar 

  8. Durgun, I. and Ertan, R., “Experimental Investigation of FDM Process for Improvement of Mechanical Properties and Production Cost,” Rapid Prototyping Journal, Vol. 20, No. 3, pp. 228–235, 2014.

    Article  Google Scholar 

  9. Ivanova, O., Williams, C., and Campbell, T., “Additive Manufacturing (AM) and Nanotechnology: Promises and Challenges,” Rapid Prototyping Journal, Vol. 19, No. 5, pp. 353–364, 2013.

    Article  Google Scholar 

  10. Dimitrov, D., Schreve, K., and De Beer, N., “Advances in Three Dimensional Printing-State of the Art and Future Perspectives,” Rapid Prototyping Journal, Vol. 12, No. 3, pp. 136–147, 2006.

    Article  Google Scholar 

  11. Lee, J., Kim, H.-C., Choi, J.-W., and Lee, I. H., “A Review on 3D Printed Smart Devices for 4D Printing,” International Journal of Precision Engineering and Manufacturing-Green Technology, Vol. 4, No. 3, pp. 373–383, 2017.

    Article  Google Scholar 

  12. Jo, K.-H., Jeong, Y.-S., Lee, J.-H., and Lee, S.-H., “A Study of Post-Processing Methods for Improving the Tightness of a Part Fabricated by Fused Deposition Modeling,” International Journal of Precision Engineering and Manufacturing, Vol. 17, No. 11, pp. 1541–1546, 2016.

    Article  Google Scholar 

  13. Zaldivar, R., Witkin, D., McLouth, T., Patel, D., Schmitt, K., et al., “Influence of Processing and Orientation Print Effects on the Mechanical and Thermal Behavior of 3D-Printed ULTEM® 9085 Material,” Additive Manufacturing, Vol. 13, pp. 71–80, 2017.

    Article  Google Scholar 

  14. Ly, S. T. and Kim, J. Y., “4D Printing-Fused Deposition Modeling Printing with Thermal-Responsive Shape Memory Polymers,” International Journal of Precision Engineering and Manufacturing-Green Technology, Vol. 4, No. 3, pp. 267–272, 2017.

    Article  MathSciNet  Google Scholar 

  15. Conner, B. P., Manogharan, G. P., Martof, A. N., Rodomsky, L. M., Rodomsky, C. M., et al., “Making Sense of 3-D Printing: Creating a Map of Additive Manufacturing Products and Services,” Additive Manufacturing, Vol. 1, pp. 64–76, 2014.

    Article  Google Scholar 

  16. Shin, D.-G., Kim, T.-H., and Kim, D.-E., “Review of 4D Printing Materials and Their Properties,” International Journal of Precision Engineering and Manufacturing-Green Technology, Vol. 4, No. 3, pp. 349–357, 2017.

    Article  Google Scholar 

  17. Zhang, S.-U., Han, J., and Kang, H.-W., “Temperature-Dependent Mechanical Properties of ABS Parts Fabricated by Fused Deposition Modeling and Vapor Smoothing,” International Journal of Precision Engineering and Manufacturing, Vol. 18, No. 5, pp. 763–769, 2017.

    Article  Google Scholar 

  18. Vahabli, E. and Rahmati, S., “Application of an RBF Neural Network for FDM Parts’ Surface Roughness Prediction for Enhancing Surface Quality,” International Journal of Precision Engineering and Manufacturing, Vol. 17, No. 12, pp. 1589–1603, 2016.

    Article  Google Scholar 

  19. Yoon, J., He, D., and Van Hecke, B., “A PHM Approach to Additive Manufacturing Equipment Health Monitoring, Fault Diagnosis, and Quality Control,” Proc. of the Prognostics and Health Management Society Conference, pp. 1–9, 2014.

    Google Scholar 

  20. Rao, P. K., Liu, J. P., Roberson, D., and Kong, Z. J., “Sensor-Based Online Process Fault Detection in Additive Manufacturing,” Proc. of the ASME International Manufacturing Science and Engineering Conference, Paper No. V002T04A010, 2015.

    Google Scholar 

  21. Wu, H., Yu, Z., and Wang, Y., “A New Approach for Online Monitoring of Additive Manufacturing Based on Acoustic Emission,” Proc. of the ASME 11th International Manufacturing Science and Engineering Conference, Paper No. V003T008A013, 2016.

    Google Scholar 

  22. Ultimaker, “Ultimaker Troubleshooting,” https://doi.org/ultimaker.com/en/resources/troubleshooting (Accessed 6 JUL 2018)

  23. Wang, L. and Gao, R. X., “Condition Monitoring and Control for Intelligent Manufacturing,” Springer Science & Business Media, pp. 1–399, 2006.

    Google Scholar 

  24. Wang, L., “Support Vector Machines: Theory and Applications,” Springer Science & Business Media, Vol. 177, pp. 1–432, 2005.

    Google Scholar 

  25. Cristianini, N. and Shawe-Taylor, J., “An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods,” Cambridge University Press, 2000.

    Google Scholar 

  26. Amari, S.-I. and Wu, S., “Improving Support Vector Machine Classifiers by Modifying Kernel Functions,” Neural Networks, Vol. 12, No. 6, pp. 783–789, 1999.

    Article  Google Scholar 

  27. Anguita, D., Ridella, S., and Rivieccio, F., “K-Fold Generalization Capability Assessment for Support Vector Classifiers,” Proc. of the International Joint Conference on Neural Networks, pp. 855–858, 2005.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Graduate School, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of Korea

    Jung Sub Kim & Chang Su Lee

  2. School of Mechanical Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of Korea

    Sung-Min Kim & Sang Won Lee

Authors
  1. Jung Sub Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chang Su Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sung-Min Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sang Won Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sang Won Lee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, J.S., Lee, C.S., Kim, SM. et al. Development of Data-Driven In-Situ Monitoring and Diagnosis System of Fused Deposition Modeling (FDM) Process Based on Support Vector Machine Algorithm. Int. J. of Precis. Eng. and Manuf.-Green Tech. 5, 479–486 (2018). https://doi.org/10.1007/s40684-018-0051-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40684-018-0051-4

Keywords

Search

Navigation