Skip to main content
SpringerLink
Log in

Machine learning integrated design for additive manufacturing

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

For improving manufacturing efficiency and minimizing costs, design for additive manufacturing (AM) has been accordingly proposed. The existing design for AM methods are mainly surrogate model based. Due to the increasingly available data nowadays, machine learning (ML) has been applied to medical diagnosis, image processing, prediction, classification, learning association, etc. A variety of studies have also been carried out to use machine learning for optimizing the process parameters of AM with corresponding objectives. In this paper, a ML integrated design for AM framework is proposed, which takes advantage of ML that can learn the complex relationships between the design and performance spaces. Furthermore, the primary advantage of ML over other surrogate modelling methods is the capability to model input–output relationships in both directions. That is, a deep neural network can model property–structure relationships, given structure–property input–output data. A case study was carried out to demonstrate the effectiveness of using ML to design a customized ankle brace that has a tunable mechanical performance with tailored stiffness.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Baturynska, I. (2019). Application of machine learning techniques to predict the mechanical properties of polyamide 2200 (PA12) in additive manufacturing. Applied Sciences, 9(6), 1060. https://doi.org/10.3390/app9061060.

    Article  Google Scholar 

  • Baturynska, I., Semeniuta, O., & Wang, K. (2019). Application of machine learning methods to improve dimensional accuracy in additive manufacturing. In Lecture notes in electrical engineering (Vol. 484, pp. 245–252). Springer. https://doi.org/10.1007/978-981-13-2375-1_31.

  • Chen, H., & Zhao, Y. F. (2015). Learning algorithm based modeling and process parameters recommendation system for binder jetting additive manufacturing process. In Proceedings of the ASME design engineering technical conference (Vol. 1A–2015). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/detc2015-47627.

  • Chen, J., Siegler, S., & Schneck, C. D. (1988). The three-dimensional kinematics and flexibility characteristics of the human ankle and subtalar joint-part II: Flexibility characteristics. Journal of Biomechanical Engineering, 110(4), 374–385. https://doi.org/10.1115/1.3108456.

    Article  Google Scholar 

  • Choi, H., McDowell, D. L., Allen, J. K., Rosen, D., & Mistree, F. (2008). An inductive design exploration method for robust multiscale materials design. Journal of Mechanical Design, Transactions of the ASME. https://doi.org/10.1115/1.2829860.

    Article  Google Scholar 

  • Chowdhury, S., & Anand, S. (2016). Artificial neural network based geometric compensation for thermal deformation in additive manufacturing processes. In Volume 3: Joint MSEC-NAMRC symposia (p. V003T08A006). Retrieved April 10, 2018, from http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?doi=10.1115/MSEC2016-8784.

  • Gibson, I., Rosen, D. W., & Stucker, B. (2010). Design for additive manufacturing. In I. Gibson, D. W. Rosen, & B. Stucker (Eds.), Additive manufacturing technologies (pp. 299–332). Boston, MA: Springer. https://doi.org/10.1007/978-1-4419-1120-9_11.

    Chapter  Google Scholar 

  • Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., et al. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354–377. https://doi.org/10.1016/j.patcog.2017.10.013.

    Article  Google Scholar 

  • He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In The IEEE international conference on computer vision (ICCV) (pp. 1026–1034).

  • Huang, J., Chen, Q., Jiang, H., Zou, B., Li, L., Liu, J., et al. (2020). A survey of design methods for material extrusion polymer 3D printing. Virtual and Physical Prototyping, 15(2), 148–162. https://doi.org/10.1080/17452759.2019.1708027.

    Article  Google Scholar 

  • Jang, K. I., Chung, H. U., Xu, S., Lee, C. H., Luan, H., Jeong, J., et al. (2015). Soft network composite materials with deterministic and bio-inspired designs. Nature Communications, 6(1), 1–11. https://doi.org/10.1038/ncomms7566.

    Article  Google Scholar 

  • Jiang, J., Hu, G., Li, X., Xu, X., Zheng, P., & Stringer, J. (2019a). Analysis and prediction of printable bridge length in fused deposition modelling based on back propagation neural network. Virtual and Physical Prototyping, 14(3), 253–266. https://doi.org/10.1080/17452759.2019.1576010.

    Article  Google Scholar 

  • Jiang, J., Weng, F., Gao, S., Stringer, J., Xu, X., & Guo, P. (2019b). A support interface method for easy part removal in direct metal deposition. Manufacturing Letters, 20, 30–33. https://doi.org/10.1016/j.mfglet.2019.04.002.

    Article  Google Scholar 

  • Jiang, J., Xu, X., & Stringer, J. (2018a). Support structures for additive manufacturing: A review. Journal of Manufacturing and Materials Processing, 2(4), 64. https://doi.org/10.3390/jmmp2040064.

    Article  Google Scholar 

  • Jiang, J., Xu, X., & Stringer, J. (2018b). A new support strategy for reducing waste in additive manufacturing. In The 48th international conference on computers and industrial engineering (CIE 48) (pp. 1–7). Auckland.

  • Jiang, J., Xu, X., & Stringer, J. (2019c). Effect of extrusion temperature on printable threshold overhang in additive manufacturing. In CIRP manufacturing systems conference 2019. Ljubljana.

  • Jiang, J., Xu, X., & Stringer, J. (2019d). Optimisation of multi-part production in additive manufacturing for reducing support waste. Virtual and Physical Prototyping, 14(3), 219–228. https://doi.org/10.1080/17452759.2019.1585555.

    Article  Google Scholar 

  • Jiang, J., Xu, X., & Stringer, J. (2019e). Optimization of process planning for reducing material waste in extrusion based additive manufacturing. Robotics and Computer-Integrated Manufacturing, 59, 317–325. https://doi.org/10.1016/j.rcim.2019.05.007.

    Article  Google Scholar 

  • Jin, K. H., McCann, M. T., Froustey, E., & Unser, M. (2017). Deep convolutional neural network for inverse problems in imaging. IEEE Transactions on Image Processing, 26(9), 4509–4522. https://doi.org/10.1109/TIP.2017.2713099.

    Article  Google Scholar 

  • Khanzadeh, M., Rao, P., Jafari-Marandi, R., Smith, B. K., Tschopp, M. A., & Bian, L. (2018). Quantifying geometric accuracy with unsupervised machine learning: Using self-organizing map on fused filament fabrication additive manufacturing parts. Journal of Manufacturing Science and Engineering, Transactions of the ASME. https://doi.org/10.1115/1.4038598.

    Article  Google Scholar 

  • Kim, S., Rosen, D. W., Witherell, P., & Ko, H. (2019). A design for additive manufacturing ontology to support manufacturability analysis. Journal of Computing and Information Science in Engineering. https://doi.org/10.1115/1.4043531.

    Article  Google Scholar 

  • Lao, W., Li, M., Wong, T. N., Tan, M. J., & Tjahjowidodo, T. (2020). Improving surface finish quality in extrusion-based 3D concrete printing using machine learning-based extrudate geometry control. Virtual and Physical Prototyping. https://doi.org/10.1080/17452759.2020.1713580.

    Article  Google Scholar 

  • Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11–26. https://doi.org/10.1016/j.neucom.2016.12.038.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.rcim.2019.01.004.

    Article  Google Scholar 

  • Lu, Z. L., Li, D. C., Lu, B. H., Zhang, A. F., Zhu, G. X., & Pi, G. (2010). The prediction of the building precision in the laser engineered net shaping process using advanced networks. Optics and Lasers in Engineering, 48(5), 519–525. https://doi.org/10.1016/j.optlaseng.2010.01.002.

    Article  Google Scholar 

  • Mahapatra, S. S., & Sood, A. K. (2012). Bayesian regularization-based Levenberg–Marquardt neural model combined with BFOA for improving surface finish of FDM processed part. International Journal of Advanced Manufacturing Technology, 60(9–12), 1223–1235. https://doi.org/10.1007/s00170-011-3675-x.

    Article  Google Scholar 

  • Malak, R. J., Aughenbaugh, J. M., & Paredis, C. J. J. (2009). Multi-attribute utility analysis in set-based conceptual design. CAD Computer Aided Design, 41(3), 214–227. https://doi.org/10.1016/j.cad.2008.06.004.

    Article  Google Scholar 

  • Matthews, J., Klatt, T., Morris, C., Seepersad, C. C., Haberman, M., & Shahan, D. (2016). Hierarchical design of negative stiffness metamaterials using a Bayesian network classifier. Journal of Mechanical Design, Transactions of the ASME. https://doi.org/10.1115/1.4032774.

    Article  Google Scholar 

  • Mohamed, O. A., Masood, S. H., & Bhowmik, J. L. (2017). Influence of processing parameters on creep and recovery behavior of FDM manufactured part using definitive screening design and ANN. Rapid Prototyping Journal, 23(6), 998–1010. https://doi.org/10.1108/RPJ-12-2015-0198.

    Article  Google Scholar 

  • Morris, C., Bekker, L., Haberman, M. R., & Seepersad, C. C. (2018). Design exploration of reliably manufacturable materials and structures with applications to negative stiffness metamaterials and microstereolithography. Journal of Mechanical Design, Transactions of the ASME. https://doi.org/10.1115/1.4041251.

    Article  Google Scholar 

  • Negi, S., & Sharma, R. K. (2016). Study on shrinkage behaviour of laser sintered PA 3200GF specimens using RSM and ANN. Rapid Prototyping Journal, 22(4), 645–659. https://doi.org/10.1108/RPJ-08-2014-0090.

    Article  Google Scholar 

  • Pacheco, J. E., Amon, C. H., & Finger, S. (2003). Bayesian surrogates applied to conceptual stages of the engineering design process. Journal of Mechanical Design, Transactions of the ASME, 125(4), 664–672. https://doi.org/10.1115/1.1631580.

    Article  Google Scholar 

  • Rosen, D. W. (2007). Computer-aided design for additive manufacturing of cellular structures. Computer-Aided Design and Applications, 4(5), 585–594. https://doi.org/10.1080/16864360.2007.10738493.

    Article  Google Scholar 

  • Rosen, D. W. (2014). Research supporting principles for design for additive manufacturing. Virtual and Physical Prototyping, 9(4), 225–232. https://doi.org/10.1080/17452759.2014.951530.

    Article  Google Scholar 

  • Samuel, A. L. (1959). Some studies in machine learning using the game of checkers—IBM journals & magazine. IBM Journal of Research and Development, 3(3), 210–229.

    Article  Google Scholar 

  • Shahan, D. W., & Seepersad, C. C. (2012). Bayesian network classifiers for set-based collaborative design. Journal of Mechanical Design, Transactions of the ASME. https://doi.org/10.1115/1.4006323.

    Article  Google Scholar 

  • Siegler, S., Chen, J., & Schneck, C. D. (1988). The three-dimensional kinematics and flexibility characteristics of the human ankle and subtalar joints—Part I: Kinematics. Journal of Biomechanical Engineering, 110(4), 364–373. https://doi.org/10.1115/1.3108455.

    Article  Google Scholar 

  • Thiele, F., Schuhmacher, S., Schwaller, C., Plüss, S., Rhiner, J., List, R., et al. (2018). Restrictions in the ankle sagittal- and frontal-plane range of movement during simulated walking with different types of orthoses. Journal of Functional Morphology and Kinesiology, 3(2), 21. https://doi.org/10.3390/jfmk3020021.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.cirp.2016.05.004.

    Article  Google Scholar 

  • Tootooni, M. S., Dsouza, A., Donovan, R., Rao, P. K., Kong, Z. J., & Borgesen, P. (2017). Classifying the dimensional variation in additive manufactured parts from laser-scanned three-dimensional point cloud data using machine learning approaches. Journal of Manufacturing Science and Engineering, Transactions of the ASME. https://doi.org/10.1115/1.4036641.

    Article  Google Scholar 

  • Unal, M., Miller, S. W., Chhabra, J. P. S., Warn, G. P., Yukish, M. A., & Simpson, T. W. (2017). A sequential decision process for the system-level design of structural frames. Structural and Multidisciplinary Optimization, 56(5), 991–1011. https://doi.org/10.1007/s00158-017-1697-1.

    Article  Google Scholar 

  • Vijayaraghavan, V., Garg, A., Lam, J. S. L., Panda, B., & Mahapatra, S. S. (2015). Process characterisation of 3D-printed FDM components using improved evolutionary computational approach. International Journal of Advanced Manufacturing Technology, 78(5–8), 781–793. https://doi.org/10.1007/s00170-014-6679-5.

    Article  Google Scholar 

  • Wei, H., Shirinzadeh, B., Niu, X., Zhang, J., Li, W., & Simeone, A. (2020). Study of the hinge thickness deviation for a 316L parallelogram flexure mechanism fabricated via selective laser melting. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01621-x.

    Article  Google Scholar 

  • Weiss, L. E., Amon, C. H., Finger, S., Miller, E. D., Romero, D., Verdinelli, I., et al. (2005). Bayesian computer-aided experimental design of heterogeneous scaffolds for tissue engineering. CAD Computer Aided Design, 37(11), 1127–1139. https://doi.org/10.1016/j.cad.2005.02.004.

    Article  Google Scholar 

  • Xiong, J., Zhang, G., Hu, J., & Wu, L. (2014). Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. Journal of Intelligent Manufacturing, 25(1), 157–163. https://doi.org/10.1007/s10845-012-0682-1.

    Article  Google Scholar 

  • Xiong, J., Zhang, Y., & Pi, Y. (2020). Control of deposition height in WAAM using visual inspection of previous and current layers. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01634-6.

    Article  Google Scholar 

  • Xiong, Y., Duong, P. L. T., Wang, D., Park, S. I., Ge, Q., Raghavan, N., et al. (2019). Data-driven design space exploration and exploitation for design for additive manufacturing. Journal of Mechanical Design, Transactions of the ASME. https://doi.org/10.1115/1.4043587.

    Article  Google Scholar 

  • Zhang, W., Mehta, A., Desai, P. S., & Fred Higgs III, C. (2017). Machine learning enabled powder spreading process map for metal additive manufacturing (AM). In Solid freeform fabrication 2017: Proceedings of the 28th annual international solid freeform fabrication symposium (pp. 1235–1249).

  • Zhao, Y., Sun, J., Gupta, M. M., Moody, W., Laverty, W. H., & Zhang, W. (2017). Developing a mapping from affective words to design parameters for affective design of apparel products. Textile Research Journal, 87(18), 2224–2232. https://doi.org/10.1177/0040517516669072.

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by Digital Manufacturing and Design (DManD) Research Center at the Singapore University of Technology and Design supported by the Singapore National Research Foundation.

Author information

Authors and Affiliations

  1. Digital Manufacturing and Design Center, Singapore University of Technology and Design, Singapore, Singapore

    Jingchao Jiang, Yi Xiong, Zhiyuan Zhang & David W. Rosen

  2. The G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    David W. Rosen

Authors
  1. Jingchao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiyuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David W. Rosen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David W. Rosen.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, J., Xiong, Y., Zhang, Z. et al. Machine learning integrated design for additive manufacturing. J Intell Manuf 33, 1073–1086 (2022). https://doi.org/10.1007/s10845-020-01715-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-020-01715-6

Keywords

Search

Navigation