Skip to main content
SpringerLink
Log in

Research on adaptive slicing method for optimizing STL model manufacturing details

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

This paper proposes an adaptive slicing method for optimizing STL model manufacturing details, addressing the issue of traditional slicing methods being unable to accurately identify model details in 3D printing. The method utilizes the angle difference between the normal vectors of intersecting triangular facets of neighboring slice planes and the print direction and it sets an angle variation threshold to determine the presence of vertical sharp corners in the current slice. Additionally, the method employs the combined triangle method to calculate the contour curve area of the current slice plane, using an area variation threshold to determine the presence of parallel sharp corners. For slices without model details, the slice thickness is determined using an adaptive slicing method based on triangular plane normal vectors. For slices with model details, the final slice thickness is chosen as the minimum value obtained from the two discriminatory bases mentioned above. Based on MATLAB simulation experiment results demonstrate that the adaptive slicing method of this paper improves the model manufacturing accuracy by 19.3% and the estimated printing time only increases by 4 min, which compared to the adaptive slicing method based on triangular facet normal vectors. The adaptive slicing method of this paper ensures both manufacturing efficiency and accuracy, which effectively preserves the model manufacturing details.

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

  1. He Z, Yang D, Jiang C, Wu Y, Jing H (2022) Strength-constrained topology optimization based on additive manufacturing anisotropy. China Mech Eng 33(19):2372-2380 +2393

    Google Scholar 

  2. Zheng X, Cheng K, Zhou X, Lin J, Jiang X (2018) An adaptive direct slicing method based on tilted voxel of two-photon polymerization. Int J Adv Manuf Technol 96:521–530

    Article  Google Scholar 

  3. Abdulhameed O, Al-Ahmari A, Ameen W, Mian S (2019) Additive manufacturing: challenges, trends, and applications. Adv Mech Eng 11(2):1687814018822880

    Article  Google Scholar 

  4. Bai R, Liang G, Naceur H, Coutellier D, Zhao J, Yi J, Luo J, Wang L, Pu H (2023) Influence of the advanced joint path strategies on the energy absorption capacity of Ti-6Al-4V Taylor bar based on additive manufacturing. J Therm Stresses 46(2):140–162

    Article  Google Scholar 

  5. Pu H, Liang G, Naceur H, Zhao J, Yi J, Luo J, Coutellier D, Wang L, Bai R (2023) Thermo-mechanical analysis of Ti-6Al-4V Taylor bar using advanced joint path strategies based on additive manufacturing. CIRP J Manuf Sci Technol 40:167–179

    Article  Google Scholar 

  6. Bai R, Pu H, Liang G, Naceur H, Coutellier D, Du Y, Zhao J, Yi J, Li X, Yuan S, Luo J, Lin J (2023) Exact forming for additive manufacturing using an irregular element-based compensating approach: simulation, experiment, and detection. Mech Adv Mater Struct 1–12. https://doi.org/10.1080/15376494.2023.2246191

  7. Wang X, Cao J, Cao Y (2023) A new multi-objective optimization adaptive layering algorithm for 3D printing based on demand-oriented. Rapid Prototyp J 29(2):246–258

    Article  Google Scholar 

  8. Zhu J, Zhou H, Wang C, Zhou L, Yuan S, Zhang W (2021) A review of topology optimization for additive manufacturing: status and challenges. Chin J Aeronaut 34(1):91–110

    Article  Google Scholar 

  9. Wang C, Hao Z (2014) The uniform thickness hierarchical algorithm of rapid prototyping technology STL model. Mach Des Manufacture 278(04):133–136

    Google Scholar 

  10. Chen Q, Xu J, Zhang S (2021) Cylindricity and flatness optimization for mechanical parts in additive manufacturing based on tolerance adaptive slicing. Int J Adv Manuf Technol 115:3839–3857

    Article  Google Scholar 

  11. Hu Y, Jiang X, Huo G, Su C, Li H, Zheng Z (2022) A novel adaptive slicing algorithm based on ameliorative area ratio and accurate cusp height for 3D printing. Rapid Prototyp J 28(3):453–465

    Article  Google Scholar 

  12. Lv N, Ouyang X, Qiao Y (2022) Adaptive layering algorithm for FDM-3D printing based on optimal volume error. Micromachines 13(6):836

    Article  Google Scholar 

  13. Lin J, Wang Y, Jing X, Gu Y (2018) Research on Adaptive Slicing Algorithm for STL Model in Additive Manufacturing. Mech Des Manuf (2):51–53+57. https://doi.org/10.19356/j.cnki.1001-3997.2018.02.015

  14. Tian R, Liu S, Zhang Y (2019) Research on adaptive layering algorithm of triangular facet normal vector of STL model in additive manufacturing. Mech Sci Technol Aerosp Eng 38(03):415–421

    Google Scholar 

  15. Wang X, Li S, Ma W, Yang L (2019) Improvement and experimental investigation of adaptive slicing method in fused deposition forming. Mech Sci Technol Aerosp Eng 38(08):1231–1238

    Google Scholar 

  16. Yi Y, Li Y, Liu B, Yuan Y, Li Y, Zhang X, Xie R, Li Z (2023) Adaptive layering algorithm for 3D printing with stereolithography model feature details retained. J Xi’an Jiaotong Univ 57(08):105–114

    Google Scholar 

  17. Ju Q, Wang L (2017) An algorithm for point cloud data reduction based on adaptive slicing. Geotech Investigation Surv 45(09):62–66

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Mechanical Engineering, Shanghai Institute of Technology, Shanghai, 200235, China

    Yan Wu, Xiaoshuai Chen, Chaoqun He, Jiale Hu & Shizhan Han

  2. Shanghai Engineering Research Center of Physical Vapor Deposition (PVD) Superhard Coating and Equipment, Shanghai Institute of Technology, Shanghai, 200235, China

    Yan Wu

  3. College of Engineering Innovation, Shanghai Institute of Technology, Shanghai, 200235, China

    Ruijin Sun

Authors
  1. Yan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoshuai Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruijin Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chaoqun He
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiale Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shizhan Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the conception and design of the study. W. Y. directed the whole process of manuscript completion. S. R. provided guidance on algorithm testing. Material preparation, data collection, and analysis were conducted by C. X., H. C., and H. J. The first draft of the manuscript was written by C. X., and all the authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xiaoshuai Chen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Y., Chen, X., Sun, R. et al. Research on adaptive slicing method for optimizing STL model manufacturing details. Int J Adv Manuf Technol 130, 4459–4468 (2024). https://doi.org/10.1007/s00170-024-13007-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-024-13007-x

Keywords

Search

Navigation