Skip to main content

Advertisement

SpringerLink
Log in

A novel multi-objective optimization of 3D printing adaptive layering algorithm based on improved NSGA-II and fuzzy set theory

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

Abstract

Uniform equal thickness layering is widely used in 3D printing, which cannot take into account the printing quality and printing efficiency. In this paper, a new adaptive layering algorithm based on multi-objective optimization is proposed for this problem. The algorithm comprehensively considers the surface features of the model, the slope and curvature of the contour, and establishes a multi-objective optimization model with print quality, print time, and feature constraints. And the Pareto optimal solution set of multi-objective optimization is solved by the improved non-dominated sorting genetic algorithm-II (NSGA-II), and the Pareto optimal solution that meets different printing requirements is selected by the Fuzzy-based weighted membership ranking method. Through comparative experiments, the method proposed in this paper reduces the volume error rate by 40.9% and the printing time by 33.3% compared with uniform layering, which can effectively improve the printing quality and printing efficiency. In addition, compared with the existing adaptive layering algorithms, it is also an algorithm with good comprehensive performance.

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

Similar content being viewed by others

References

  1. Gao W, Zhang Y, Ramanujan D, Ramani K, Chen Y, Williams CB, Wang C, Shin YC, Zhang S, Zavattieri PD (2015) The status, challenges, and future of additive manufacturing in engineering. Comput Aided Des 69:65–89

    Article  Google Scholar 

  2. Xu J, Gu X, Ding D, Pan Z, Chen K (2018) A review of slicing methods for directed energy deposition based additive manufacturing. Rapid Prototyp J

  3. Mao H, Kwok TH, Chen Y, Wang C (2019) Adaptive slicing based on efficient profile analysis. Comput Aided Des 107:89–101

    Article  Google Scholar 

  4. Dolenc A, Mäkelä I (1994) Slicing procedures for layered manufacturing techniques. Comput Aided Des 26(2):119–126

    Article  Google Scholar 

  5. Pandey PM, Reddy NV, Dhande SG (2003) Real time adaptive slicing for fused deposition modelling. Int J Mach Tools Manuf 43(1):61–71

    Article  Google Scholar 

  6. Huang B, Singamneni SB (2015) Curved layer adaptive slicing (CLAS) for fused deposition modelling. Rapid Prototyp J

  7. Livesu M, Ellero S, Martínez J, Lefebvre S, Attene M (2017) From 3D models to 3D prints: an overview of the processing pipeline. Comput Graphics Forum 36(2):537–564

    Article  Google Scholar 

  8. Pereira S, Vaz AIF, Vicente LN (2018) On the optimal object orientation in additive manufacturing. Int J Adv Manuf Technol 98(5):1685–1694

    Article  Google Scholar 

  9. Fu G, Fu J, Lin Z, Shen H, Jin Y (2017) A polygons Boolean operations-based adaptive slicing with sliced data for additive manufacturing. Proc Inst Mech Eng C J Mech Eng Sci 231(15):2783–2799

    Article  Google Scholar 

  10. Hu Y, Jiang X, Huo G, Su C, Li H, Zheng Z (2021) A novel adaptive slicing algorithm based on ameliorative area ratio and accurate cusp height for 3D printing. Rapid Prototyp J

  11. Pandey PM, Reddy NV, Dhande SG (2003) Improvement of surface finish by staircase machining in fused deposition modeling. J Mater Process Technol 132(1–3):323–331

    Article  Google Scholar 

  12. Siraskar N, Paul R, Anand S (2015) Adaptive slicing in additive manufacturing process using a modified boundary octree data structure. J Manuf Sci Eng 137(1)

  13. Liu GH, Wong YS, Zhang YF, Loh HT (2003) Error-based segmentation of cloud data for direct rapid prototyping. Comput Aided Des 35(7):633–645

    Article  Google Scholar 

  14. Qiu Y, Zhou X, Qian X (2011) Direct slicing of cloud data with guaranteed topology for rapid prototyping. Int J Adv Manuf Technol 53(1):255–265

    Article  Google Scholar 

  15. Xu J, Hou W, Sun Y, Lee Y (2018) PLSP based layered contour generation from point cloud for additive manufacturing. Robot Comput Integr Manuf 49:1–12

    Article  Google Scholar 

  16. Chen L, Chung MF, Tian Y, Jonejab A, Tang K (2019) Variable-depth curved layer fused deposition modeling of thin-shells. Robot Comput Integr Manuf 57:422–434

    Article  Google Scholar 

  17. Rosa F, Graziosi S (2019) A parametric and adaptive slicing (PAS) technique: general method and experimental validation. Rapid Prototyp J

  18. Zhang K, Li D, Gui H, Li Z (2019) An adaptive slicing algorithm for laser cladding remanufacturing of complex components. Int J Adv Manuf Technol 101(9):2873–2887

    Article  Google Scholar 

  19. Ma W, But WC, He P (2004) NURBS-based adaptive slicing for efficient rapid prototyping. Comput Aided Des 36(13):1309–1325

    Article  Google Scholar 

  20. Hayasi MT, Asiabanpour B (2013) A new adaptive slicing approach for the fully dense freeform fabrication (FDFF) process. J Intell Manuf 24(4):683–694

    Article  Google Scholar 

  21. Ding D, Pan Z, Cuiuri D, Li H, Larkin N, Duin S (2016) Automatic multi-direction slicing algorithms for wire based additive manufacturing. Robot Comput Integr Manuf 37:139–150

    Article  Google Scholar 

  22. Demir İ, Aliaga DG, Benes B (2018) Near-convex decomposition and layering for efficient 3D printing. Addit Manuf 21:383–394

    Google Scholar 

  23. Zhao D, Guo W (2020) Mixed-layer adaptive slicing for robotic Additive Manufacturing (AM) based on decomposing and regrouping. J Intell Manuf 31(4):985–1002

    Article  Google Scholar 

  24. Song Y, Yang Z, Liu Y, Deng J (2018) Function representation based slicer for 3D printing. Computer Aided Geometric Design 62:276–293

    Article  MathSciNet  Google Scholar 

  25. Minetto R, Volpato N, Stolfi J, Gregori RM, Silva MV (2017) An optimal algorithm for 3D triangle mesh slicing. Comput Aided Des 92:1–10

    Article  Google Scholar 

  26. Liu S, Liu T, Zou Q, Wang W, Doubrovski EL, Wang C (2021) Memory-Efficient modeling and slicing of Large-Scale adaptive lattice structures. J Comput Inf Sci Eng 21(6)

  27. Wang W, Chao H, Tong J, Yang Z, Tong X, Li H, Liu X, Liu L (2015) Saliency-preserving slicing optimization for effective 3d printing. Comput Graphics Forum 34(6):148–160

    Article  Google Scholar 

  28. Yang P, Qian X (2008) Adaptive slicing of moving least squares surfaces: toward direct manufacturing of point set surfaces. J Comput Inf Sci Eng 8(3)

  29. Zeng L, Lai LML, Qi D, Lai YH, Yuen MMF (2011) Efficient slicing procedure based on adaptive layer depth normal image. Comput Aided Des 43(12):1577–1586

    Article  Google Scholar 

  30. Srinivasan S, Ramakrishnan S (2011) Evolutionary multi objective optimization for rule mining: a review. Artif Intell Rev 36(3):205–248

    Article  Google Scholar 

  31. Wang C, Zhao J, Xia E (2018) Multi-objective optimal design of a novel multi-function rescue attachment based on improved NSGA-II. J Braz Soc Mech Sci Eng 40(7):1–15

    Article  Google Scholar 

  32. Alawode KO, Adegboyega GA, Abimbola Muhideen J (2018) NSGA-II/EDA hybrid evolutionary algorithm for solving multi-objective economic/emission dispatch problem. Electr Power Compon Syst 46(10):1160–1172

    Article  Google Scholar 

  33. Abido MA (2006) Multiobjective evolutionary algorithms for electric power dispatch problem. IEEE Trans Evol Comput 10(3):315–329

    Article  Google Scholar 

Download references

Funding

Financial funding was provided by NSFC-Shenzhen United Fund (U1913603) and National Key Research and Development Plan of China (2018YFB1306901).

Author information

Authors and Affiliations

  1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, West Xianning Road No. 28, Xi’an, 710049, Shaanxi, China

    Xiaoqi Wang & Jianfu Cao

Authors
  1. Xiaoqi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianfu Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Xiaoqi Wang. The first draft of the manuscript was written by Xiaoqi Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jianfu Cao.

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 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

Wang, X., Cao, J. A novel multi-objective optimization of 3D printing adaptive layering algorithm based on improved NSGA-II and fuzzy set theory. Int J Adv Manuf Technol 123, 957–972 (2022). https://doi.org/10.1007/s00170-022-10189-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-10189-0

Keywords

Search

Navigation