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Comparison of different cellular structures for the design of selective laser melting parts through the application of a new lightweight parametric optimisation method

  • Rubén PazEmail author
  • Mario D. Monzón
  • Philippe Bertrand
  • Alexey Sova
Article
  • 17 Downloads

Abstract

Interest in lightweight geometries and cellular structures has increased due to the freeform capabilities of additive manufacturing technologies. In this paper, six different cellular structures were designed and parameterised with three design variables to carry out the lightweight optimisation of an initial solid sample. According to the limitations of conventional computer-aided design (CAD) software, a new parametric optimisation method was implemented and used to optimise these six types of structures. The best one in terms of optimisation time and stiffness was parameterised with nine design variables, changing the dimensions of the internal cellular structure and the reinforcement zones. These seven optimised geometries were manufactured in a Phenix ProX200 selective laser melting machine without using support. The samples obtained were tested under flexural load. The results show that the cubic cell structures have some advantages in terms of CAD definition, parameterisation and optimisation time because of their simpler geometry. However, from the flexural test results it can be concluded that this type of cell structure and those with horizontal bars experience a loss of stiffness compared to the estimates of the finite element analysis because of imperfections in the manufacturing process of hanging structures.

Key words

Parametric optimisation Cellular structures Selective laser melting (SLM) Finite element analysis Design of experiments Refinement 

通过新型轻量化参数优化方法比较激光选区熔化 部件设计的不同细胞结构

中文概要

目的

1. 提出一种在外型不变的部件内模拟不同细胞结 构的方法;2. 发展激光选区熔化(SLM)部件轻 量化参数设计的新方法;3. 利用这一方案实现优 化设计并比较不同细胞结构的质量。

创新点

1. 提出基于拉丁超立方实验设计、遗传算法、克 里金元模型和有限元方法的轻量化优化方案; 2. 该方法可通过较少的采样获得良好的结果并 能克服几何奇点(内部网格细化算法)的问题。

方法

1. 进行内部细胞结构的生成和参数化;2. 根据输 入数据(设计变量和约束条件等)采用拉丁超立 方实验设计模拟所选样本;3. 利用先前的数据创 建克里金元模型并利用预测的元模型来计算遗 传算法演化过程中的适应函数;4. 将模拟实现的 优化结果添加到数据中更新元模型,并通过数次 重复迭代提高元模型的准确度直至误差小于 5%;5. 将这一概念应用于不同的几何结构,然后 通过SLM 加工制造优化后的几何结构,并在弯 曲载荷下进行测试。

结论

1. 该优化算法通过适当的参数化克服了SLM 技 术的相关限制,可适用于SLM部件的优化;2. 立 方单元格在计算机辅助设计定义、参数化和时间 优化等方面有一些优势,但和有限元分析的估计 结果相比,其存在的缺乏坚实支撑的水平条(悬 挂结构)会造成机械性能损失;3. 将立方单元结 构与用户自定义的参数化增强相结合可以得到 更有效的设计结果(更高的比刚度),但更多的 设计变量也延长了所需要的优化时间。

关键词

参数优化 细胞结构 激光选区熔化 有限元分析 实验设计 精细化 

CLC number

TH164 TG665 

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References

  1. Akin JE, Arjona-Baez J, 2001. Enhancing structural topology optimization. Engineering Computations, 18(3-4):663–675. https://doi.org/10.1108/02644400110387640CrossRefzbMATHGoogle Scholar
  2. Aremu A, Ashcroft I, Wildman R, et al., 2013. The effects of bidirectional evolutionary structural optimization parameters on an industrial designed component for additive manufacture. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 227(6), 794–807. https://doi.org/10.1177/0954405412463857CrossRefGoogle Scholar
  3. Autodesk, 2015. Netfabb Software: Additive Manufacturing and Design Software. Autodesk. http://www.netfabb.com/Autodesk Within,2015. Within–Software–General Overview. Autodesk. http://withinlab.com/software/Google Scholar
  4. Bagheri ZS, Melancon D, Liu L, et al., 2017. Compensation strategy to reduce geometry and mechanics mismatches in porous biomaterials built with selective laser melting. Journal of the Mechanical Behavior of Biomedical Materials, 70, 17–27. https://doi.org/10.1016/j.jmbbm.2016.04.041CrossRefGoogle Scholar
  5. Calignano F, 2018. Investigation of the accuracy and roughness in the laser powder bed fusion process. Virtual and Physical Prototyping, 13(2), 97–104. https://doi.org/10.1080/17452759.2018.1426368CrossRefGoogle Scholar
  6. Campanelli SL, Contuzzi N, Angelastro A, et al., 2010. Capabilities and performances of the selective laser melting process. In: Er MJ (Ed.), New Trends in Technologies: Devices, Computer, Communication and Industrial Systems. InTech, p.233–252.Google Scholar
  7. Dassault Systèmes, 2013. SOLIDWORKS Help: Node to Surface Contact. Dassault Systèmes. http://help.solidworks.com/2013/English/SolidWorks/cworks/c_Node_to_Surface_Contact.htmGoogle Scholar
  8. Dotcheva M, Thomas D, Millward H, 2009. An evaluation of rapid manufactured cellular structures to enhance injection moulding tool performance. International Journal of Materials Engineering and Technology, 1(2), 105–127.Google Scholar
  9. Gibson I, Rosen DW, Stucker B, 2009. Additive Manufacturing Technologies: Rapid Prototyping to Direct Digital Manufacturing. Springer, New York, USA.Google Scholar
  10. González SG, 2010. SolidWorks Simulation. RA-MA S.A., Madrid, Spain (in Spanish).Google Scholar
  11. Hutmacher DW, Sittinger M, Risbud MV, 2004. Scaffoldbased tissue engineering: rationale for computer-aided design and solid free-form fabrication systems. Trends in Biotechnology, 22(7), 354–362. https://doi.org/10.1016/j.tibtech.2004.05.005CrossRefGoogle Scholar
  12. ISO/ASTM International, 2015. Additive Manufacturing— General Principles—Terminology, ISO/ASTM 52900: 2015. ISO/ASTM International, Switzerland.Google Scholar
  13. Jorge MA, da Conceicao Batista F, Almeida HA, et al., 2007. Virtual and Rapid Manufacturing: Advanced Research in Virtual and Rapid Prototyping. CRC Press, Boca Raton, USA.Google Scholar
  14. Kranz J, Herzog D, Emmelmann C, 2015. Design guidelines for laser additive manufacturing of lightweight structures in TiAl6V4. Journal of Laser Applications, 27(S1): S14001. https://doi.org/10.2351/1.4885235CrossRefGoogle Scholar
  15. Kulkarni VR, Tambe AG, 2013. Optimization and finite element analysis of steering knuckle. Proceedings of Altair Technology Conference.Google Scholar
  16. Labeas GN, Sunaric MM, 2010. Investigation on the static response and failure process of metallic open lattice cellular structures. Strain, 46(2), 195–204. https://doi.org/10.1111/j.1475-1305.2008.00498.xCrossRefGoogle Scholar
  17. Lophaven SN, Nielsen HB, Søndergaard J, 2002a. DACE–a Matlab Kriging Toolbox, Version 2.0. IMM-TR-2002-12, Technical University of Denmark, Kongens Lyngby, Denmark.Google Scholar
  18. Lophaven SN, Nielsen HB, Søndergaard J, 2002b. Aspects of the Matlab Toolbox Dace. IMMREP-2002-13, Technical University of Denmark, Kongens Lyngby, Denmark.Google Scholar
  19. Lynch ME, Gu WJ, El-Wardany T, et al., 2013. Design and topology/shape structural optimisation for additively manufactured cold sprayed components. Virtual and Physical Prototyping, 8(3), 213–231. https://doi.org/10.1080/17452759.2013.837629CrossRefGoogle Scholar
  20. Materialise 3-matic, 2015. Materialise 3-matic Lightweight Structures Module. Materialise 3-matic. http://software.materialise.com/3-matic-lightweight-struc tures-moduleGoogle Scholar
  21. MathWorks, 2015. Lhsdesign: Latin Hypercube Sample. MathWorks, Spain. http://es.mathworks.com/help/stats/lhsdesign.htmlGoogle Scholar
  22. Monzón M, 2018. Biomaterials and additive manufacturing: osteochondral scaffold innovation applied to osteoarthritis (BAMOS project). Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 19(4): 329–330. https://doi.org/10.1631/jzus.A18NW001CrossRefGoogle Scholar
  23. Mullen L, Stamp RC, Brooks WK, et al., 2009. Selective laser melting: a regular unit cell approach for the manufacture of porous, titanium, bone in-growth constructs, suitable for orthopedic applications. Journal of Biomedical Materials Research Part B: Applied Biomaterials, 89B(2): 325–334. https://doi.org/10.1002/jbm.b.31219CrossRefGoogle Scholar
  24. Murr LE, Gaytan SM, Medina F, et al., 2010. Next-generation biomedical implants using additive manufacturing of complex, cellular and functional mesh arrays. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368(1917): 1999–2032. https://doi.org/10.1098/rsta.2010.0010CrossRefGoogle Scholar
  25. Peltola SM, Melchels FP, Grijpma DW, et al., 2008. A review of rapid prototyping techniques for tissue engineering purposes. Annals of Medicine, 40(4), 268–280. https://doi.org/10.1080/07853890701881788CrossRefGoogle Scholar
  26. Pepelnjak T, Gantar G, Kuzman K, 2001. Numerical simulations in optimisation of product and forming process. Journal of Materials Processing Technology, 115(1), 122–126. https://doi.org/10.1016/S0924-0136(01)00744-0CrossRefGoogle Scholar
  27. Protasov CE, Khmyrov RS, Grigoriev SN, et al., 2017. Selective laser melting of fused silica: interdependent heat transfer and powder consolidation. International Journal of Heat and Mass Transfer, 104, 665–674. https://doi.org/10.1016/j.ijheatmasstransfer.2016.08.107CrossRefGoogle Scholar
  28. Sachlos E, Czernuszka JT, 2003. Making tissue engineering scaffolds work. Review: the application of solid freeform fabrication technology to the production of tissue engineering scaffolds. European Cells and Materials, 5, 29–40.Google Scholar
  29. Salmi A, Calignano F, Galati M, et al., 2018. An integrated design methodology for components produced by laser powder bed fusion (L-PBF) process. Virtual and Physical Prototyping, 13(3), 191–202. https://doi.org/10.1080/17452759.2018.1442229CrossRefGoogle Scholar
  30. Siemens PLM Software, 2010. Femap Version 10.2: What’s New. Siemens PLM Software. https://appliedcax.com/support-and-training/technical-on line-seminars/seminars/2010-12-02_seminar.pdfGoogle Scholar
  31. Sing SL, Yeong WY, Wiria FE, et al., 2017. Direct selective laser sintering and melting of ceramics: a review. Rapid Prototyping Journal, 23(3), 611–623. https://doi.org/10.1108/RPJ-11-2015-0178CrossRefGoogle Scholar
  32. Sing SL, Wiria FE, Yeong WY, 2018. Selective laser of lattice structures: a statistical approach to manufacturability and mechanical behavior. Robotics and Computer-Integrated Manufacturing, 49, 170–180. https://doi.org/10.1016/j.rcim.2017.06.006CrossRefGoogle Scholar
  33. Synopsys, 2015. Simpleware Software Solutions: 3D Image Data Visualization, Analysis and Model Generation with Simpleware. Synopsys. http://www.simpleware.comGoogle Scholar
  34. Thomas D, 2009. The Development of Design Rules for Selective Laser Melting. PhD Thesis, University of Wales, Cardiff, UK.Google Scholar
  35. Wohlers T, Caffrey T, 2014. Wohlers Report 2014: 3D Printing and Additive Manufacturing State of the Industry Annual Worldwide Progress Report. Wohlers Associates, Fort Collins, USA.Google Scholar
  36. Wong KV, Hernandez A, 2012. A review of additive manufacturing. ISRN Mechanical Engineering, 2012:208760. https://doi.org/10.5402/2012/208760Google Scholar
  37. Yang SF, Leong KF, Du ZH, et al., 2001. The design of scaffolds for use in tissue engineering. Part I. Traditional factors. Tissue Engineering, 7(6), 679–689. https://doi.org/10.1089/107632701753337645CrossRefGoogle Scholar
  38. Yap CY, Chua CK, Dong ZL, et al., 2015. Review of selective laser melting: materials and applications. Applied Physics Reviews, 2(4):041101. https://doi.org/10.1063/1.4935926CrossRefGoogle Scholar
  39. Yeong WY, Chua CK, Leong KF, et al., 2004. Rapid prototyping in tissue engineering: challenges and potential. Trends in Biotechnology, 22(12), 643–652. https://doi.org/10.1016/j.tibtech.2004.10.004CrossRefGoogle Scholar
  40. Yoo DJ, 2011. Computer-aided porous scaffold design for tissue engineering using triply periodic minimal surfaces. International Journal of Precision Engineering and Manufacturing, 12(1), 61–71. https://doi.org/10.1007/s12541-011-0008-9CrossRefGoogle Scholar
  41. Zhang K, Liu TT, Liao WH, et al., 2018. Influence of laser parameters on the surface morphology of slurry-based Al2O3 parts produced through selective laser melting. Rapid Prototyping Journal, 24(2), 333–341. https://doi.org/10.1108/RPJ-12-2016-0201CrossRefGoogle Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universidad de Las Palmas de Gran CanariaDepartamento de Ingeniería MecánicaLas Palmas de Gran CanariaSpain
  2. 2.University of Lyon, ENISELTDS CNRS UMR 5513Saint-EtienneFrance

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