Multi-material Design in the Case of a Coupled Selection of Architectures and Materials: Application to Embedded Electronic Packaging

  • Paul Baracchini
  • Claire GuillebaudEmail author
  • François-Xavier Kromm
  • Hervé Wargnier


The objective of this study was to produce a methodology for designing multi-materials. This methodology was to be applied to an electronic embedded packaging structure for the aeronautics field. The reference material used for the electronic packaging was substituted for a multi-material able to combine the benefits of both material and architecture in order to enhance performance. The design of the multi-material was based on a coupled selection of materials and architectures performed using materials and geometrical pattern databases. The methodology provided both the optimal design in response to specifications and a large diversity of optimized designs (in terms of architectures and materials) relevant for the conceptual design stage. First, the optimal design of the electronic packaging was determined using a genetic algorithm. Next, the built approach integrated a hybridization of the genetic algorithm with a backtracking algorithm in order to propose optimized designs in a controlled time. Finally, the search space was modified by removing optimal designs previously identified by the genetic algorithm in order to determine a wide diversity of optimized designs.


computational materials development genetic algorithm materials by design modeling and simulation multi-material optimization algorithm 



The authors thank the partners of the MUJU project framework and the National Research Agency [ANR-11-0003-RMNP] for its financial support.


  1. 1.
    H. Ahari, A. Khajepour, S. Bedi, and W.W. Melek, A Genetic Algorithm for Optimization of Laminated Dies Manufacturing, CAD Comput. Aided Des., 2011, 43(6), p 730–737CrossRefGoogle Scholar
  2. 2.
    C.C. António, Self-adaptation Procedures in Genetic Algorithms Applied to the Optimal Design of Composite Structures, Int. J. Mech. Mater. Des., 2009, 5(3), p 289–302CrossRefGoogle Scholar
  3. 3.
    M.F. Ashby, Criteria for Selecting the Components of Composites, Acta Metall. Mater., 1993, 41(5), p 1313–1335CrossRefGoogle Scholar
  4. 4.
    M.F. Ashby, Overview No. 92: Materials and Shape, Acta Metallurgica Et Materialia, 1991, 39(6), p 1025–1039CrossRefGoogle Scholar
  5. 5.
    M.F. Ashby and Y.J.M. Bréchet, Designing Hybrid Materials, Acta Mater., 2003, 51(19), p 5801–5821CrossRefGoogle Scholar
  6. 6.
    Y. Bréchet and J.D. Embury, Architectured Materials: Expanding Materials Space, Scripta Mater., 2013, 68(1), p 1–3CrossRefGoogle Scholar
  7. 7.
    E.K. Burke, P.I. Cowling, and R. Keuthen, Effective Local and Guided Variable Neighbourhood Search Methods for the Asymmetric Travelling Salesman Problem, Lect. Notes Comput. Sci., 2001, 2037, p 203–212CrossRefGoogle Scholar
  8. 8.
    A. Collignan, P. Sebastian, J. Pailhes, and Y. Ledoux, Arc-Elasticity and Hierarchical Exploration of the Neighborhood of Solutions in Mechanical Design, Adv. Eng. Inform., 2012, 26(3), p 603–617CrossRefGoogle Scholar
  9. 9.
    J. Dirrenberger, S. Forest, and D. Jeulin, Effective Elastic Properties of Auxetic Microstructures: Anisotropy and Structural Applications, Int. J. Mech. Mater. Des., 2013, 9(1), p 21–33CrossRefGoogle Scholar
  10. 10.
    J.W.C. Dunlop and Y.J.M. Bréchet, Architectured Structural Materials: A Parallel Between Nature and Engineering, Materials Research Society Symposium Proceedings, MRS Online Proceedings Library, 1188-LL09-04, 15-25, 2009,
  11. 11.
    L. Duratti, L. Salvo, D. Landru, and Y. Bréchet, Selecting the Components of Polymeric Composites, Adv. Eng. Mater., 2002, 4(6), p 367–371CrossRefGoogle Scholar
  12. 12.
    S. Gasser, F. Paun, and Y. Bréchet, Finite Elements Computation for the Elastic Properties of a Regular Stacking of Hollow Spheres, Mater. Sci. Eng., A, 2004, 379(1–2), p 240–244CrossRefGoogle Scholar
  13. 13.
    S. Giaccobi, F.X. Kromm, H. Wargnier, and M. Danis, Filtration in Materials Selection and Multi-materials Design, Mater. Des., 2010, 31(4), p 1842–1847CrossRefGoogle Scholar
  14. 14.
    F. Glover and M. Laguna, M.: Tabu Search, in Handbook of Combinatorial Optimization, vol. 5–5 (Springer, New York 2013,), p 3261–3362.CrossRefGoogle Scholar
  15. 15.
    M. Grujicic, X. Xie, G. Arakere, A. Grujicic, D.W. Wagner, and A. Vallejo, Design-Optimization and Material Selection for a Proximal Radius Fracture-Fixation Implant, J. Mater. Eng. Perform., 2010, 19(8), p 1090–1103CrossRefGoogle Scholar
  16. 16.
    F.X. Irisarri, D.H. Bassir, N. Carrere, and J.F. Maire, Multiobjective Stacking Sequence Optimization for Laminated Composite Structures, Compos. Sci. Technol., 2009, 69(7–8), p 983–990CrossRefGoogle Scholar
  17. 17.
    M. Ivanova, Y. Avenas, C. Schaeffer, J.B. Dezord, and J. Schulz-Harder, Heat Pipe Integrated in Direct Bonded Copper (DBC) Technology for Cooling of Power Electronics Packaging, IEEE Trans. Power Electron., 2006, 21(6), p 1541–1547CrossRefGoogle Scholar
  18. 18.
    Q.C. Jiang, X.L. Li, and H.Y. Wang, Fabrication of TiC Particulate Reinforced Magnesium Matrix Composites, Scripta Mater., 2003, 48(6), p 713–717CrossRefGoogle Scholar
  19. 19.
    L. Jourdan, M. Basseur, and E.G. Talbi, Hybridizing Exact Methods and Metaheuristics: A Taxonomy, Eur. J. Oper. Res., 2009, 199(3), p 620–629CrossRefGoogle Scholar
  20. 20.
    W.A. Khan, J.R. Cuham, and M.M. Yovanovich, Modeling of Cylindrical Pin-Fin Heat Sinks for Electronic Packaging, IEEE Trans. Compon. Packag. Technol., 2008, 31(3), p 536–545CrossRefGoogle Scholar
  21. 21.
    F.X. Kromm, J.M. Quenisset, T. Lorriot, R. Harry, and H. Wargnier, Definition of a Multimaterials Design Method, Mater. Des., 2007, 28(10), p 2641–2646CrossRefGoogle Scholar
  22. 22.
    L. Laszczyk, R. Dendievel, O. Bouaziz, Y. Bréchet, and G. Parry, Design of Architecture Sandwich Core Materials Using Topological Optimization Methods, Mater. Res. Soc. Symp. Proc., 2009, 1188, p 131–136CrossRefGoogle Scholar
  23. 23.
    P. Leite, M. Thomas, F. Simon, and Y. Bréchet, Optimal Design of a Multifunctional Sandwich Panel with Foam Core: Lightweight Design for Flexural Stiffness and Acoustical Transmission Loss, Adv. Eng. Mater., 2015, 17(3), p 311–318CrossRefGoogle Scholar
  24. 24.
    R. Le Riche and R.T. Haftka, Optimization of Laminate Stacking Sequence for Buckling Load Maximization by Genetic Algorithm, AIAA J., 1993, 31(5), p 951–956CrossRefGoogle Scholar
  25. 25.
    P. Lohmuller, J. Favre, S. Kenzari, B. Piotrowski, L. Peltier, and P. Laheurte, Architectural Effect on 3D Elastic Properties and Anisotropy of Cubic Lattice Structures, Mater. Des., 2019, 182, p 108059CrossRefGoogle Scholar
  26. 26.
    M. Montemurro, Y. Koutsawa, S. Belouettar, A. Vincenti, and P. Vannucci, Design of Damping Properties of Hybrid Laminates Through a Global Optimisation Strategy, Compos. Struct., 2012, 94(11), p 3309–3320CrossRefGoogle Scholar
  27. 27.
    M. Montemurro, A. Vincenti, and P. Vannucci, A Two-Level Procedure for the Global Optimum Design of Composite Modular Structures—Application to the Design of an Aircraft Wing: Part 2: Numerical Aspects and Examples, J. Optim. Theory Appl., 2012, 155(1), p 24–53CrossRefGoogle Scholar
  28. 28.
    D. Pasini, Shape Transformers for Material and Shape Selection of Lightweight Beams, Mater. Des., 2007, 28(7), p 2071–2079CrossRefGoogle Scholar
  29. 29.
    J. Pflug and I. Verpoest, Sandwich Materials Selection Charts, J. Sandwich Struct. Mater., 2006, 8(5), p 407–421CrossRefGoogle Scholar
  30. 30.
    Y. Sakurai, K. Takada, N. Tsukamoto, T. Onoyama, R. Knauf and S. Tsuruta, A Simple Optimization Method Based on Backtrack and GA for Delivery Schedule, in Proceedings of the IEEE Congress of Evolutionary Computation, (New Orleans, 2011), p. 2790–2797,
  31. 31.
    K. Sivakumar, C. Balamurugan, and S. Ramabalan, Simultaneous Optimal Selection of Design and Manufacturing Tolerances With Alternative Manufacturing Process Selection, CAD Comput. Aided Des., 2011, 43(2), p 207–218CrossRefGoogle Scholar
  32. 32.
    E.G. Talbi, A Taxonomy of Hybrid Metaheuristics, J. Heuristics, 2002, 8(5), p 541–564CrossRefGoogle Scholar
  33. 33.
    A. Vincenti, M.R. Ahmadian, and P. Vannucci, BIANCA: A Genetic Algorithm to Solve Hard Combinatorial Optimisation Problems in Engineering, J. Global Optim., 2010, 48(3), p 399–421CrossRefGoogle Scholar
  34. 34.
    G. Vierhout, S. Roper, D. Li, B. Sangha, M. Pamwar and I.Y. Kim, Multi-Material Topology Optimization: A Practical Method for Efficient Material Selection and design, SAE Technical Papers, 2019,
  35. 35.
    H. Wargnier, F.X. Kromm, M. Danis, and Y. Brechet, Proposal for a Multi-material Design Procedure, Mater. Des., 2014, 56, p 44–49CrossRefGoogle Scholar
  36. 36.
    P.M. Weaver and M.F. Ashby, Material Limits for Shape Efficiency, Prog. Mater Sci., 1997, 41(1–2), p 61–128CrossRefGoogle Scholar
  37. 37.
    J. Xu, Y. Wu, L. Wang, J. Li, Y. Yang, Y. Tian, Z. Gong, P. Zhang, S.R. Nutt, and S. Yin, Compressive Properties of Hollow Lattice Truss Reinforced Honeycombs (Honeytubes) by Additive Manufacturing: Patterning and Tube Alignment Effects, Mater. Des., 2018, 156, p 446–457CrossRefGoogle Scholar
  38. 38.
    J. Xu, Y. Wu, X. Gao, H. Wu, S. Nutt, and S. Yin, Design of Composite Lattice Materials Combined with Fabrication Approaches, J. Compos. Mater., 2019, 53(3), p 393–404CrossRefGoogle Scholar
  39. 39.
    S. Yin, J. Li, B. Liu, K. Meng, Y. Huan, S.R. Nutt, and J. Xu, Honeytubes: Hollow Lattice Truss Reinforced Honeycombs for Crushing Protection, Compos. Struct., 2017, 160, p 1147–1154CrossRefGoogle Scholar
  40. 40.
    J. Zhang, C. Xu, M. Yi, and B. Fang, Design of Nano-Micro-composite Ceramic Tool and Die Material with Back Propagation Neural Network and Genetic Algorithm, J. Mater. Eng. Perform., 2012, 21(4), p 463–470CrossRefGoogle Scholar

Copyright information

© ASM International 2019

Authors and Affiliations

  1. 1.CNRS, I2MUniversité de BordeauxGradignanFrance

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