Discrete Material and Thickness Optimization of sandwich structures

Sjølund, Jonas Heidemann; Peeters, Daniël; Lund, Erik

doi:10.1016/j.compstruct.2019.03.003

Cited by 28 publications

(15 citation statements)

References 38 publications

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“…Sørensen et al (2014) continued the work as Discrete Material and Thickness Optimization (DMTO) and focused on practical tasks of mass minimization with buckling, eigenfrequency, displacement, manufacturing constraints, and patch application. In a recent study, Sjølund et al (2019) improved DMTO to handle sandwiches with variable thickness core and face-sheets, and demonstrated it on minimization mass task with displacement and buckling constraints.…”

Section: Introductionmentioning

confidence: 99%

“…Lund (2018) applied DMTO on laminated composites with failure constraints. Several DMO studies used mass as a goal function (Cheng and Guo 1997;Holmberg et al 2013;Lund 2013, 2015;Kennedy and Hicken 2015;Kennedy 2016;Sjølund et al 2019). Gao and Zhang (2011) used recursive multiphase material interpolation (RMMI) and uniform multiphase materials interpolation (UMMI) schemes in multi-material topology optimization and discussed the difference of replacing volume with mass.…”

Section: Introductionmentioning

confidence: 99%

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Discrete material optimization with sandwich failure constraints

Löffelmann

2021

Struct Multidisc Optim

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Discrete Material Optimization (DMO) is a method, which was originally developed for designing composite structures via multi-material topology optimization principles. Current study applies DMO to sandwich structures with variable thickness in the core and face-sheets. Each layer contains design variables for available materials. Materials are combined through interpolation schemes to define properties of the layer. The objective function (mass of the structure) and the failure constraints are interpolated via Rational Approximation of Material Properties (RAMP) in order to calculate with smooth variables, but achieve discrete results. This enables gradient optimization via Interior Point Optimizer (IPOPT) with constraints on maximum stress, wrinkling, and crimping. Structure is modeled by the finite element method, which calculates element forces and moments repeatedly as the stiffness of the structure changes during optimization. Element loads are used by the first-order shear deformation theory to evaluate the stresses in the layers to obtain failure constraints requested in each iteration by the gradient optimizer. Solution is demonstrated on the plate examples showing material distribution and discreteness level. In addition, constraint aggregation by Kreisselmeier-Steinhauser (KS) function was utilized to decrease the number of constraints in the optimization.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discrete material optimization with sandwich failure constraints

Löffelmann

2021

Struct Multidisc Optim

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“…In particular, access to this problem has been frequently studied in the field of composite materials. Discrete material and thickness optimization (DMTO), which arranges materials and thicknesses in order of strength and optimizes them with discrete IDs, has been researched [20][21][22].…”

Section: Introduction (A Head)mentioning

confidence: 99%

Discrete Material and Thickness Optimization of Pop-Up Seat Frame in Static Condition

Moon

Shin

Jeon

et al. 2020

International Journal of Mechanical Engineering and Technology (Ijmet)

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With the increase in the number of lightweight, high-strength and highperformance automotive parts, studies are being conducted on the application of highstrength and lightweight materials and the optimization of the thickness values of these parts. From a practical perspective, however, the indiscriminate use of highstrength and lightweight materials leads to very low mass production. Suggesting optimum design values also requires the adoption of new processes that are not practical for application in manufacturing processes. In this study, discrete material and thickness optimization (DMTO) that considers materials and thickness values for commercialization was applied.A simple model of a retractable seat frame for recreational vehicles (RVs), whose thickness and material were not fixed, was investigated. Tests were conducted to secure accurate material properties for finite element analysis (FEA), and constraints were set based on the Federal Motor Vehicle Safety Standards (FMVSS) 207 and FMVSS 210 tests. The results of DMTO were compared with those of discrete thickness optimization (DTO) to verify the validity of the design parameters.

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“…The new thickness formulation also adds new possibilities with regard to applying DMTO for sandwich structures which is explored in Paper C (Sjølund et al, 2019). Since intermediate voids can no longer appear, it is possible to change densities of both internal core plies and external face sheet plies simultaneously.…”

Section: Discrete Materials and Thickness Optimization (Dmto)mentioning

confidence: 99%

“…The DMTO method is used to optimize a wind turbine main spar in Sørensen et al (2014); Lund (2018); Sjølund et al (2019). In Sørensen et al (2014) the mass of a main spar is minimized while subject to displacement, buckling, eigenfrequency, and manufacturing constraints.…”

Section: Application To Wind Turbine Bladesmentioning

confidence: 99%

Structural Optimization of Wind Turbine Blades

Sjølund¹

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Discrete Material and Thickness Optimization of sandwich structures

Cited by 28 publications

References 38 publications

Discrete material optimization with sandwich failure constraints

Discrete material optimization with sandwich failure constraints

Discrete Material and Thickness Optimization of Pop-Up Seat Frame in Static Condition

Structural Optimization of Wind Turbine Blades

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