This study demonstrates that two-and three-dimensional spatially graded,truss-based polymeric-material metamaterials can be designed for beneficial impact mitigation and energy absorption capabilities.Through a combin...This study demonstrates that two-and three-dimensional spatially graded,truss-based polymeric-material metamaterials can be designed for beneficial impact mitigation and energy absorption capabilities.Through a combination of numerical and experimental techniques,we highlight the broad property space of periodic viscoelastic trusses,realized using 3D printing via selective laser sintering.Extending beyond periodic designs,we investigate the impact response of spatially variant viscoelastic lattices in both two and three dimensions.Our result reveal that introducing spatial variations in lattice topology allows for redirecting of the impact trajectory,opening new opportunities for engineering and tailoring lightweight materials with target impact functionality.This is achieved through the combined selection of base material and metamaterial design.展开更多
After a decade of periodic truss-,plate-,and shell-based architectures having dominated the design of metamaterials,we introduce the non-periodic class of spinodoid topologies.Inspired by natural self-assembly process...After a decade of periodic truss-,plate-,and shell-based architectures having dominated the design of metamaterials,we introduce the non-periodic class of spinodoid topologies.Inspired by natural self-assembly processes,spinodoid metamaterials are a close approximation of microstructures observed during spinodal phase separation.Their theoretical parametrization is so intriguingly simple that one can bypass costly phase-field simulations and obtain a rich and seamlessly tunable property space.Counterintuitively,breaking with the periodicity of classical metamaterials is the enabling factor to the large property space and the ability to introduce seamless functional grading.We introduce an efficient and robust machine learning technique for the inverse design of(meta-)materials which,when applied to spinodoid topologies,enables us to generate uniform and functionally graded cellular mechanical metamaterials with tailored direction-dependent(anisotropic)stiffness and density.We specifically present biomimetic artificial bone architectures that not only reproduce the properties of trabecular bone accurately but also even geometrically resemble natural bone.展开更多
We propose an approach for data-driven automated discovery of material laws,which we call EUCLID(Efficient Unsupervised Constitutive Law Identification and Discovery),and we apply it here to the discovery of plasticit...We propose an approach for data-driven automated discovery of material laws,which we call EUCLID(Efficient Unsupervised Constitutive Law Identification and Discovery),and we apply it here to the discovery of plasticity models,including arbitrarily shaped yield surfaces and isotropic and/or kinematic hardening laws.The approach is unsupervised,i.e.,it requires no stress data but only full-field displacement and global force data;it delivers interpretable models,i.e.,models that are embodied by parsimonious mathematical expressions discovered through sparse regression of a potentially large catalog of candidate functions;it is one-shot,i.e.,discovery only needs one experiment.The material model library is constructed by expanding the yield function with a Fourier series,whereas isotropic and kinematic hardening is introduced by assuming a yield function dependency on internal history variables that evolve with the plastic deformation.For selecting the most relevant Fourier modes and identifying the hardening behavior,EUCLID employs physics knowledge,i.e.,the optimization problem that governs the discovery enforces the equilibrium constraints in the bulk and at the loaded boundary of the domain.Sparsity promoting regularization is deployed to generate a set of solutions out of which a solution with low cost and high parsimony is automatically selected.Through virtual experiments,we demonstrate the ability of EUCLID to accurately discover several plastic yield surfaces and hardening mechanisms of different complexity.展开更多
文摘This study demonstrates that two-and three-dimensional spatially graded,truss-based polymeric-material metamaterials can be designed for beneficial impact mitigation and energy absorption capabilities.Through a combination of numerical and experimental techniques,we highlight the broad property space of periodic viscoelastic trusses,realized using 3D printing via selective laser sintering.Extending beyond periodic designs,we investigate the impact response of spatially variant viscoelastic lattices in both two and three dimensions.Our result reveal that introducing spatial variations in lattice topology allows for redirecting of the impact trajectory,opening new opportunities for engineering and tailoring lightweight materials with target impact functionality.This is achieved through the combined selection of base material and metamaterial design.
文摘After a decade of periodic truss-,plate-,and shell-based architectures having dominated the design of metamaterials,we introduce the non-periodic class of spinodoid topologies.Inspired by natural self-assembly processes,spinodoid metamaterials are a close approximation of microstructures observed during spinodal phase separation.Their theoretical parametrization is so intriguingly simple that one can bypass costly phase-field simulations and obtain a rich and seamlessly tunable property space.Counterintuitively,breaking with the periodicity of classical metamaterials is the enabling factor to the large property space and the ability to introduce seamless functional grading.We introduce an efficient and robust machine learning technique for the inverse design of(meta-)materials which,when applied to spinodoid topologies,enables us to generate uniform and functionally graded cellular mechanical metamaterials with tailored direction-dependent(anisotropic)stiffness and density.We specifically present biomimetic artificial bone architectures that not only reproduce the properties of trabecular bone accurately but also even geometrically resemble natural bone.
基金M.F.and L.D.L.gratefully acknowledge the financial support of the Swiss National Science Foundation(SNF Project 200021_204316).
文摘We propose an approach for data-driven automated discovery of material laws,which we call EUCLID(Efficient Unsupervised Constitutive Law Identification and Discovery),and we apply it here to the discovery of plasticity models,including arbitrarily shaped yield surfaces and isotropic and/or kinematic hardening laws.The approach is unsupervised,i.e.,it requires no stress data but only full-field displacement and global force data;it delivers interpretable models,i.e.,models that are embodied by parsimonious mathematical expressions discovered through sparse regression of a potentially large catalog of candidate functions;it is one-shot,i.e.,discovery only needs one experiment.The material model library is constructed by expanding the yield function with a Fourier series,whereas isotropic and kinematic hardening is introduced by assuming a yield function dependency on internal history variables that evolve with the plastic deformation.For selecting the most relevant Fourier modes and identifying the hardening behavior,EUCLID employs physics knowledge,i.e.,the optimization problem that governs the discovery enforces the equilibrium constraints in the bulk and at the loaded boundary of the domain.Sparsity promoting regularization is deployed to generate a set of solutions out of which a solution with low cost and high parsimony is automatically selected.Through virtual experiments,we demonstrate the ability of EUCLID to accurately discover several plastic yield surfaces and hardening mechanisms of different complexity.
