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A computational method for optimizing the shape of the centerline curve and the spatial variation of geometric and material sizing parameters of the cross-sections of elastic, 3-dimensional beams and beam structures subject to large deformations is presented in this work. The approach is based on the concept of isogeometric analysis, i.e., the representation of geometry and the discretization of the numerical solution using spline functions. Here, mixed isogeometric collocation methods are used to discretize the geometrically exact 3D beam model. These spline representations are extended to the parameterization of the design variables, which are the initial centerline curves of the beams, as well as cross-sectional sizing properties, which may be varying along the beam axis and can be functionally graded through the cross-sections. To tailor the mechanical deformation behavior of a beam or beam structure, a nonlinear optimization problem is formulated and solved using gradient-based methods. For this purpose, all required gradients and sensitivities are derived analytically. The potential of this holistic design optimization approach is demonstrated in application to tailoring of elastic metamaterials and beam lattice structures, as well as 4D printing of multi-material laminate beams.

In the present work, two machine learning based constitutive models for finite deformations are proposed. Using input convex neural networks, the models are hyperelastic, anisotropic and fulfill the polyconvexity condition, which implies ellipticity and thus ensures material stability. The first constitutive model is based on a set of polyconvex, anisotropic and objective invariants. The second approach is formulated in terms of the deformation gradient, its cofactor and determinant, uses group symmetrization to fulfill the material symmetry condition, and data augmentation to fulfill objectivity approximately. The extension of the dataset for the data augmentation approach is based on mechanical considerations and does not require additional experimental or simulation data. The models are calibrated with highly challenging simulation data of cubic lattice metamaterials, including finite deformations and lattice instabilities. A moderate amount of calibration data is used, based on deformations which are commonly applied in experimental investigations. While the invariant-based model shows drawbacks for several deformation modes, the model based on the deformation gradient alone is able to reproduce and predict the effective material behavior very well and exhibits excellent generalization capabilities. In addition, the models are calibrated with transversely isotropic data, generated with an analytical polyconvex potential. For this case, both models show excellent results, demonstrating the straightforward applicability of the polyconvex neural network constitutive models to other symmetry groups.

In this work, we introduce KnitKit , a flexible and customizable system for the computational design and production of functional, multi-material, and three-dimensional knitted textiles. Our system greatly simplifies the knitting of 3D objects with complex, varying patterns that use multiple yarns and stitch patterns by separating the high-level design specification in terms of geometry, stitch patterns, materials or colors from the low-level, machine-specific knitting instruction generation. Starting from a triangular 3D mesh and a 2D texture that specifies knitting patterns on top of the geometry, our system generates the required machine instructions in three major steps. First, the input is processed and the KnitNet data structure is generated. This graph structure serves as an abstract interface between the high-level geometric and knitting configuration and the low-level, machine-specific knitting instructions. Second, a graph rewriting procedure is applied on the KnitNet that produces a sequence of abstract machine actions. Finally, the low-level machine instructions are generated by adapting those abstract actions to a specific machine context. We showcase the potential of this computational approach by designing and fabricating a variety of objects with complex geometries, multiple yarns, and multiple stitch patterns.

This work investigates the capabilities of anisotropic theory-based, purely data-driven and hybrid approaches to model the homogenized constitutive behavior of cubic lattice metamaterials exhibiting large deformations and buckling phenomena. The effective material behavior is assumed as hyperelastic, anisotropic and finite deformations are considered. A highly flexible analytical approach proposed by Itskov (Int J Numer Methods Eng 50(8): 1777–1799, 2001) is taken into account, which ensures material objectivity and fulfillment of the material symmetry group conditions. Then, two non-intrusive data-driven approaches are proposed, which are built upon artificial neural networks and formulated such that they also fulfill the objectivity and material symmetry conditions. Finally, a hybrid approach combing the approach of Itskov (Int J Numer Methods Eng 50(8): 1777–1799, 2001) with artificial neural networks is formulated. Here, all four models are calibrated with simulation data of the homogenization of two cubic lattice metamaterials at finite deformations. The data-driven models are able to reproduce the calibration data very well and reproduce the manifestation of lattice instabilities. Furthermore, they achieve superior accuracy over the analytical model also in additional test scenarios. The introduced hyperelastic models are formulated as general as possible, such that they can not only be used for lattice structures, but for any anisotropic hyperelastic material. Further, access to the complete simulation data is provided through the public repository https://github.com/CPShub/sim-data.

A frictionless contact formulation for spatial rods is developed within the framework of isogeometric collocation. The structural mechanics is described by the Cosserat theory of geometrically nonlinear spatial rods. The numerical discretization is based on an isogeometric collocation method, where the geometry and solution fields are represented as NURBS curves and the strong forms of the equilibrium equations are collocated at Greville points. In this framework, a frictionless rod-to-rod contact formulation is proposed. Contact points are detected by a coarse-level and a refined search for close centerline points and reaction forces are computed by the actual penetration of rod surface points, so that the enforcement of the contact constraints is performed with the penalty method. An important aspect is the application of contact penalty forces as point loads within the collocation scheme, and methods for this purpose are proposed and evaluated. The overall contact algorithm is validated by and applied to several numerical examples. This is the authors' accepted and refereed manuscript to the article. Locked until 01 July 2019 due to copyright restrictions

Igatools is a newly released library for operators assembly in isogeometric analysis. The library, which is object oriented designed and written in C++11, is general purpose, therefore it is not devoted to any specific application. In this paper we show that such a design makes igatools an effective tool in assembling isogeometric discretizations of sophisticated differential operators. This effectiveness will be demonstrated showing code snippets relating one-to-one with the operators written on paper. To embrace a wide audience, applications from nonlinear incompressible solid and fluid mechanics will be addressed. In both cases we are going to deal with mixed isogeometric formulations. The applicative nature of this paper will be stressed solving industrially relevant tests cases. Fil: Cavallini, Nicola. Scuola Internazionale Superiore Di Studi Avanzati; Italia Fil: Weeger, Oliver. No especifíca; Fil: Pauletti, Miguel Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina Fil: Martinelli, Massimiliano. Consiglio Nazionale delle Ricerche. Istituto di Matematica Applicata e Tecnologie Informatiche; Italia Fil: Antolin Jofre, Pablo Ariel. Consiglio Nazionale delle Ricerche. Istituto di Matematica Applicata e Tecnologie Informatiche; Italia

In this paper we present a method for nonlinear frequency response analysis of mechanical vibrations of 3-dimensional solid structures. For computing nonlinear frequency response to periodic excitations, we employ the well-established harmonic balance method. A fundamental aspect for allowing a large-scale application of the method is model order reduction of the discretized equation of motion. Therefore we propose the utilization of a modal projection method enhanced with modal derivatives, providing second-order information. For an efficient spatial discretization of continuum mechanics nonlinear partial differential equations, including large deformations and hyperelastic material laws, we employ the concept of isogeometric analysis. Isogeometric finite element methods have already been shown to possess advantages over classical finite element discretizations in terms of higher accuracy of numerical approximations in the fields of linear vibration and static large deformation analysis. With several computational examples, we demonstrate the applicability and accuracy of the modal derivative reduction method for nonlinear static computations and vibration analysis. Thus, the presented method opens a promising perspective on application of nonlinear frequency analysis to large-scale industrial problems.