• A fast surrogate material model for architected AM weaves is presented. • The training data is created using an automated labeling process. • The labeling finds the weaves’ internal forces using full-field displacement tests. • The results show a mean accuracy of above 90% for both normalized load and shape. Additively Manufacturing (AM) textiles enables the fabrication of architected structures that have tuned local mechanical properties to customize their design, e.g. , for personalization to the human body. For the inverse design of architected AM textiles, creating a computationally efficient mechanical simulation is still a major challenge. Continuum mechanics approaches lose their inherent simulation speed for the high element count that is required to simulate spatially varying designs and yarn-level behavior. Truss-based textile simulations typically have a low simulation time and high adaptivity, but the complexity of textile mechanics is challenging to model using simple truss elements. In this paper, we model weave structures as planar trusses with truss elements that have variable stiffness depending on strain, textile shear and textile design. We present an automated framework to evaluate the correlation between textile design, textile deformation and textile mechanics on a truss member level. This correlation is modeled using a discrete constitutive manifold, whose datapoints represent an empirical surrogate for traditional constitutive models. A design-sensitive surrogate material model formulated as an Artificial Neural Network (ANN) is trained on the constitutive manifold and is validated by evaluating the simulation accuracy of textile samples that vary their weave direction. The results show the capability of the ANN to simulate AM weaves with spatially varying designs on the order of seconds. This paves the way towards an efficient inverse design of architected AM textiles.

Current industrial practice does not reflect the opportunities provided by state-of-the-art design automation methods. The limited application of computational methods to support the design process by automating design tasks is caused by the lack of methods for comprehensive design automation task definition. Therefore, potential design automation tasks are not recognized and already deployed solutions lack integration to design practice from a product lifecycle management (PLM) perspective. In response to these shortcomings, this work proposes a method for identification and integration of design automation tasks that features collaborative workshops and enterprise architecture modelling for comprehensive analysis of design processes including its technological environments. The method applies design automation task templates that contextualize the knowledge levels required for design automation task definition and the design process including its technological environments. Evaluation with three industrial cases shows that the method enables efficient identification and integration of potential design automation tasks in a PLM context. The application of knowledge levels in conjunction with enterprise architecture modelling support the identification and validation of the relevant sources of knowledge required for design automation task formalization. Thus, this work contributes by introducing and evaluating a novel method for design automation task definition that brings the opportunities of state-of-the-art design automation methods into line with requirements stemming from design practice and the related PLM.

State-of-the-art computational design approaches for designing the assembly of a structure often rely on high-precision building objects, which can be difficult and costly to fabricate. To consider a wider range of construction materials and fabrication processes, e.g. low-cost 3D printing and brick making, this research introduces a novel computational design method for fabrication-aware assembly problems using existing building components with dimensional variations. Given a finite set of building objects, this study develops a statistical preprocessing approach and a modified pattern search with an efficient integer linear programming method to generate the assembly of a target structure, such that it is statically stable and optimized for geometric smoothness and space utilization. In two case studies, the proposed algorithm exhibits significantly better boundary fitting and space utilization compared with a genetic algorithm and a standard pattern search method and lower computational cost compared with a genetic algorithm.