• 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.

This paper introduces Gertie the Robotic Desk Lamp, a novel research platform that has five degrees of freedom, and is equipped with a camera and microphone in its lamp shade. These features mean that Gertie is a flexible and low-cost resource for conducting research into cognitive products and human-robot interaction. It will be available as an open hardware on http://www.opengertie.org/. Gertie was designed from first principles, and assembled using off the shelf electronic components and parts fabricated using a 3D printer. In this paper, the design of Gertie is presented, and its application as a research platform is described. Gertie has already been used to investigate a problem of simple object tracking, building on computer vision algorithms. Furthermore, it has also been used to investigate and replicate emotional body language. By imitating human body language Gertie is capable of expressing four of the basic Ekman emotions: 1) joy; 2) sadness; 3) surprise; and 4) fear. This work was validated using an online study, which investigates how well the emotions expressed by Gertie are recognized by human audiences. In total 84 participants were shown one video for each of the four emotions and they were asked to choose from a list of seven emotions, which they thought was displayed by Gertie. While joy and sadness were recognized very reliably with 81% and 88% of all people giving the correct answer, fear and surprise were more commonly misinterpreted as surprise and disgust. However, all emotions were recognized above the chance level percentage of 14%.

Flexible plant components can significantly increase the flexibility of manufacturing systems and enable concepts like mass-customized production. This paper presents an approach for production planning and execution for flexible manufacturing system components, based on software agents. The agents are implemented directly on a PLC, making them capable of real-time operation. Additionally, a service-interface contributes to the vertical integration of the approach into the higher level planning of a flexible production system. Using the presented software agents, flexible plant components can be fully automated and integrated in modern FMS, leading to a higher degree of flexibility and dependability of the overall system. The approach is evaluated on a flexible fixture device as a FMS component, capable of automatic reconfiguration.

Today's manufacturing and assembly systems have to be flexible to adapt quickly to an increasing number and variety of products, and changing market volumes. To manage these dynamics, several production concepts (e.g., flexible, reconfigurable, changeable or autonomous manufacturing and assembly systems) were proposed and partly realized in the past years. This paper presents the general principles of autonomy and the proposed concepts, methods and technologies to realize cognitive planning, cognitive control and cognitive operation of production systems. Starting with an introduction on the historical context of different paradigms of production (e.g., evolution of production and planning systems), different approaches for the design, planning, and operation of production systems are lined out and future trends towards fully autonomous components of an production system as well as autonomous parts and products are discussed. In flexible production systems with manual and automatic assembly tasks, human-robot cooperation is an opportunity for an ergonomic and economic manufacturing system especially for low lot sizes. The state-of-the-art and a cognitive approach in this area are outlined. Furthermore, introducing self-optimizing and self-learning control systems is a crucial factor for cognitive systems. This principles are demonstrated by a quality assurance and process control in laser welding that is used to perform improved quality monitoring. Finally, as the integration of human workers into the workflow of a production system is of the highest priority for an efficient production, worker guidance systems for manual assembly with environmentally and situationally dependent triggered paths on state-based graphs are described in this paper.

Current approaches for factory automation have not yet fully succeeded in realizing an autonomous manufacturing system for mass customized, highly variant products. In this interdisciplinary work between computer science and mechanical engineering departments, the authors report on an integral approach for a cognitive manufacturing system which uses planning, perception and knowledge capabilities to reach a level of flexibility and robustness as found in a traditional human workshop. Integrating a bottom-up approach for local machining planning, a global planning system and a perception system, an autonomously operating manufacturing system can be realized. The integrated approach is validated using a simple yet characteristic example part that demonstrates the potential of the approach and the interplay and interfaces between the methods. Overall, the approach demonstrates that a specialized local planning system for machining can be effectively integrated with a general, global planning system and a perception system and thus be integrated in the manufacturing system.