Design and Optimization of a Curved-Crease-Folding Process Applied to a Light Metallic Structure

Presently, the realization of complex, unconventional designs using efficient modalities is possible due to an increasing interest in interdisciplinary approaches: materials science, mathematics, IT, architecture, etc. Computerized techniques, among which the algorithmic/generative design is the most advanced one, that are associated with the individualized production methods are used for finding solutions for modern spatial forms with an unconventional spatial geometric shape, which are generically called “free-forms”. This work presents the design, realization and testing of a thin-walled metallic structure proposed as a light structural unit. An integrated research approach was proposed that utilized an algorithmic/digital design applied to the curved-crease-folding method with the study (at different length scales) of the metallic material behaviour after folding. An original method was proposed for the digital design and simulations. The specific mechanical behaviour of the metallic material in the elastic–plastic regime was used in this case to improve the structural performances; mechanical and structural tests were realized to analyse the behaviour of the entire structure. The results are useful for enhancing the accuracy of the digital design, the structural simulation programs and the fabrication methods.

Sustainable Implications of Industry 4.0

Industry 4.0, and in general, digital technologies, represent a fundamental model shift towards decentralization and individualized production. With this, the development of new services and business models based on the internet is encouraged. Somehow, this forces traditional supply chains to evolve into highly adaptative networks. Companies have to consider their internal resources and the benefits of getting closer to partners in the supply chain. In this sense, the implementation of these technologies is accompanied by a series of sustainable implication at economic, environmental, and social levels.

Produktivitätssteigerung durch Hybridisierung im 3D-Druck/Process development for the automated production of plastic parts with integrated functional components. Increased productivity through hybridization in 3D printing

Die additive Fertigung erlaubt eine standortunabhängige sowie de facto individualisierte Produktion von Bauteilen mit nahezu beliebiger Komplexität. Für die flexible Herstellung von hochfunktionalen Hybridbauteilen fehlt es allerdings an entsprechenden Maschinenkonzepten sowie Automatisierungslösungen. Durch ein hier vorgestelltes Anlagenkonzept sollen Funktionskomponenten in den additiven Herstellungsprozess integriert und neue Möglichkeiten der Bauteilhybridisierung erforscht werden.   Additive manufacturing allows a location-independent and de facto individualized production of components of almost any complexity. However, there is a need for appropriate machine concepts and automation solutions for the flexible production of highly functional hybrid components. A plant concept presented here is intended to integrate functional components into the additive manufacturing process and to explore new possibilities for component hybridization.

Lastgerechte Endlosfaser- verstärkungen im 3D-Druck/Load-compliant continuous fiber reinforcements in 3D-printing

Neue additive Fertigungsverfahren ermöglichen die individualisierte Herstellung von endlosfaserverstärkten Kunststoffen (eFVK) mit hohen mechanischen Steifigkeiten und Festigkeiten. Um Bauteile belastungsspezifisch auslegen zu können, müssen neue Methoden zur belastungsgerechten Integration der Verstärkungsfasern unter Berücksichtigung von Prozessrandbedingungen entwickelt werden. Am wbk Institut für Produktionstechnik des Karlsruher Instituts für Technologie (KIT) wird ein innovativer Ansatz entwickelt, der Verstärkungsfasern spannungs- und fertigungsgerecht in additiv gefertigte Bauteile integriert.   New additive manufacturing processes enable the individualized production of continuous fiber reinforced plastics (eFRP) with high mechanical stiffness and strength. To be able to design components in a load-specific manner, new methods for the load-compliant integration of reinforcing fibers must be developed under consideration of the process boundary conditions. An approach developed at the Institute of Production Science (wbk) of the Karlsruhe Institute of Technology (KIT) integrates reinforcing fibers into a component while considering local stresses.

Simulation-driven machine learning for robotics and automation

Mass personalization—a megatrend in industrial manufacturing and production—requires fast adaptations of robotics and automation solutions to continually decreasing lot sizes. In this paper, the challenges of applying robot-based automation in a highly individualized production are highlighted. To face these challenges, a framework is proposed that combines latest machine learning (ML) techniques, like deep learning, with high-end physics simulation environments. ML is used for programming and parameterizing machines for a given production task with minimal human intervention. If the simulation environment realistically captures physical properties like forces or elasticity of the real world, it provides a high-quality data source for ML. In doing so, new tasks are mastered in simulation faster than in real-time, while at the same time existing tasks are executed. The functionality of the simulation-driven ML framework is demonstrated on an industrial use case.

Sustainable Implications of Industry 4.0

Industry 4.0, and in general, digital technologies, represent a fundamental model shift towards decentralization and individualized production. With this, the development of new services and business models based on the internet is encouraged. Somehow, this forces traditional supply chains to evolve into highly adaptative networks. Companies have to consider their internal resources and the benefits of getting closer to partners in the supply chain. In this sense, the implementation of these technologies is accompanied by a series of sustainable implication at economic, environmental, and social levels.

Data Flow and Communication Framework Supporting Digital Twin for Geometry Assurance

Faster optimization algorithms, increased computer power and amount of available data, can leverage the area of simulation towards real-time control and optimization of products and production systems. This concept — often referred to as Digital Twin — enables real-time geometry assurance and allows moving from mass production to more individualized production. To master the challenges of a Digital Twin for Geometry Assurance the project Smart Assembly 4.0 gathers Swedish researchers within product development, automation, virtual manufacturing, control theory, data analysis and machine learning. The vision of Smart Assembly 4.0 is the autonomous, self-optimizing robotized assembly factory, which maximizes quality and throughput, while keeping flexibility and reducing cost, by a sensing, thinking and acting strategy. The concept is based on active part matching and self-adjusting equipment which improves geometric quality without tightening the tolerances of incoming parts. The goal is to assemble products with higher quality than the incoming parts. The concept utilizes information about individual parts to be joined (sensing), selects the best combination of parts (thinking) and adjust locator positions, clamps, weld/rivet positions and sequences (acting). The project is ongoing, and this paper specifies and highlights the infrastructure, components and data flows necessary in the Digital Twin in order to realize Smart Assembly 4.0. The framework is generic, but the paper focuses on a spot weld station where two robots join two sheet metal parts in an adjustable fixture.