Technologie: Additive Manufacturing

In this paper, the creation of strength and stiffness-adapted structural nodes using the numerical simulation method of topology optimization is presented. The resulting node is transferred into a robot path planning by means of CAD/CAM software and manufactured with wire arc additive manufacturing (WAAM) with the GMAW process using the welding filler material G4Si1.

The manufacturing share of laser powder bed fusion (L-PBF) increases in industrial application, but still many process steps are manually operated. Additionally, it is not possible to achieve tight dimensional tolerances or low surface roughness. Hence, a process chain has to be set up to combine additive manufacturing (AM) with further machining technologies. To achieve a continuous workpiece flow as basis for further industrialization of L-PBF, the article presents a novel substrate system and its application on L-PBF machines and post-processing. The substrate system consists of a zero-point clamping system and a matrix-like interface of contact pins to be substantially connected to the workpiece within the L-PBF process.

Current advances result in increasingly complex production programs. Through combination of additive manufacturing and Industry 4.0, new elements can be formed and - as a whole - enable to economically manufacture the above mentioned programs. The Bionic Smart Factory 4.0 provides a framework, structuring them in terms of relation and interaction. Their development and implementation is being promoted through their evaluation against the determinants of complex production programs.

Metal-cutting manufacturing companies constantly demand for highly efficient process layouts which can particularly be achieved by the utilization of application optimized special tools. Conventional methods for the manufacturing of cutting tools are subject to restrictions, particularly with respect to the inner and outer shape design. At this point, additive manufacturing offers a substantial innovation potential. Through the buildup by layers, design limits of conventional methods are repealed and the production of complex and individual structures is feasible. Against the background of these process-specific potentialities, the iWFT and associated research and industry partners are developing a process chain for additive manufacturing of tungsten carbide cutting tools in the framework of the joint research project PraeziGen.

Architectural design is traditionally limited by the availability of fabrication tools and increasing loan costs. Especially in concrete industries this has a great effect due to the intensive need of manual craftsmanship at the production of formwork. By introducing additive fabrication processes and methods of Industry 4.0 as advanced inspection procedures new design limits can be discovered. To produce large scale concrete elements with high surface qualities and high accuracy at joints in short time hybrid fabrication processes can be a solution. At TU Braunschweig a new generative method is investigated where concrete is sprayed on an adaptive formwork and graded surfaces can be generated.

The proliferation of technological innovations in additive manufacturing is accompanied by an increasing awareness of sustainability. In order to achieve an adequate investment methodical support is needed. A set of indicators for sustainable production represents the performance of a technology. Since an evaluation of technologies is subjective individual preferences of decision makers have to be taken into account. Hence, the multiple-criteria decision analysis methodology PROMETHEE is applicable in this context.

The LearningGripper from Festo looks like an abstract form of the human hand. The four fingers of the compliant gripper are driven by 12 pneumatic bellows actuators with low-level pressurisation. Thanks to the process of machine learning, it is able to teach itself to carry out complex actions such as, for example, gripping and positioning an object. By means of the LearningGripper we demonstrate how the development of such complex systems will be accelerated in the production of the future. Furthermore, the specific usage of machine learning algorithms will increase the efficiency of whole production plants.