Additive manufacturing (AM) offers numerous benefits for innovative design solutions. However, engineers are currently not supported in identifying and incorporating these potentials systematically in their design solutions. In this paper, previous Design for Additive Manufacturing (DfAM) approaches are first reviewed comprehensively and classified into distinct categories according to their main purpose and application. They are then analysed further by being related to conventional design methodologies like VDI 2221. Since previous DfAM approaches only provide selective assistance at single steps in the product development process, a new framework for DfAM is proposed. Existing methods and tools, both from DfAM and from general design methodologies, are integrated into the modular framework structure. A concept for using the framework is presented to provide design engineers with continuous support in all product development phases, thereby fostering the complete exploitation of AM potentials and the development of AM-conformal designs.

Additive manufacturing, especially material extrusion (MEX), has received a lot of attention recently. The reasons for this are the numerous advantages compared to conventional manufacturing processes, which result in various new possibilities for product development and -design. By applying material layer by layer, parts with complex, load-path optimized geometries can be manufactured at neutral costs. To expand the application fields of MEX, high-strength and simultaneously lightweight materials are required which fulfill the requirements of highly resilient technical parts. For instance, the embedding of continuous carbon and flax fibers in a polymer matrix offers great potential for this. To achieve the highest possible variability with regard to the material combinations while ensuring simple and economical production, the fiber–matrix bonding should be carried out in one process step together with the actual parts manufacture. This paper deals with the adaptation and improvement of the 3D printer on the one hand and the characterization of 3D printed test specimens based on carbon and flax fibers on the other hand. For this purpose, the print head development for in-situ processing of contin uous fiber-reinforced parts with improved mechanical properties is described. It was determined that compared to neat polylactic acid (PLA), the continuous fiber-reinforced test specimens achieve up to 430% higher tensile strength and 890% higher tensile modulus for the carbon fiber reinforcement and an increase of up to 325% in tensile strength and 570% in tensile modulus for the flax fibers. Similar improvements in performance were achieved in the bending tests.

Multi-material additive manufacturing offers new design freedom for functional integration and opens new possibilities in innovative part design, for instance, a local integration of electrically conductive structures or heat radiant surfaces. Detailed experimental investigations on materials with three different fillers (carbon black (CB), carbon nanotubes (CNT) and nano copper wires) were conducted to identify process-specific influencing factors on electrical conductivity and resistive heating. In this regard, raster angle orientation, extrusion temperature, speed and flow rate were investigated. A variation of the raster angle (0°, ±45°, and 90°) shows the highest influence on resistivity. An angle of 0° had the lowest electrical resistance and the highest temperature increase due to resistive heating. The material filled with nano copper wires showed the highest electrical conductivity followed by the CNT filled material and materials filled with CB. Both current–voltage characteristics and voltage-dependent heat distribution of the surface temperature were determined by using a thermographic camera. The highest temperature increase was achieved by the CNT filled material. The materials filled with CB and nano copper wires showed increased electrical resistance depending on temperature. Based on the experiments, solution principles and design rules for additively manufactured electrically conductive structures are derived.

Material composition complexity offered by material extrusion additive manufacturing offers new opportunities for function-driven part design. Nevertheless, since influencing factors on the interface strength between different materials are not well understood, this complexity is only used infrequently, in part, in design thereby restraining innovation. This paper proposes a systematical approach for identification and quantification of relevant adhesion phenomena that influence interface strength. For this reason, suited test specimen, which utilize the geometric freedom offered by additive manufacturing, are developed for roll peeling tests and peeling resistance of several combinations of rigid and flexible materials is determined. The results show that material choice especially regarding polarity as well as mechanical interlocking in regards to surface roughness and design features have high influence on the interface strength of multi-material parts manufactured by material extrusion. These results are explained through the relevant adhesion mechanisms that determine the interface strength in additively manufactured parts. Finally, criteria that predominantly affect interface strength are deduced and design recommendations for creating functional parts with ill-fitting material combinations are formulated.

To be able to use finite element (FE) simulations in structural component development, experimental investigations for the characterization of the material properties are required to subsequently calibrate suitable material cards. In contrast to the commonly used computational and time-consuming method of parameter identification (PI) by using analytical and numerical optimizations with internal or commercial software, a more time-efficient method based on machine learning (ML) is presented. This method is applied to simulate the material behavior of additively manufactured specimens made of acrylonitrile butadiene styrene (ABS) under uniaxial stress in a structural simulation. By using feedforward artificial neural networks (FFANN) for the ML-based direct inverse PI process, various investigations were carried out on the influence of sampling strategies, data quantity and data preparation on the prediction accuracy of the NN. Furthermore, the results of hyperparameter (HP) search methods are presented and discussed and their influence on the prediction quality of the FFANN are critically evaluated. The investigations show that the NN-based method is applicable to the present use case and results in material parameters that lead to a lower error between experimental and calculated force-displacement curves than the commonly used optimization-based method.