The industrialization of AM is only possible by creating synergies with the tools of Industry 4.0. The system technology of Powder Bed Fusion with Laser beam of Metals (PBF‑LB/M) reached a level of high performance in terms of process stability and material spectrum. However, the digital process chain, starting from CAD via CAM and plant-specific compilation of the manufacturing file exhibits media disruptions. The consequence is a loss of metadata. A uniform data scheme for Design for Additive Manufacturing (DfAM), the PBF‑LB/M process itself, simulations and quality assurance is currently not realized within industry. There is no entity in the common data flows of the process chains, that enables the integration of these functionalities. As part of test bed for the quality assurance in AM within the initiative Quality Infrastructure (QI)‑Digital, an integration of the CAD/CAM chain is being established. The outcome is a file in an advanced commercially available format which includes all simulations and manufacturing instructions. The information depth of this file extends to the level of the scan vectors and allows the automatic optimization and holistic documentation. In addition, the KPIs for the economic analysis are generated by compressing information into a unique file combined with the application of a digital twin (DT). The implementation and advantages are demonstrated in a case study on a multi-laser PBF‑LB/M system. A build cycle containing a challenging geometry is thermally simulated, optimized, and manufactured. To verify its suitability for an Additive Manufacturing Service Platform (AMSP), the identical production file is transferred to a PBF‑LB/M system of another manufacturer. Finally, the achieved quality level of the build cycle is evaluated via 3D scanning. This evaluation is carried out in the identical entity of the production file to highlight the versatility of this format and to integrate quality assurance data.

The emerging multimaterial technology is extending the potential applications of additive manufacturing in many areas. However, these possibilities also bring new challenges such as creating a sufficiently strong bond between materials and ensuring their continued recyclability. This paper presents a method for designing structures for joining different materials based on the use of experimentally determined values and artificial intelligence.
The objective of the method is to find, for a given design space, an arrangement of the various materials that provides a high strength of the material composite in one loading direction and, at the same time, is designed to result in a significantly reduced strength for a further loading direction. As a result, the material composite should break at this predetermined breaking point when loaded in the second direction, so that the materials involved can be recycled. To be able to perform the calculation as quickly as possible, the geometry is reduced to arrangements of small cubes (voxels).
Four steps are provided for the use of this method. In the first step, the maximum resolution that can be achieved with the respective additive process is determined. In a further step, different arrangements of voxels are examined for resulting strength using tensile tests. In the next step, the results of these tests serve as input values for an AI application that finds an arrangement of the voxels that provides the desired strengths in the various load directions, taking into account a given design space. A genetic algorithm is used to geometrically optimize the joint. Finally, these designs are used to automatically build a CAD model that enables additive manufacturing of the components.
Initial investigations into the voxel sizes and manufacturability of the multimaterial joints using the material extrusion (MEX) process are presented, but evaluation of the overall method is still pending.

Additive manufacturing (AM) framework enables the customization of geometries regardless of the number of parts to manufacture. Hence, the right product can be provided to the right customer. However, traditional optimization methods, used to improve performance and mass, are usually non-parametric. Therefore, the process flow shall be repeated even if all parts in a library display similar functions and overall geometry. As such, the relation between development time and the number of parts to design is linear. Hence, one cannot afford to generate large libraries using these approaches.

In this paper, we propose an alternative solution for structural design, inspired by Knowledge Based Engineering. First, topology optimization is used to emulate a prior knowledge of optimal geometry. As this result is nonparametric, it is approximated using low complexity elements such as shells and beams. Then, batch optimization is run to model the optimal geometry in function of the load case. Finally, the ready to print part is reconstructed using traditional parametric CAD software.

We benchmarked our methodology with the Design for AM (DfAM) of a library of chassis components. Thanks to the selective complexity reduction, the computation time is reduced significantly compared to traditional approaches. Furthermore, the parametrization of the part and the modeling of its behavior rationalize the design of families of parts. Indeed, most activities are only executed once.

In this manner, our methodology uncouples the development time from the number of load cases allowing to design for AM entire libraries of similar part.