Additive manufacturing (AM) technologies, such as laser-based powder bed fusion of metals (PBF-LB/M), allow for the fabrication of complex parts due to their high freedom of design. PBF-LB/M is already used in several different industrial application fields, especially the automotive and aerospace industries. Nevertheless, the amount of materials being processed using AM technologies is relatively small compared to conventional manufacturing. Due to this, an extension of the material portfolio is necessary for fulfilling the demands of these industries. In this work, the AM of case-hardening steel 16MnCr5 using PBF-LB/M is investigated. In this context, the influences of different processing strategies on the final hardness of the material are studied. This includes, e.g., stress relief heat treatment and microstructure modification to increase the resulting grain size, thus ideally simplifying the carbon diffusion during case hardening. Furthermore, different heat treatment strategies (stress relief heat treatment and grain coarsening annealing) were applied to the as-built samples for modifying the microstructure and the effect on the final hardness of case-hardened specimens. The additively manufactured specimens are compared to conventionally fabricated samples after case hardening. Thus, an increase in both case-hardening depth and maximum hardness is observed for additively manufactured specimens, leading to superior mechanical properties.

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Today’s manufacturing facilities and processes offer the potential to collect data on an unprecedented scale. However, conventional Programmable Logic Controllers are often proprietary systems with closed-source hardware and software and not designed to also take over the seamless acquisition and processing of enormous amounts of data. Furthermore, their major focus on simple control tasks and a rigid number of static built-in I/O connectors make them not well suited for the big data challenge and an industrial environment that is changing at a high pace. This paper, advocates emerging hardware- and I/O reconfigurable Programmable System-on-Chip (PSoC) solutions based on Field-Programmable Gate Arrays to provide flexible and adaptable capabilities for both data acquisition and control right at the edge. Still, the design and implementation of applications on such heterogeneous PSoC platforms demands a comprehensive expertise in hardware/software co-design. To bridge this gap, a model-based design automation approach is presented to generate automatically optimized HW/SW configurations for a given PSoC. As a case study, a metal forming process is considered and the design automation of an industrial closed-loop control algorithm with the design objectives performance and resource costs is investigated to show the benefits of the approach.

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