SPP 2122: Materials for Additive Manufacturing

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Lasers in production are becoming increasingly powerful and brilliant, but the materials available are often completely inadequate for the processing tasks currently required. To date, metal powders are used in additive manufacturing that were developed over 50 years ago for a completely different process - thermal spraying. However, in modern laser-based additive processes, these powders lead to process instabilities, porosities, and defects in the component. In the field of polymer powders, there is also a lack of a wide range of materials. Therefore, there is an urgent need to adapt the materials to these widespread production processes, as laser-based processes will dominate important production processes in the long term due to their throughput and precision. In fact, a fundamental research approach already at the beginning of the process chain, the material, is required. Therefore, there is an urgent need for action to defend and further expand Germany's leading position worldwide in photonics and materials science. A coordinated, coherent research program combining materials development and photonics research for the first time, starting at the materials synthesis stage, should help exploit this considerable potential. To ensure feedback between process behavior and material properties, the SPP will fund tandem projects from the fields of "materials" and "laser process", which will cooperate across projects in thematic clusters. The scientific questions will be formulated across materials and focused on the photonic process of additive laser manufacturing. With this, for the first time, chemical, as well as metallurgical and additive-based modifications, will be developed specifically for photonic production. Such a large-scale interdisciplinary study requires targeted coordination and enables a unique Interlaboratory Study (Round Robin), including Research Data Management. Only by this, is it possible to generate an inter-laboratory scientific exchange, which guarantees reproducibility and statistical robustness.