Sebastian Hacker

Multi-axis fused-filament fabrication (FFF) expands the design space of polymer components by enabling spatially oriented filament paths. Existing stress-aligned deposition methods can align paths with principal stress directions, but they provide limited guidance for selecting local deposition strategies in mechanically critical regions where loads are introduced, transmitted, redirected, supported, or constrained. This paper proposes a functional region-based method for deriving reusable local deposition-design rules. The method identifies the local structural context and governing load-transfer mechanism in a representative bolted bracket and abstracts these regions into compression, open-hole tensile, and bearing-response benchmarks. The compression benchmark is evaluated experimentally. Candidate deposition strategies for the open-hole tensile and bearing-response benchmarks undergo qualitative comparative screening using an FEM model with the Tsai–Wu failure-initiation index. Selected configurations are further evaluated through physical testing on multi-axis FFF specimens. The physical tests show that continuous load-transmitting paths are favorable in the investigated open-hole and bearing-response cases, whereas adding local offset reinforcement did not automatically improve the tested configurations. The resulting deposition-design rules were applied to a representative load-bearing bracket, which reached 3.89 kN and provided component-level transfer-feasibility evidence under the investigated conditions.

Future cyber-physical systems (CPSs), integrating subsystems of the mechanical, electrical and software domains, are becoming increasingly interconnected and complex. As complexity grows, testing effort increases as well. This includes the test-case definition step, where the test targets and boundary conditions are specified. With rising system complexity, the effort required to ensure that all relevant conditions for each test target are identified increases. Manual test-case definition remains the norm, creating effort bottlenecks in ensuring systematic coverage and compliance with standards such as ISO 26262 and ISO 29119. This paper explores how large language models (LLMs) can support the identification of complex boundary conditions for CPS test cases through detailed requirement analysis. The impact of performing taxonomy-guided, structured requirement mapping prior to test-case generation was evaluated by comparing it with a version without this guidance. Furthermore, the influence of supplying a Model-Based Systems Engineering (MBSE) system model as context information via Graph RAG is examined. The results show that structured, stepwise reasoning significantly improves reliability and consistency over unguided generation, while system-model information provides valuable contextual insight but has a minor impact in the chosen example. These findings outline a scalable framework for AI-assisted test-case generation.