Current developments towards autonomous drive and increasing numbers of car sharing users are likely to detach vehicle occupants from awareness of vehicle state. This affords increased condition monitoring capabilities of road vehicles with a focus on safety-relevant components as e.g. found in the chassis and suspension system. Surface attachment of sensors to these parts, which are typically subjected to harsh environmental conditions, is prone to failure of the sensor. Integration of the sensors within the components, however, is rendered difficult due to the manufacturing process: Components like wheel carriers are usually produced by casting, and specifically by low pressure die casting (LPDC). Integration of sensors in cast components is difficult due to the high process temperatures, which typically exceed 700°C in the case of aluminum casting, though not impossible [1]. In terms of high pressure die casting (HPDC), several successful case studies are known, however, beyond slower cooling, LPDC of chassis components adds a further complication due to the fact that these parts are typically subjected to a T6 or T7 type heat treatment, which includes solution heat treatment at temperatures of approx. 530°C extending over several hours for the common LPDC alloy AlSi7Mg0.3. In the course of the Fraunhofer-DFG transfer project smartCAST, thick film printing techniques have been employed to produce sensors on (a) aluminum alloys and (b) LTCC substrates. Initial studies have proven the general feasibility of the former approach, but also difficulties encountered when relying on a hybrid design employing thin film techniques for realization of the sensor itself [2]. The present study is focused on the transfer of the initial experimental validation to an actual automotive component, a wheel carrier, and the evaluation of the second, LTCC-based approach. The studies conducted include characterization of the sensors before and after casting and heat treatment, and the evaluation of their response to mechanical loads in integrated state. The data obtained is matched with Finite Element analyses of the loaded component and process simulation using the MAGMASOFT® casting simulation software package.

References
[1] D. Lehmhus, T. Rahn, C. Pille, M. Busse Lecture Notes in Networks and Systems, 2022, 546, 350-361.
[2] D. Lehmhus, M. Cen, A. Struss, T. de Rijk et al. Journal of Physics: Conference Series, 2024, 2692, 012007.