Data-Based Process Monitoring for Structural Bonding of Fiber-Reinforced Thermoplastics in Aviation

The rising adoption of fiber-reinforced thermoplastics in aviation demands advanced structural bonding processes to ensure lightweight, high-performance components while meeting stringent quality requirements. This study proposes a semi-automated process setup combining automated and manual steps for structural bonding. The process encompasses four key stages: manufacturing of joining partners, surface treatment, adhesive application, and joining. A particular focus was placed on identifying and monitoring critical process parameters, enabling robust data acquisition and laying the groundwork for future quality assurance strategies. Digital tools, such as SCADA systems and edge devices, were integrated to enable real-time monitoring and data visualization. While the setup demonstrated the feasibility of reliable data collection in hybrid manufacturing environments, further work is needed to establish direct correlations between process parameters and bond quality. This research forms a foundation for developing AIbased predictive models and integrating advanced technologies, such as computer vision and digital twins, to optimize structural bonding processes and enhance their applicability in aviation. Although no empirical validation is presented at this stage, the system establishes the technical foundation for future experimental investigations of parameter-quality relationships in adhesive bonding.