Experimental framework for investigating manual drilling in aircraft assembly

In aircraft structural assembly, a substantial share of rivet holes is drilled manually using handheld machines. In contrast to semi-automatic drilling machines, operations with handheld tools are subject to process-inherent parameter variations such as fluctuations in feed force and differences in tool alignment, both of which strongly influence hole quality and tool wear. Moreover, handheld drilling machines typically lack integrated sensing capabilities, further complicating process monitoring and quality assurance. To address these challenges, a dedicated experimental test rig for manual drilling is developed and tested. The setup enables systematic investigation of manual drilling operations under controlled conditions, incorporating external sensors to capture key process signals such as feed force, spindle speed, torque and feed travel. Based on these measurements, derived parameters such as feed rate and drilling time can be analyzed to study their relationship to hole exit quality, particularly delamination in carbon fiber reinforced polymer (CFRP) material. Initial investigations using Random Forest classification and permutation importance to identify significant process characteristics demonstrate that higher feed rates right before tool exit correlate with increased delamination, consistent with the findings from automated drilling research. The results further reveal a strong influence of operator- induced variability and tool wear on the measured process parameters, with feed force variations exceeding 40 N within individual drilling operations. This emphasizes the need for systematic understanding of human–machine interactions in manual drilling and motivates the development of monitoring and assistance systems capable of compensating operator-induced variations. The presented experimental framework provides a foundation for quantitative analysis of manual drilling behavior and serves as a data-driven basis for future work on process monitoring, anomaly detection, and operator feedback control.