Material recognition method to enable adaptive drilling of multi-material aerospace stacks

Multi-material stacks comprising composite and metallic layers are widely used in aerospace components. For the assembly of structural part, high-quality holes are required to ensure the performance of the mechanical fastening (rivet/bolt). On the final assembly lines of aircraft structures, these machining operations are often performed using an electric automated drilling unit (eADU). When drilling hybrid stacks, the difference in machinability of the materials (CFRP, titanium alloy, and aluminum alloy) makes it difficult to avoid delamination, fibre pullout, matrix degradation, burrs, roughness, and size defects. Therefore, each material must be drilled with suitable machining conditions to meet the demanding quality requirements. To this end, automated material detection would allow for the adjustment of appropriate cutting parameters for each material. Due to the numerous stack configurations (material, thickness) and the variability of process parameters (tool geometry, cutting conditions, lubrication, etc.), automated material detection is not an easy task. To address this issue in eADU applications, this paper presents a novel approach to identify during the process the drilled material being manufactured using a random forest (RF) machine learning model and multi-sensor data fusion. Cutting forces, vibration, micro-lubrication conditions (flow rate and pressure), and eADU spindle and feed motor currents are monitored on a dedicated drilling test rig. Numerous tests were performed on Al7175/CFRP stacks with different cutting conditions to validate the proposed methodology. Advanced signal processing and analysis in the time and frequency domains are used for feature extraction to identify Al7175 from the CFRP. The input features of the RF model were selected using the feature importance measure embedded in the RF model learning process. The knowledge process of drilling multiple material stacks is also considered. The results showed that the features extracted from the frequency domain are more effective in identifying the CFRP Al7175 than those extracted from the time domain. This optimal subset was then used to build the RF model. The proposed methodology resulted in a highly accurate classification allowing the implementation of an adaptive machining process on the eADU for hybrid stack drilling.