Abstract
Composite material parts are typically laid out in near-net-shape, i.e., very close to the finished product configuration. However, further machining processes are often required to meet dimensional and tolerance requirements. Drilling, edge trimming and slotting are the main cutting processes employed for carbon fiber-reinforced plastic (CFRP) composite materials. In particular, drilling stands out as the most widespread machining process of CFRP composite parts, chiefly in the aerospace industrial sector, due to the extensive use of mechanical joints, such as rivets, rather than welded or bonded joints. However, CFRP drilling is markedly challenging: due to CFRP abrasiveness, inhomogeneity and anisotropic properties, tool wear rates are inherently high leading to superior cutting forces and detrimental effects on workpiece surface quality and material integrity. Damage such as delamination, cracks or matrix thermal degradation is often observed as the result of uncontrolled tool wear or improper machining conditions. Sensor monitoring of drilling operations is, therefore, highly desirable for process conditions’ optimization and tool life maximization. The development of this kind of automated control technologies for process and tool state evaluation can notably contribute to the reduction of scraps and tool costs as well as to the improvement of process productivity in the drilling of CFRP composite material parts. In this paper, multi-sensor process monitoring based on thrust force and torque signal detection and analysis was applied during drilling of CFRP/CFRP laminate stacks for the assembly of aircraft fuselage panels with the scope to evaluate the tool wear state. Different signal-processing methods were utilised to extract diverse types of features from the detected sensor signals. A machine-learning approach based on an artificial neural network (ANN) was implemented to make smart decisions on the timely execution of tool change, which is highly functional for CFRP drilling process automation.
Highlights
In the aerospace industry, weight reduction is critical to meet environmental requirements and to reduce management costs
The root mean square error (RMSE) values obtained from a fourth feature pattern vectors (FPV), called combined feature pattern vector FPVcomb, which includes both time domain and fractal analysis features is a sensor fusion approach, are reported in the last column of the table
0.00347. laminate stack drilling for aeronautical assembly was carried out based on thrust force and torque signal detection and analysis for the on-line
Summary
Weight reduction is critical to meet environmental requirements (lower emissions) and to reduce management costs (lower fuel consumption). Drilling of CFRP laminates is frequently carried out manually and cutting tools are often replaced largely before the end of tool life to avoid material damage due to early tool failure [6] This has a quite negative effect on the cost and productivity of drilling operations. Drilling of CFRP composite parts is a challenge for manufacturing engineers due to the anisotropic nature of the material, the rapid tool wear caused by abrasive carbon fibers, and the highly concentrated stresses and vibrations These phenomena may cause critical defects affecting material integrity, surface quality and part acceptability: hole entry and hole exit delamination, geometrical and dimensional errors, interlaminar delamination, fiber pullout, and thermal damage [7,8,9]. A machine-learning approach based on artificial neural network (ANN) data processing was implemented using selected signal features to make smart decisions on the timely execution of tool change, which is highly functional for CFRP drilling process automation, on the basis of tool wear level estimation and tool wear curve reconstruction
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