The utilization of Carbon Fiber Reinforced Plastic (CFRP) is increasing in the aerospace industry due to its high strength-to-weight ratio and low weight . However, machining of CFRP to achieve the required surface quality remains a challenge. During machining of CFRP, abrasion and chipping are known as the major tool wear issues. Tool wear affects the rate of material removal and the quality of the machined surface . While machining CFRP, the cutting tool must maintain a suitable level of edge sharpness in order to provide a clean cut at the end. During composite machining, minimizing and controlling tool wear is critical to avoid degrading the finished surface and losing dimensional accuracy of the final part . Any failure may result in workpieces being rejected in the production line. Fiber pull-out or breakage, matrix smearing, or delamination may occur during the machining of CFRP . To ensure product quality at the end of the finishing operation, direct or indirect methods of tool condition monitoring can be used . In the direct method, the geometric parameters of the cutting tool are measured using an optical microscope with a high degree of accuracy . This method has real-time limitations since it requires interrupting the cutting process to estimate the tool wear. Moreover, the direct method requires appropriate laboratory equipment, which is a limitation in harsh industrial machining applications . The indirect method for tool wear monitoring is instead based on real-time analysis of signal acquisition during machining. This online approach is more appropriate for industrial applications that require few laboratory equipment and seek for automation of production processes to increase product quality and decrease operating costs . It has been shown that such tool condition monitoring in an automated machining center can lead to early detection of tool wear, boost cutting processes speed by 50 percent, and lower the manufacturing costs from 10 percent to 40 percent [7, 8]. Recently, Hassan et al.  developed the Wavelet Scattering Convolution Neural Network (WSCNN) technique to extract distortion-stable features from vibration signals generated by tool wear. Large-scale experimental validation tests under various cutting conditions revealed that the WSCNN method could achieve 98 percent detection accuracy in tool conditions and minimize system training by up to 97 percent.
Due to the sensitivity of cutting forces related to cutting conditions, the force signals have been widely used in tool condition monitoring . Hu et al.  could predict distinct tool wear states, using statistical features of cutting force and acoustic emission signals during machining titanium alloy. This study employed Mutual Information (MI) and ν-Support Vector Machine (ν -SVM) for model training and prediction. The proposed strategy could successfully predict different tool wear states, with a prediction accuracy of 98.9 percent. Despite the capability of cutting force signal to detect tool wear, the acquisition of cutting forces requires sensors, such as dynamometers, which are not practical or cost-effective to use in production . Alternatively, any changes in the cutting state can be reflected in the electric current signal of machine tools. Jeong and Cho  succeeded in estimating the cutting forces normal to a machined surface using the stationary feed motor current with less than 20 percent error. Current sensors are generally inexpensive and reliable and can be located far from the machining area . However, developing a reliable method to reveal inherent patterns hidden in the current signal remains a challenge. Different approaches have been developed to analyze various online tool condition monitoring signals. Recently, an Artificial Neural Network (ANN) was applied during machining to classify the tool wear states in real-time using acceleration data  and acoustic emission signals . Fuzzy logic was also introduced as another possible approach for tool condition monitoring by analyzing the uncertainties in acoustic emission signals . Fractal analysis was recently developed as a new approach in tool condition monitoring. For the first time, the concept of fractal was used by B.B. Mandelbrot to estimate the length of the British coastline. Fractal objects are irregular shapes with affine structure and a sort of self-similarity. They have a fractal dimension that is greater than the topological dimension . Fractal analysis was widely applied in advanced surface roughness evaluation , and it was also utilized in machine maintenance and diagnosis improvement . Recently, Jamshidi et al.  analyzed the cutting force signal using fractal analysis to monitor the tool condition. In this study, fractal parameters of cutting force signals while drilling CFRP/titanium stacks of material was estimated to identify distinct wear stages of the cutting tool.
The present study investigates the online tool condition monitoring using fractal analysis of the spindle current signal and the total cutting force signal while trimming Carbon Fiber Reinforced Plastics (CFRP). The proposed approach investigates the feasibility of using an electric current signal to monitor tool condition and achieve the desired dimensional accuracy or surface integrity. Inexpensive and reliable current sensors, which meet industrial requirements, provide helpful information about the tool condition. This study aims to demonstrate the robustness of the fractal analysis as a decision-making system in tool condition monitoring using electric current signal.