# In-Process Tool Condition Forecasting of Drilling CFRP/Ti Stacks Based on ResNet and LSTM Network
## 1 Introduction  
Carbon fiber-reinforced plastic (CFRP) has been widely used in aviation parts to reduce their weight, due to its excellent material properties, such as high specific strength and specific stiffness. Meantime, traditional metallic materials, such as titanium alloys, are still irreplaceable for key structural components due to their excellence in bearing multi-directional loads. As such, CFRP/Ti stacks structures are formed, and tens of thousands of high accuracy holes need be drilled for bolted or riveted assembling [1]. The reliability and safety of equipment will be directly affected by the drilling quality [2, 3]. However, CFRP and Ti titanium are both well-known as hard-to-cut materials and have distinguished cutting performance. These would lead to severe and rapid tool wear, consequently increasing the risk of drilling defects. As most of these stack holes are produced by drilling in the assembly site, even a slight defect would be a risk of huge losses of the whole assembled parts; therefore, the cutting tool condition needs to be precisely monitored for further constant control of drilling process. For a given tool, the standard rule of presetting a conservative tool life is still utilized in many industrial scenes, which is not realistic and relies on operator skills. In this case, premature failure occurs occasionally, which causes unexpected material losses, machine downtime and even equipment damage, while another tool may exceed the preset life [4]. Normally, only 50-80% of a cutting tool's life is actually used in order to reduce the risk of scrapped parts and unexpected downtime [5, 6]. Automatic monitoring technology is the key component of unmanned machining, as it can help to avoid damages and reducethe production costs in industries [4]. However, it is not feasible to measure tool-wear conditions directly in the continuous machining process, due to the harsh machining environment. Data-driven intelligence has achieved good performance in enabling in-process tool-condition monitoring (TCM) and tool-condition forecasting (TCF) with the rapid application and development of digital technology in the current era of Industry 4.0, such as smart sensors, signal acquisition and data analytics [7; 8]. TCM is used to predict the current tool conditions based on measuring signals, while TCF forecasts tool conditions with the cutting tool in continued use. Compared with current states' monitoring, TCF is more meaningful for intelligent manufacturing [9]. The measured signals consist of cutting force [10; 11], vibration [12; 13; 14], acoustic emission [15], and current [16; 17; 18], which are widely adopted as the input data in the literature. Cutting force is the most stable and reliable signal; it is very sensitive to tool wear [19], but difficult to apply due to the high cost and limited size of measuring instruments [20]. It remains difficult for the industrial application of acoustic emission sensors because the signals are easily affected by various factors, such as sensor location and environmental noise [21]. Compared to cutting force and acoustic emission, cutting vibration and current/power signals are easy and economical to acquire. In addition, multiple sensors and fusion can be utilized for improving the deficiency of single sensor [22; 23], such as low signal-to-noise ratio and sensitivity to the mechanical system characteristics, and increase the reliability of TCM and TCF systems. Thus, in this paper, the cutting spindle power and vibration for drilling CFRP/Ti stacks are used for tool-wear forecasting in order to facilitate its application in the production site. The cutting signal changes are not only affected by the tool conditions, but are also related to the machining variables, such as cutting parameters, clamping method, and cooling/lubrication conditions. The relationships between signals and tool conditions are nonlinear and complex, and classification and regression models are essential to map from measured signals to cutting tool conditions.