# In-Process Tool Condition Forecasting of Drilling CFRP/Ti Stacks Based on ResNet and LSTM Network
## 1 Introduction
Machine learning plays an important role in classification and regression, and has been extensively used in different engineering applications, such as laser welding, robotics optimization, etc., having achieved good results [24; 25; 26; 27]. For many tool condition classification and regression methods, the models have been presented using support vector machine [16; 17], hidden Markov model [28], and fuzzy logic [29]. However, for tool-wear forecasting, deep learning models have been verified to outperform the traditional techniques [30] regarding the capture of the nonlinear dependencies. For example, [PERSON] et al. presented a TCM approach for end mill based on the deep convolutional neural network (CNN) [31]. [PERSON] et al. used deep CNN to propose a tool-wear-predicting model for milling operation of a three-flute cutter [32]. The CNN model can combine multi-domain feature fusion, overcoming the deficiencies of manual feature fusion, which possesses the advantage of extracting high correlated signal features. [PERSON] et al. proposed a tool wear evolution monitoring method on the basis of random forest machining learning for drilling CFRP automatically and emphasized it is effective as a process control indicator [33]. [PERSON] et al. proposed a LSTM model for tool flank wear prediction during the turning of a steel alloy [34]. The LSTM model contained two layers and eight hidden units, using spindle current signals, and demonstrated the highest accuracy. However, the turning condition for predicting tool wear is simple and stable. LSTM is an appropriate method for learning the process features of different time periods. [PERSON] et al. presented a novel method for tool wear monitoring using a parallel ResNet to extract multi-domain signals' features and using a stacked bidirectional LSTM to obtain time series features [35]. The raw signals of cutting force, vibration and acoustic emission for milling single material are utilized as inputs to overcome the low efficiency of manually selecting signal features. However, data are still the cornerstone for these approaches, and the accuracy and robustness are directly related to the quality of signal features. Unfortunately, for CFRP/Ti stacks, the cutting spindle power and vibration signals change are complex, influenced by more factors, compared to single material stack, due to the different materials properties. The sensitive features are difficult to extract. The effectiveness of deep learning models for tool-wear forecasting of drilling CFRP/Ti stacks remains questionable and need to be studied, as it is rarely reported in the literature. In this paper, an in-process TCF method of drilling CFRP/Ti stacks based on a ResNet and LSTM network has been proposed. The cutting spindle power and vibration signals are measured and preprocessed. The ResNet-based model is utilized for predicting the current tool wear. Then, an LSTM network is used to estimate tool wear of the next drilling holes using the predicted latest tool-wear values as inputs. The identification accuracy is studied through an experiment, and the results using raw measured signals and preprocessed datasets are tested for comparison. The proposed data preprocessing method for drilling CFRP/Ti stacks can improve the tool-wear-forecasting accuracy, which meets the demand for online TCF. The proposed TCF model is effective in estimating tool wear of the next drilling holes. The structure of this paper is as follows. Section 2 presents the proposed methodology of the TCF model for drilling CFRP/Ti stacks, including the signal preprocessing method, the ResNet-based TCM model, and the LSTM network. The experimental details for drilling CFRP/Ti stacks are presented in Section 3. Section 4 presents the verifications and discussions of the methods using measured cutting signals and tool-wear values, followed by the conclusions, which are drawn in Section 5.