# In-Process Tool Condition Forecasting of Drilling CFRP/Ti Stacks Based on ResNet and LSTM Network
## 5 Conclusions  
In this paper, aiming to monitor and forecast tool wear of drilling CFRP/Ti stacks, an in-process TCF method based on deep learning has been proposed. Using the cutting spindle power and vibration signals preprocessed using the proposed method, the LSTM network with the ResNet-based model integrated can forecast tool-wear values of the next drilling holes. A case study shows the effectiveness of the proposed method. In addition, the results using raw measured signals and preprocessed datasets are tested for comparison. The proposed method can extract the sensitive signal features of tool wear for drilling CFRP/Ti stacks and improve the accuracy of TCF. Based on the work conducted, the following conclusions can be drawn.  
(1) The LSTM network can estimate tool-wear values of the next drilling holes, using the predicted values of the ResNet-based model as inputs. The results fit nicely with the  
Figure 11: TCF results.  
Figure 12: Comparison between models with data preprocessing and without preprocessing: (**a**) TCM results; (**b**) TCF results.  
measured tool-wear curve. Using historical cutting datasets, it is feasible to predict future cutting tool-wear values for drilling CFRP/Ti stacks.  
(2) For the LSTM network, the small errors accumulate to greater errors because the input-monitored tool-wear values may wave violently during the machining process. The proposed data correction method based on the preprocessed mean values can reduce the MAE to 20.43 \(\upmu\)m, decreasing the impact of data fluctuations.  
(3) Compared to using raw measured signals, the results of the TCF model using preprocessed data are better accuracy. The MAE of LSTM network results using raw signals are 45.01 \(\upmu\)m, which is 2.20 times bigger than that using preprocessed signals. The proposed data preprocessing method for drilling CFRP/Ti stacks can improve the tool-wear-forecasting accuracy, which meets the demand for online TCF.  
However, there are some limitations for this approach, which call for further research. More experiments for more drill bits should be conducted to test the model's robustness and reliability. Otherwise, the impact of the drilling workpiece's structure should be studied with various stiffness and different drilling parameters on the accuracy.  
Conceptualization, Z.J. and F.W.; methodology, Z.J. and F.W.; software, D.Z. and S.Z.; validation, Z.J. and R.F.; formal analysis, D.Z.; investigation, Z.J.; resources, Z.J. and F.W.; data curation, Z.J. and S.Z.; writing--original draft preparation, Z.J.; writing--review and editing, R.F.; visualization, S.Z.; supervision, F.W.; project administration, F.W.; funding acquisition, F.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (Grant No. 2018 YFA0702803), Liaoning Revitalization Talents Program (Grant No. XLYCYSZX1901), Liaoning Revitalization Talents Program (Grant No. XLYC1902014), Science and Technology Innovation Foundation of Dalian (Grant No. 2021 RD08).  
Not applicable.  
All the authors are greatly acknowledged for their financial support in making this research possible.  
The authors declare no conflict of interest.