## 5 Conclusions and Future Work  
This paper presents a novel and effective approach to the task of machining feature recognition with limited labeled samples through the use of a semi-supervised learning framework. The key contribution of this framework is the incorporation of self-supervised learning, which enables the learning of useful visual representations from large-scale unlabeled data. Then, the backbone network is fine-tuned with small labeled data, utilizing the knowledge gained from self-supervised learning. Ablation experiments were performed to demonstrate the effectiveness of the proposed method. The MsvNetLite outperformed the MsvNet by approximately 19% with only one training sample per class, whereas the FeatureNetLite outperformed the FeatureNet by about 20% in machining feature classification. The t-SNE visualization technique was used to provide an intuitive visualization of the effects of the proposed framework on class separation and visual representation learning. Additionally, a large number of hyper-parameter search experiments were conducted to select the optimal semi-supervised learning settings for machining feature recognition. Moreover, lightweight techniques were applied to the CNNs to reduce memory usage and increase inference speed. Our study shows that the proposed compressing approaches have effectively reduced the model parameters and computing complexity of the current approaches. Specifically, the MsvNetLite demonstrated a 1/76 reduction in parameters and a 1/47 reduction in FLOPs compared to the MsvNet, whereas the FeatureNetLite showed a 1/89 reduction in parameters and a 1/18 reduction in FLOPs compared to the FeatureNet. These results highlight the efficiency of our proposed compressing approaches. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Optimizer for Fine-Tuning**} & \multicolumn{6}{c}{**Number of Labeled Data per Class for Fine-Tuning**} \\ \cline{2-9}  & **1** & **2** & **4** & **8** & **16** & **32** & **64** & **128** \\ \hline Adam & 15.39 & 21.62 & 13.21 & 24.08 & 26.42 & 81.99 & 94.93 & 96.15 \\ SGDM (\(momentum=0.9\)) & **69.81** & **76.04** & **87.74** & **93.95** & **97.15** & **97.56** & **98.03** & **98.84** \\ SGD & 66.98 & 73.85 & 82.08 & 83.70 & 93.83 & 96.24 & 97.99 & 98.53 \\ RMSProp & 7.71 & 7.17 & 9.19 & 8.17 & 10.24 & 44.97 & 80.12 & 89.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The recognition accuracy (%) of the MsvNetLite with different optimizers for fine-tuning. The bold number is the best result in each column. On a common X86 CPU, the MsvNetLite demonstrated 6.68\(\times\) improvement in speed over the MsvNet, and the FeatureNetLite was 2.49\(\times\) faster than the FeatureNet in terms of inference speed. The proposed models were proven to be more efficient in comparison to several other machining feature recognition algorithms, achieving the best balance between classification performance and inference speed. However, there are some future research directions as follows:  
* Better self-supervised representation learning methods will be explored. In the future, we will improve the effectiveness of the self-supervised training stage to learn more stable pre-trained knowledge. * We will continuously focus on the robustness of semi-supervised hyper-parameters. We will carry out a series of experiments with different hyper-parameters to understand how the hyper-parameters effect the training process. * Our further research will consider the application of multiple, intersecting, and incomplete feature recognition. We will try to use the proposed approach to boost the performance of the current intersecting feature recognition method. Therefore, we will carry on improving our framework in future work. Conceptualization, H.W. and Y.P. ; Data curation, H.W. ; Formal analysis, H.W. ; Funding acquisition, Y.P. ; Investigation, H.W. ; Methodology, H.W. ; Project administration, Y.P. ; Resources, R.L. ; Software, H.W. and R.L. ; Supervision, P.H. and Y.P. ; Validation, H.W., R.L. and Y.P.