# Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering
## 5 Conclusions  
SMD refers to a machine used for assembling electronic components or semiconductors on electronic boards. Due to the characteristics of SMD, which is continuously assembled along the manufacturing line, a large loss can be prevented by immediately detecting an abnormality. The problem is that the types of SMD products produced according to the combination of electronic components and sensors vary, and accordingly, the number of anomaly detection models also increases linearly. To resolve these problems, in this work, we proposed a practical management method by clustering various products using the structural characteristics of an autoencoder. The method involves hierarchically clustering the latent vectors embedded in the encoder of the autoencoder, even with a strict threshold that can detect the _Error 1_ noise level, which is difficult for humans to detect. With one model trained only on the base product, up to 7 untrained normal products can be grouped and overseen, and for more efficient management, up to 30 untrained products can be grouped and overseen if they are grouped with a threshold that can detect the _Error 2_ noise level, which is relatively easy to detect. The findings suggest that our anomaly detection method allows for anomaly detection without the need to train individual anomaly detection models for each new SMD product, as long as the data of the new product exhibit similar characteristics to the previously training data. These results provide evidence that the proposed methodologies can effectively operate SMD anomaly detection models in real-world situations.  
Our research employs a threshold using abnormal data, but it primarily trains on normal data for anomaly detection. With the passage of time, the SMD assembly machine is expected to undergo aging, which can lead to changes in data characteristics even when the machine operates normally. Currently, our anomaly detection research does not encompass changes in data characteristics caused by machine aging. However, we plan to conduct future research on anomaly detection to account for such changes.  
Writing-original draft, Y.J.S.; Writing-review & editing, K.H.N.; Supervision, I.D.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science, Technology (no. 2019R1A2C1085113) and the 2023 Hankuk University of Foreign Studies Research Fund.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Appendix A presents the results of the hierarchical clustering using the proposed method as a dendrogram.  
Figure A2: Hierarchical clustering results of products that can oversee _Error 1_ as a grouping threshold. The green label refers to the base product used for training, and the blue label refers to the newly acquired normal data. The black label refers to the normal product data previously collected, the magenta dotted line refers to the _Error 1_ grouping threshold, and the red dotted line refers to the _Error 2_ grouping threshold.