## 3 Results and Discussion
To analyze the classification of incomplete fusion welds and normal welds further, AUC, specificity and processing time are adopted, as listed in Table 6. The average training time and average test time over the range of \(c\) or \(nu\) are used to compare the model efficiency. The LOF model is time-saving for model construction in this application, but it cannot identify all the incomplete fusion welds. Hence, its AUC is regarded as NA. The performance of OCSVM for anomaly detection is not poor; however, it is relatively time-consuming to train and test the model. The identification of incomplete fusion welds and normal welds can be performed effectively and efficiently by the iForest model. It has a high AUC score of 0.9525, and it takes 1.79 s to train the model with over 60,000 welds in the training set, and 1.11 s to test over 47,000 welds. In addition to identifying abnormal welds, the interpretation of anomalies is also important for quality monitoring. For incomplete fusion welds, one of them and its previous 10 welds are shown as an example in Figure 9(a),c,d. It can be observed that this abnormal weld has a different \(R_{t}\) profile and smaller \(Q_{E}\). The previous welds have an 'Up&Down' profile in the 1 st pulse, while the abnormal weld has an 'Up-Down' profile in the double pulses. This implies that the insulation coating was not properly removed as designed in the 1 st pulse for this abnormal weld. Since the welding time was fixed, this weld lacked enough energy to join the wire to the pad after the insulation coating was removed. Based on the assumption that normal welds are'many and similar' and anomalies are 'few and different', the iForest model can detect incomplete fusion welds with good performance in this application. For other defect types, one from abnormal wire welds is analyzed as an example (Figure 9(b)). It was caused by the deviation of wire position. As shown in Figure 9(e),f, it overlaps with its previous 10 welds in terms of \(R_{t}\) profile. This is related to the characteristics of the MRSW process for micro enameled wire joining, implying that the case of abnormal wire which occurs in the early and middle stage of electrode life does not obviously influence the temperature rise of electrode tip and tinned pad. Therefore, there is no remarkable difference in either \(\Delta Q_{\text{10N}}\) or \(\Delta S_{\text{10N}}\) between normal welds and these abnormal welds, meaning that the models cannot detect them effectively. Although only incomplete fusion can be detected by the present iForest model with good performance, it is favorable for the quality monitoring research for MRSW. Its detection requires inspectors to be experienced and focused, which makes it relatively difficult to perform visual testing in the production line, while the deviation of wire position can be identified as abnormal wire with relative ease by a machine vision system. To further separate various abnormal welds from normal welds, more features from other process signals are required to fully represent the welding process. Similar to NSW, the quality monitoring based on multiple signals may be better for MRSW than that based on electrical signals only. Electrode displacement, dynamic force and images before and after welding are expected to be added in future work to improve quality monitoring. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**AUC**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**Processing Time (s)**} \\ \cline{3-6}  & & **Specificity of 90\%** & **Specificity of 80\%** & **Train** & **Test** \\ \hline
**iForest** & 0.9525 & 0.8633 & 0.9524 & 1.79 & 1.11 \\ OCSVM & 0.9047 & 0.6437 & 0.9037 & 53.29 & 9.46 \\ LOF & NA & 0.1429 & 0.2857 & 0.43 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: AUC, recall and processing time.