## 5 Discussion
### The Performance of Different Fusion Weights  
In Section 3.3, the equation of the final prediction result is introduced and both networks had an average weight while calculating the final scores. This section will discuss the performance of fusion networks under different weight circumstances. Here, the weight of the arbitrary network decreased from 0.9 to 0.1 while the other weight increased from 0.1 to 0.9, respectively. In this experiment, all the implementation details were taken according to Section 3.1 and by applying 50 samples from the NEU dataset as the training set. Based on the experiment results, the level 1 models were able to enhance the performance of the level 0 models. According to the report in Section 3.3, the level 0 models had an average accuracy of 99.30%. Hence, the accuracy of the fusion networks was improved when comparing the results below with the level 0 models. Obviously, once the weight of the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Cr** & **In** & **Pa** & **Ps** & **Rs** & **Sc** & **Precision** \\ \hline
**Cr** & **99.12** & 0.00 & 0.00 & 1.40 & 0.60 & 0.00 & 98.02 \\
**In** & 0.00 & **97.20** & 0.00 & 3.00 & 0.00 & 4.00 & 93.28 \\
**Pa** & 0.04 & 0.00 & **100.00** & 0.00 & 0.00 & 0.00 & 99.96 \\
**Ps** & 0.04 & 1.88 & 0.00 & **94.12** & 0.08 & 0.68 & 97.23 \\
**Rs** & 0.80 & 0.12 & 0.00 & 1.12 & **99.28** & 0.44 & 97.56 \\
**Sc** & 0.00 & 0.80 & 0.00 & 0.36 & 0.04 & **94.88** & 98.75 \\
**Recall** & 99.12 & 97.20 & 100.00 & 94.12 & 99.28 & 94.88 & **97.43** \\ \hline \hline \end{tabular}
\end{table}
Table 9: The confusion matrices when adopting the MRFFN+IA method on motion length 20 task containing six types of defects (%), where Cr, In, Pa, PS, RS, and Sc denote the crazing, inclusion, patches, pitted surface, rolled-in scale, and scratches defects respectively.  
level 0 models was greater than or equal to the level 1 models, the overall results of the fusion networks was higher than the level 0 models. In Figure 11, it can be seen that the accuracy of the fusion network was increasing when the weight of the level 0 models decreased. The results show that the fusion network had the highest accuracy of 99.61% when applying an average weight for both networks. However, once the level 1 models had a greater influence than the level 0 models, the performance of the fusion networks decreased gradually. Thus, these results indicated that the average weight was the most conducive for improving the performance of both networks. Lastly, in order to solve the divergent results between the fusion networks, the weights of the level 0 and level 1 models were set as 0.6:0.4 when the final results contained the two highest scores, as it achieved the second-highest accuracy among the other weights.  
Figure 11: The performance of different fusion weights. \(w_{0}\) and \(w_{1}\) denote as the fusion weight of level 0 and level 1 models.  
Figure 10: The accuracies distribution of individual and fused networks based on NEU dataset. (**a**) Individual network (**b**). Fused two networks. (**c**) Fused three networks. (**d**) Greatest performance of individual and fused networks.