Penetration Prediction on Laser Welding (test)
98.67AccuracyTimeSformer + MFN-Mamba
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| TimeSformer + MFN-MambaFeature Extraction Network=TimeSformer, Feature Fusion Network=MFN-Mamba, Inference time (ms)=140.152026.06 | 98.67 | 98.54 | 98.76 | 98.65 | |
| CNN + TCN + MFN-MambaFeature Extraction Network=CNN + TCN, Feature Fusion Network=MFN-Mamba, Inference time (ms)=95.802026.06 | 98.15 | 98.2 | 98.02 | 98.11 | |
| CNN + Pyraformer + MFN-MambaFeature Extraction Network=CNN + Pyraformer, Feature Fusion Network=MFN-Mamba, Inference time (ms)=129.432026.06 | 97.9 | 98.01 | 97.71 | 97.86 | |
| CNN-LSTM + MFN-MambaFeature Extraction Network=CNN-LSTM, Feature Fusion Network=MFN-Mamba, Inference time (ms)=63.152026.06 | 95.63 | 95 | 96.27 | 95.63 | |
| ConvLSTM + Type IIFeature Extraction Network=ConvLSTM, Feature Fusion Network=Type II, Inference time (ms)=71.962026.06 | 92.71 | 91.49 | 94 | 92.72 | |
| CNN-LSTM + MFNFeature Extraction Network=CNN-LSTM, Feature Fusion Network=MFN, Inference time (ms)=50.662026.06 | 91.79 | 92.86 | 92.13 | 92.49 | |
| ConvLSTM + Type IFeature Extraction Network=ConvLSTM, Feature Fusion Network=Type I, Inference time (ms)=69.412026.06 | 91.58 | 88.09 | 87.48 | 87.78 | |
| CNN-LSTM + Type IIFeature Extraction Network=CNN-LSTM, Feature Fusion Network=Type II, Inference time (ms)=41.892026.06 | 90.93 | 88.75 | 93.16 | 90.9 | |
| CNN-LSTM + Type IFeature Extraction Network=CNN-LSTM, Feature Fusion Network=Type I, Inference time (ms)=40.772026.06 | 87.15 | 81.84 | 90.76 | 86.06 |