Multi-modal contrastive causal consistency fusion for anomaly detection in additive manufacturing

With the increasing demand for intelligent and efficient process monitoring in additive manufacturing (AM), multi-sensor data fusion has shown superior anomaly detection accuracy over single-modality sensor systems. However, cross-modal data exhibit substantial differences in feature distributions, presenting challenges for their fusion. To tackle these challenges, this paper proposes an anomaly detection method using multiple sensor modalities, integrating their data via a causal approach. First, a contrastive feature extraction method is introduced to identify anomaly-sensitive features within each sensor modality. Second, causal consistency alignment is utilized to exploit the causal relationships among cross-modal data, thereby facilitating collaborative learning across multi-sensor data during the AM process. Third, a collaborative fusion strategy based on global attention mechanisms using transformers is proposed to adaptively fuse multi-modal features for anomaly detection tasks. Finally, the real fused deposition modeling (FDM) dataset, sourced from an AM dataset platform with multiple sensor modalities, is utilized to validate the effectiveness of the proposed method. The experimental results demonstrate that the proposed method significantly enhances early anomaly detection and the identification of anomalous regions in comparison to existing methods.