Detecting the extent of co-existing anomalies in additively manufactured metal matrix composites through explainable selection and fusion of multi-camera deep learning features

Process development for customised additively manufactured materials is challenging and labour-intensive. Advanced in-situ monitoring coupled with modern machine learning (ML) methods can expedite defect detection and qualification of additive manufacturing (AM) parts. Directed energy deposition (DED) processes offer flexibility to deposit material on existing complex parts for hybrid manufacturing and repairs. DED enables custom metal matrix composites (MMCs) like nickel tungsten carbide (Ni-WC) overlays on ferrous mining tools for enhanced wear resistance. However, co-existing anomalies specific to defects in the matrix, reinforcement and their interaction present development challenges. The challenge is compounded since the co-existing anomalies can exist in varying extents (e.g. absent, low, high). This study investigates dual mid-wave infrared (MWIR) cameras (FLIR and CLAMIR) for defect extent detection in Ni-WC MMCs. Deep learning features extracted with a fine-tuned vision transformer outperformed conventional methods by improving anomaly separability and revealing process-regime-aware feature distributions. Explainable artificial intelligence identified key MWIR features detecting six defect categories. Data ablation revealed FLIR’s superior accuracy and generalisability under noise, while CLAMIR demonstrated robustness under instability. Explainable fusion enabled effective selection of camera features. Our work provides a foundation for ML-assisted development of AM-based Ni-WC and similar MMCs by facilitating in-situ detection of co-existing anomalies.ed with modern machine learning (ML) methods canexpedite defect detection and qualification of additive manufacturing (AM) parts. Directed energydeposition (DED) processes offer flexibility to deposit material on existing complex parts for hybridmanufacturing and repairs. DED enables custom metal matrix composites (MMCs) like nickeltungsten carbide (Ni-WC) overlays on ferrous mining tools for enhanced wear resistance.However, co-existing anomalies specific to defects in the matrix, reinforcement and theirinteraction present development challenges. The challenge is compounded since theco-existing anomalies can exist in varying extents (e.g. absent, low, high). This study investigatesdual mid-wave infrared (MWIR) cameras (FLIR and CLAMIR) for defect extent detection in Ni-WCMMCs. Deep learning features extracted with a fine-tuned vision transformer outperformedconventional methods by improving anomaly separability and revealing process-regime-awarefeature distributions. Explainable artificial intelligence identified key MWIR features detecting sixdefect categories. Data ablation revealed FLIR’s superior accuracy and generalisability undernoise, while CLAMIR demonstrated robustness under instability. Explainable fusion enabledeffective selection of camera features. Our work provides a foundation for ML-assisteddevelopment of AM-based Ni-WC and similar MMCs by facilitating in-situ detection of co-existing anomalies.