APA (7th ed.) Citation

Safdar, M., Wood, G., Zimmermann, M., Lamouche, G., Wanjara, P., & Zhao, Y. F. (2025). Detecting the extent of co-existing anomalies in additively manufactured metal matrix composites through explainable selection and fusion of multi-camera deep learning features. Virtual & Physical Prototyping, 20(1), 1. https://doi.org/10.1080/17452759.2025.2515240

Chicago Style (17th ed.) Citation

Safdar, Mutahar, Gentry Wood, Max Zimmermann, Guy Lamouche, Priti Wanjara, and Yaoyao Fiona Zhao. "Detecting the Extent of Co-existing Anomalies in Additively Manufactured Metal Matrix Composites Through Explainable Selection and Fusion of Multi-camera Deep Learning Features." Virtual & Physical Prototyping 20, no. 1 (2025): 1. https://doi.org/10.1080/17452759.2025.2515240.

MLA (9th ed.) Citation

Safdar, Mutahar, et al. "Detecting the Extent of Co-existing Anomalies in Additively Manufactured Metal Matrix Composites Through Explainable Selection and Fusion of Multi-camera Deep Learning Features." Virtual & Physical Prototyping, vol. 20, no. 1, 2025, p. 1, https://doi.org/10.1080/17452759.2025.2515240.