| Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework
Pandiyan, V., Cui, D., Richter, R. A., Parrilli, A., & Leparoux, M. (2025). Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework. Journal of Intelligent Manufacturing, 36, 909-933. https://doi.org/10.1007/s10845-023-02279-x |
| Investigating laser beam shadowing and powder particle dynamics in directed energy deposition through high-fidelity modelling and high-speed imaging
Aggarwal, A., Pandiyan, V., Leinenbach, C., & Leparoux, M. (2024). Investigating laser beam shadowing and powder particle dynamics in directed energy deposition through high-fidelity modelling and high-speed imaging. Additive Manufacturing, 91, 104344 (23 pp.). https://doi.org/10.1016/j.addma.2024.104344 |
| Classification of progressive wear on a multi-directional pin-on-disc tribometer simulating conditions in human joints-UHMWPE against CoCrMo using acoustic emission and machine learning
Deshpande, P., Wasmer, K., Imwinkelried, T., Heuberger, R., Dreyer, M., Weisse, B., … Pandiyan, V. (2024). Classification of progressive wear on a multi-directional pin-on-disc tribometer simulating conditions in human joints-UHMWPE against CoCrMo using acoustic emission and machine learning. Lubricants, 12(2), 47 (23 pp.). https://doi.org/10.3390/lubricants12020047 |
| Acoustic emission signature of martensitic transformation in laser powder bed fusion of Ti6Al4V-Fe, supported by operando X-ray diffraction
Esmaeilzadeh, R., Pandiyan, V., Van Petegem, S., Van der Meer, M., Nasab, M. H., de Formanoir, C., … Logé, R. E. (2024). Acoustic emission signature of martensitic transformation in laser powder bed fusion of Ti6Al4V-Fe, supported by operando X-ray diffraction. Additive Manufacturing, 96, 104562 (18 pp.). https://doi.org/10.1016/j.addma.2024.104562 |
| Monitoring of Laser Powder Bed Fusion process by bridging dissimilar process maps using deep learning-based domain adaptation on acoustic emissions
Pandiyan, V., Wróbel, R., Richter, R. A., Leparoux, M., Leinenbach, C., & Shevchik, S. (2024). Monitoring of Laser Powder Bed Fusion process by bridging dissimilar process maps using deep learning-based domain adaptation on acoustic emissions. Additive Manufacturing, 80, 103974 (13 pp.). https://doi.org/10.1016/j.addma.2024.103974 |
| Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process
Pandiyan, V., Baganis, A., Axel Richter, R., Wróbel, R., & Leinenbach, C. (2024). Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process. Virtual and Physical Prototyping, 19(1), e2356080 (20 pp.). https://doi.org/10.1080/17452759.2024.2356080 |
| Acousto-optic material differentiation during water jet-guided laser cutting by applying a neural network
Richter, R. A., Disalvo, L., Ivas, T., Pandiyan, V., Zryd, A., Hoffmann, P., & Shevchik, S. (2024). Acousto-optic material differentiation during water jet-guided laser cutting by applying a neural network. In 16th Pacific rim conference on lasers and electro-optics, CLEO-PR 2024. Pacific rim conference on lasers and electro-optics. https://doi.org/10.1109/CLEO-PR60912.2024.10676839 |
| Unsupervised quality monitoring of metal additive manufacturing using Bayesian adaptive resonance
Shevchik, S., Wrobel, R., Quang T, L., Pandiyan, V., Hoffmann, P., Leinenbach, C., & Wasmer, K. (2024). Unsupervised quality monitoring of metal additive manufacturing using Bayesian adaptive resonance. Heliyon, 10(12), e32656 (12 pp.). https://doi.org/10.1016/j.heliyon.2024.e32656 |
| Encoder-decoder based convolutional neural network (EDCNN) for video classification of smoke and fire image
Caesarendra, W., Pandiyan, V., Umar, M. M., Pamungkas, D. S., Sulowicz, M., & Yassin, H. (2023). Encoder-decoder based convolutional neural network (EDCNN) for video classification of smoke and fire image. In W. R. Puspita (Ed.), AIP conference proceedings: Vol. 2665. International conference on applied engineering (p. 040012 (9 pp.). https://doi.org/10.1063/5.0127353 |
| Harmonizing sound and light: X-ray imaging unveils acoustic signatures of stochastic inter-regime instabilities during laser melting
Hamidi Nasab, M., Masinelli, G., de Formanoir, C., Schlenger, L., Van Petegem, S., Esmaeilzadeh, R., … Logé, R. E. (2023). Harmonizing sound and light: X-ray imaging unveils acoustic signatures of stochastic inter-regime instabilities during laser melting. Nature Communications, 14(1), 8008 (14 pp.). https://doi.org/10.1038/s41467-023-43371-3 |
| Optimizing in-situ monitoring for laser powder bed fusion process: deciphering acoustic emission and sensor sensitivity with explainable machine learning
Pandiyan, V., Wróbel, R., Leinenbach, C., & Shevchik, S. (2023). Optimizing in-situ monitoring for laser powder bed fusion process: deciphering acoustic emission and sensor sensitivity with explainable machine learning. Journal of Materials Processing Technology, 321, 118144 (17 pp.). https://doi.org/10.1016/j.jmatprotec.2023.118144 |
| Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring
Pandiyan, V., Wróbel, R., Richter, R. A., Leparoux, M., Leinenbach, C., & Shevchik, S. (2023). Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring. Materials and Design, 235, 112458 (15 pp.). https://doi.org/10.1016/j.matdes.2023.112458 |
| Monitoring of functionally graded material during laser directed energy deposition by acoustic emission and optical emission spectroscopy using artificial intelligence
Wasmer, K., Wüst, M., Cui, D., Masinelli, G., Pandiyan, V., & Shevchik, S. (2023). Monitoring of functionally graded material during laser directed energy deposition by acoustic emission and optical emission spectroscopy using artificial intelligence. Virtual and Physical Prototyping, 18(1), e2189599 (21 pp.). https://doi.org/10.1080/17452759.2023.2189599 |
| Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning
Drissi-Daoudi, R., Pandiyan, V., Logé, R., Shevchik, S., Masinelli, G., Ghasemi-Tabasi, H., … Wasmer, K. (2022). Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning. Virtual and Physical Prototyping, 17(2), 181-204. https://doi.org/10.1080/17452759.2022.2028380 |
| Multimodal signal segmentation technique based on morphological operators applied on synchronized optical data for Laser Powder Bed Fusion processes
Masinelli, G., Wrobel, R., Pandiyan, V., & Wasmer, K. (2022). Multimodal signal segmentation technique based on morphological operators applied on synchronized optical data for Laser Powder Bed Fusion processes. In M. Schmidt, F. Vollertsen, & B. M. Colosimo (Eds.), Procedia CIRP: Vol. 111. CIRP conference on photonic technologies (pp. 838-843). https://doi.org/10.1016/j.procir.2022.08.094 |
| Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and <em>operando</em> X-ray radiography guidance
Pandiyan, V., Masinelli, G., Claire, N., Le-Quang, T., Hamidi-Nasab, M., de Formanoir, C., … Wasmer, K. (2022). Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography guidance. Additive Manufacturing, 58, 103007 (15pp.). https://doi.org/10.1016/j.addma.2022.103007 |
| Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process
Pandiyan, V., Drissi-Daoudi, R., Shevchik, S., Masinelli, G., Le-Quang, T., Logé, R., & Wasmer, K. (2022). Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process. Journal of Materials Processing Technology, 303, 117531 (14 pp.). https://doi.org/10.1016/j.jmatprotec.2022.117531 |
| Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm
Pandiyan, V., Prost, J., Vorlaufer, G., Varga, M., & Wasmer, K. (2022). Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm. Friction, 10(4), 583-596. https://doi.org/10.1007/s40544-021-0518-0 |
| In situ quality monitoring in direct energy deposition process using co-axial process zone imaging and deep contrastive learning
Pandiyan, V., Cui, D., Le-Quang, T., Deshpande, P., Wasmer, K., & Shevchik, S. (2022). In situ quality monitoring in direct energy deposition process using co-axial process zone imaging and deep contrastive learning. Journal of Manufacturing Processes, 81, 1064-1075. https://doi.org/10.1016/j.jmapro.2022.07.033 |
| Long short-term memory based semi-supervised encoder—decoder for early prediction of failures in self-lubricating bearings
Pandiyan, V., Akeddar, M., Prost, J., Vorlaufer, G., Varga, M., & Wasmer, K. (2022). Long short-term memory based semi-supervised encoder—decoder for early prediction of failures in self-lubricating bearings. Friction, 11(1), 109-124. https://doi.org/10.1007/s40544-021-0584-3 |
