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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
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
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
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. (2023). 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. https://doi.org/10.1007/s10845-023-02279-x
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
Monitoring of direct energy deposition process using manifold learning and co-axial melt pool imaging
Pandiyan, V., Cui, D., Parrilli, A., Deshpande, P., Masinelli, G., Shevchik, S., & Wasmer, K. (2022). Monitoring of direct energy deposition process using manifold learning and co-axial melt pool imaging. Manufacturing Letters, 33(Suppl.), 776-785. https://doi.org/10.1016/j.mfglet.2022.07.096
A cnn prediction method for belt grinding tool wear in a polishing process utilizing 3-axes force and vibration data
Caesarendra, W., Triwiyanto, T., Pandiyan, V., Glowacz, A., Permana, S. D. H., & Tjahjowidodo, T. (2021). A cnn prediction method for belt grinding tool wear in a polishing process utilizing 3-axes force and vibration data. Electronics, 10(12), 1429 (30 pp.). https://doi.org/10.3390/electronics10121429
Acoustic emission and machine learning based classification of wear generated using a pin-on-disc tribometer equipped with a digital holographic microscope
Deshpande, P., Pandiyan, V., Meylan, B., & Wasmer, K. (2021). Acoustic emission and machine learning based classification of wear generated using a pin-on-disc tribometer equipped with a digital holographic microscope. Wear, 476, 203622 (12 pp.). https://doi.org/10.1016/j.wear.2021.203622
Artificial intelligence for monitoring and control of metal additive manufacturing
Masinelli, G., Shevchik, S. A., Pandiyan, V., Quang-Le, T., & Wasmer, K. (2021). Artificial intelligence for monitoring and control of metal additive manufacturing. In M. Meboldt & C. Klahn (Eds.), Industrializing additive manufacturing. Proceedings of AMPA2020 (pp. 205-220). https://doi.org/10.1007/978-3-030-54334-1_15
Semi-supervised monitoring of laser powder bed fusion process based on acoustic emissions
Pandiyan, V., Drissi-Daoudi, R., Shevchik, S., Masinelli, G., Le-Quang, T., Logé, R., & Wasmer, K. (2021). Semi-supervised monitoring of laser powder bed fusion process based on acoustic emissions. Virtual and Physical Prototyping, 16(4), 481-497. https://doi.org/10.1080/17452759.2021.1966166