| In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm V Pandiyan, W Caesarendra, T Tjahjowidodo, HH Tan Journal of Manufacturing Processes, 199-213, 2017 | 164 | 2017 |
| Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques: A review V Pandiyan, S Shevchik, K Wasmer, S Castagne, T Tjahjowidodo Journal of Manufacturing Processes 57, 114-135, 2020 | 88 | 2020 |
| Predictive modelling and analysis of process parameters on material removal characteristics in abrasive belt grinding process V Pandiyan, W Caesarendra, T Tjahjowidodo, G Praveen Applied Sciences 7 (4), 363, 2017 | 77 | 2017 |
| In-Process Virtual Verification of Weld Seam Removal in Robotic Abrasive Belt Grinding Process Using Deep Learning V Pandiyan, P Murugan, T Tjahjowidodo, W Caesarendra, ... 10.17632/2pcnt8kpw9.1, 2019 | 76 | 2019 |
| Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process V Pandiyan, R Drissi-Daoudi, S Shevchik, G Masinelli, T Le-Quang, ... Journal of Materials Processing Technology 303, 117531, 2022 | 67 | 2022 |
| Semi-supervised Monitoring of Laser powder bed fusion process based on acoustic emissions V Pandiyan, R Drissi-Daoudi, S Shevchik, G Masinelli, T Le-Quang, ... Virtual and Physical Prototyping 16 (4), 481-497, 2021 | 57 | 2021 |
| Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning R Drissi-Daoudi, V Pandiyan, R Logé, S Shevchik, G Masinelli, ... Virtual and Physical Prototyping 17 (2), 181-204, 2022 | 55 | 2022 |
| Use of Acoustic Emissions to detect change in contact mechanisms caused by tool wear in abrasive belt grinding process V Pandiyan, T Tjahjowidodo Wear 436, 203047, 2019 | 55 | 2019 |
| Analysis of time, frequency and time-frequency domain features from acoustic emissions during Laser Powder-Bed fusion process V Pandiyan, R Drissi-Daoudi, S Shevchik, G Masinelli, R Logé, K Wasmer Procedia CIRP 94, 392-397, 2020 | 51 | 2020 |
| Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography guidance V Pandiyan, G Masinelli, N Claire, T Le-Quang, M Hamidi-Nasab, ... Additive Manufacturing 58, 103007, 2022 | 38 | 2022 |
| Acoustic emission and machine learning based classification of wear generated using a pin-on-disc tribometer equipped with a digital holographic microscope P Deshpande, V Pandiyan, B Meylan, K Wasmer Wear 476, 203622, 2021 | 37 | 2021 |
| Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm V Pandiyan, J Prost, G Vorlaufer, M Varga, K Wasmer Friction 10 (4), 583-596, 2022 | 34 | 2022 |
| Artificial intelligence for monitoring and control of metal additive manufacturing G Masinelli, SA Shevchik, V Pandiyan, T Quang-Le, K Wasmer Industrializing Additive Manufacturing: Proceedings of AMPA2020, 205-220, 2021 | 28 | 2021 |
| Modelling of material removal in abrasive belt grinding process: A regression approach V Pandiyan, W Caesarendra, A Glowacz, T Tjahjowidodo Symmetry 12 (1), 99, 2020 | 28 | 2020 |
| High frequency and amplitude effects in vibratory media finishing V Pandiyan, S Castagne, S Subbiah Procedia Manufacturing 5, 546-557, 2016 | 24 | 2016 |
| In-process endpoint detection of weld seam removal in robotic abrasive belt grinding process V Pandiyan, T Tjahjowidodo The International Journal of Advanced Manufacturing Technology 93, 1699-1714, 2017 | 23 | 2017 |
| In-process surface roughness estimation model for compliant abrasive belt machining process V Pandiyan, T Tjahjowidodo, MP Samy Procedia Cirp 46, 254-257, 2016 | 23 | 2016 |
| A CNN prediction method for belt grinding tool wear in a polishing process utilizing 3-axes force and vibration data W Caesarendra, T Triwiyanto, V Pandiyan, A Glowacz, SDH Permana, ... Electronics 10 (12), 1429, 2021 | 16 | 2021 |
| Long short-term memory based semi-supervised encoder—decoder for early prediction of failures in self-lubricating bearings V Pandiyan, M Akeddar, J Prost, G Vorlaufer, M Varga, K Wasmer Friction 11 (1), 109-124, 2023 | 15 | 2023 |
| In situ quality monitoring in direct energy deposition process using co-axial process zone imaging and deep contrastive learning V Pandiyan, D Cui, T Le-Quang, P Deshpande, K Wasmer, S Shevchik Journal of Manufacturing Processes 81, 1064-1075, 2022 | 13 | 2022 |
Tegoeh TjahjowidodoKatholieke Universiteit LeuvenVerified email at kuleuven.be
