| Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines R Jegadeeshwaran, V Sugumaran Mechanical Systems and Signal Processing 52, 436 - 446, 2015 | 252 | 2015 |
| A machine learning approach for vibration-based multipoint tool insert health prediction on vertical machining centre (VMC) AD Patange, R Jegadeeshwaran Measurement 173, 108649, 2021 | 99 | 2021 |
| Comparative study of decision tree classifier and best first tree classifier for fault diagnosis of automobile hydraulic brake system using statistical features R Jegadeeshwaran, V Sugumaran Measurement 46 (9), 3247-3260, 2013 | 83 | 2013 |
| Determination of minimum sample size for fault diagnosis of automobile hydraulic brake system using power analysis V Indira, R Vasanthakumari, R Jegadeeshwaran, V Sugumaran Engineering Science and Technology, an International Journal 18 (1), 59 - 69, 2015 | 82* | 2015 |
| Brake fault diagnosis using Clonal Selection Classification Algorithm (CSCA) - A statistical learning approach R. Jegadeeshwaran, V Sugumaran. Engineering Science and Technology, an International Journal 18 (1), 14 - 23, 2015 | 60 | 2015 |
| Vibration based brake health monitoring using wavelet features: A machine learning approach TM Alamelu Manghai, R Jegadeeshwaran Journal of vibration and control 25 (18), 2534-2550, 2019 | 54 | 2019 |
| A Bayesian optimized discriminant analysis model for condition monitoring of face milling cutter using vibration datasets NS Bajaj, AD Patange, R Jegadeeshwaran, KA Kulkarni, RS Ghatpande, ... Journal of Nondestructive Evaluation, Diagnostics and Prognostics of …, 2022 | 52 | 2022 |
| Application of bayesian family classifiers for cutting tool inserts health monitoring on CNC milling AD Patange, R Jegadeeshwaran International Journal of Prognostics and Health Management 11 (2), 1-13, 2020 | 50 | 2020 |
| Milling cutter condition monitoring using machine learning approach AD Patange, R Jegadeeshwaran, NC Dhobale IOP Conference Series: Materials Science and Engineering 624 (1), 012030, 2019 | 49 | 2019 |
| Application of metaheuristic optimization based support vector machine for milling cutter health monitoring NS Bajaj, AD Patange, R Jegadeeshwaran, SS Pardeshi, KA Kulkarni, ... Intelligent Systems with Applications 18, 200196, 2023 | 47 | 2023 |
| Application of Machine Learning for Tool Condition Monitoring in Turning AD Patange, R Jegadeeshwaran, NS Bajaj, AN Khairnar, NA Gavade Sound and Vibration 55 (2), 87-116, 2022 | 40 | 2022 |
| Condition monitoring of FSW tool using vibration analysis–A machine learning approach K Balachandar, R Jegadeeshwaran, D Gandhikumar Materials Today: Proceedings 27, 2970-2974, 2020 | 37 | 2020 |
| Augmentation of decision tree model through hyper-parameters tuning for monitoring of cutting tool faults based on vibration signatures AD Patange, SS Pardeshi, R Jegadeeshwaran, A Zarkar, K Verma Journal of Vibration Engineering & Technologies 11 (8), 3759-3777, 2023 | 36 | 2023 |
| Friction stir welding tool condition monitoring using vibration signals and Random forest algorithm–A Machine learning approach K Balachandar, R Jegadeeshwaran Materials Today: Proceedings 46 (2), 1174-1180, 2021 | 35 | 2021 |
| Review on tool condition classification in milling: A machine learning approach AD Patange, R Jegadeeshwaran Materials Today: Proceedings 46 (2), 1106-1115, 2021 | 34 | 2021 |
| Deep learning algorithms for tool condition monitoring in milling: A review SS Patil, SS Pardeshi, AD Patange, R Jegadeeshwaran Journal of Physics: Conference Series 1969 (1), 012039, 2021 | 33 | 2021 |
| Improving program outcome attainments using project based learning approach for: UG course-mechatronics AD Patange, AK Bewoor, SP Deshmukh, SS Mulik, SS Pardeshi, ... Journal of Engineering Education Transformations 33 (1), 1-8, 2019 | 30 | 2019 |
| Digital twin-driven tool condition monitoring for the milling process S Natarajan, M Thangamuthu, S Gnanasekaran, J Rakkiyannan Sensors 23 (12), 5431, 2023 | 29 | 2023 |
| A white-box SVM framework and its swarm-based optimization for supervision of toothed milling cutter through characterization of spindle vibrations TY Deo, AD Patange, SS Pardeshi, R Jegadeeshwaran, AN Khairnar, ... arXiv preprint arXiv:2112.08421, 2021 | 28 | 2021 |
| Design of bagged tree ensemble for carbide coated inserts fault diagnosis HS Khade, AD Patange, SS Pardeshi, R Jegadeeshwaran Materials Today: Proceedings 46 (2), 1283-1289, 2021 | 26 | 2021 |
Sakthivel GnanasekaranSchool of Mechanical Engineering, VIT ChennaiПодтвержден адрес электронной почты в домене vit.ac.in