Automated surface defect detection framework using machine vision and convolutional neural networks SA Singh, KA Desai Journal of Intelligent Manufacturing 34 (4), 1995-2011, 2023 | 102 | 2023 |
Error compensation in flexible end milling of tubular geometries TC Bera, KA Desai, PVM Rao Journal of Materials Processing Technology 211 (1), 24-34, 2011 | 66 | 2011 |
Process geometry modeling with cutter runout for milling of curved surfaces KA Desai, PK Agarwal, PVM Rao International Journal of Machine Tools and Manufacture 49 (12-13), 1015-1028, 2009 | 65 | 2009 |
On cutter deflection surface errors in peripheral milling KA Desai, PVM Rao Journal of Materials Processing Technology 212 (11), 2443-2454, 2012 | 54 | 2012 |
Machine learning-based instantaneous cutting force model for end milling operation S Vaishnav, A Agarwal, KA Desai Journal of Intelligent Manufacturing 31, 1353-1366, 2020 | 50 | 2020 |
Effect of direction of parameterization on cutting forces and surface error in machining curved geometries KA Desai, PVM Rao International Journal of Machine Tools and Manufacture 48 (2), 249-259, 2008 | 33 | 2008 |
Comparative assessment of common pre-trained CNNs for vision-based surface defect detection of machined components SA Singh, AS Kumar, KA Desai Expert Systems with Applications 218, 119623, 2023 | 32 | 2023 |
On milling of thin-walled tubular geometries TC Bera, KA Desai, PVM Rao Proceedings of the Institution of Mechanical Engineers, Part B: Journal of …, 2010 | 25 | 2010 |
Modeling of cutting force, tool deflection, and surface error in micro-milling operation TM Moges, KA Desai, PVM Rao The International Journal of Advanced Manufacturing Technology 98, 2865-2881, 2018 | 23 | 2018 |
Importance of bottom and flank edges in force models for flat-end milling operation A Agarwal, KA Desai The International Journal of Advanced Manufacturing Technology 107, 1437-1449, 2020 | 20 | 2020 |
Build Orientation Optimization for Strength Enhancement of 3-D Printed Parts Using Machine Learning based Algorithm KAD Malviya, Manoj engrXiv, 2019 | 16* | 2019 |
Predictive framework for cutting force induced cylindricity error estimation in end milling of thin-walled components A Agarwal, KA Desai Precision Engineering 66, 209-219, 2020 | 14 | 2020 |
Improved process geometry model with cutter runout and elastic recovery in micro-end milling TM Moges, KA Desai, PVM Rao Procedia Manufacturing 5, 478-494, 2016 | 14 | 2016 |
On modeling of cutting forces in micro-end milling operation TM Moges, KA Desai, PVM Rao Machining Science and Technology 21 (4), 562-581, 2017 | 13 | 2017 |
Tool and workpiece deflection induced flatness errors in milling of thin-walled components A Agarwal, KA Desai Procedia CIRP 93, 1411-1416, 2020 | 12 | 2020 |
NSLNet: An improved deep learning model for steel surface defect classification utilizing small training datasets V Nath, C Chattopadhyay, KA Desai Manufacturing Letters 35, 39-42, 2023 | 10 | 2023 |
Modeling of flatness errors in end milling of thin-walled components A Agarwal, KA Desai Proceedings of the Institution of Mechanical Engineers, Part B: Journal of …, 2021 | 10 | 2021 |
Amalgamation of physics-based cutting force model and machine learning approach for end milling operation A Agarwal, KA Desai Procedia CIRP 93, 1405-1410, 2020 | 10 | 2020 |
Machining of curved geometries with constant engagement tool paths KA Desai, PVM Rao Proceedings of the Institution of Mechanical Engineers, Part B: Journal of …, 2016 | 10 | 2016 |
In-process dimension monitoring system for integration of legacy machine tools into the industry 4.0 framework S Dayam, KA Desai, M Kuttolamadom Smart and Sustainable Manufacturing Systems 5 (1), 242-263, 2021 | 7 | 2021 |