| Tool-based micro machining and applications in MEMS FZ Fang, K Liu, TR Kurfess, GC Lim MEMS/NEMS: Handbook Techniques and Applications, 678-740, 2006 | 516 | 2006 |
| Robotics and automation handbook TR Kurfess CRC press, 2005 | 513 | 2005 |
| A brief discussion on the trends of habilitating technologies for Industry 4.0 and Smart manufacturing H Ahuett-Garza, T Kurfess Manufacturing letters 15, 60-63, 2018 | 374 | 2018 |
| Bearing condition diagnostics via vibration and acoustic emission measurements J Shiroishi, Y Li, S Liang, T Kurfess, S Danyluk Mechanical systems and signal processing 11 (5), 693-705, 1997 | 362 | 1997 |
| Rolling element bearing diagnostics in run-to-failure lifetime testing T Williams, X Ribadeneira, S Billington, T Kurfess Mechanical systems and signal processing 15 (5), 979-993, 2001 | 347 | 2001 |
| Adaptive prognostics for rolling element bearing condition Y Li, S Billington, C Zhang, T Kurfess, S Danyluk, S Liang Mechanical systems and signal processing 13 (1), 103-113, 1999 | 338 | 1999 |
| A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing D Wu, S Liu, L Zhang, J Terpenny, RX Gao, T Kurfess, JA Guzzo Journal of Manufacturing Systems 43, 25-34, 2017 | 303 | 2017 |
| Part data integration in the Shop Floor Digital Twin: Mobile and cloud technologies to enable a manufacturing execution system PDU Coronado, R Lynn, W Louhichi, M Parto, E Wescoat, T Kurfess Journal of manufacturing systems 48, 25-33, 2018 | 280 | 2018 |
| Stochastic prognostics for rolling element bearings Y Li, TR Kurfess, SY Liang Mechanical Systems and Signal Processing 14 (5), 747-762, 2000 | 277 | 2000 |
| Tool life predictions in milling using spindle power with the neural network technique C Drouillet, J Karandikar, C Nath, AC Journeaux, M El Mansori, T Kurfess Journal of Manufacturing Processes 22, 161-168, 2016 | 207 | 2016 |
| Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images M Aminzadeh, TR Kurfess Journal of Intelligent Manufacturing 30, 2505-2523, 2019 | 166 | 2019 |
| Dynamic prognostic prediction of defect propagation on rolling element bearings Y Li, S Billington, C Zhang, T Kurfess, S Danyluk, S Liang Tribology transactions 42 (2), 385-392, 1999 | 130 | 1999 |
| Introduction to the design rules for metal big area additive manufacturing C Greer, A Nycz, M Noakes, B Richardson, B Post, T Kurfess, L Love Additive manufacturing 27, 159-166, 2019 | 125 | 2019 |
| Quality and inspection of machining operations: tool condition monitoring JT Roth, D Djurdjanovic, X Yang, L Mears, T Kurfess | 109 | 2010 |
| Study of spindle power data with neural network for predicting real-time tool wear/breakage during inconel drilling R Corne, C Nath, M El Mansori, T Kurfess Journal of Manufacturing Systems 43, 287-295, 2017 | 103 | 2017 |
| Grain refinement and mechanical properties of CP-Ti processed by warm accumulative roll bonding JL Milner, F Abu-Farha, C Bunget, T Kurfess, VH Hammond Materials Science and Engineering: A 561, 109-117, 2013 | 98 | 2013 |
| Sampling uncertainty in coordinate measurement data analysis W Choi, TR Kurfess, J Cagan Precision Engineering 22 (3), 153-163, 1998 | 97 | 1998 |
| Development and control of an automated robotic weld bead grinding system DE Whitney, AC Edsall, AB Todtenkopf, TR Kurfess, AR Tate | 94 | 1990 |
| Rethinking additive manufacturing and intellectual property protection T Kurfess, WJ Cass Research-Technology Management 57 (5), 35-42, 2014 | 86 | 2014 |
| Verification of a dynamic grinding model TR Kurfess, DE Whitney, ML Brown | 86 | 1988 |
