| 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 | 209 | 2016 |
| Tool wear monitoring using naive Bayes classifiers J Karandikar, T McLeay, S Turner, T Schmitz The International Journal of Advanced Manufacturing Technology 77, 1613-1626, 2015 | 101 | 2015 |
| Tool life prediction using Bayesian updating. Part 2: Turning tool life using a Markov Chain Monte Carlo approach JM Karandikar, AE Abbas, TL Schmitz Precision Engineering 38 (1), 18-27, 2014 | 78 | 2014 |
| Prediction of remaining useful life for fatigue-damaged structures using Bayesian inference JM Karandikar, NH Kim, TL Schmitz Engineering Fracture Mechanics 96, 588-605, 2012 | 78 | 2012 |
| Artificial intelligence/machine learning in manufacturing and inspection: A GE perspective KS Aggour, VK Gupta, D Ruscitto, L Ajdelsztajn, X Bian, KH Brosnan, ... MRS Bulletin 44 (7), 545-558, 2019 | 75 | 2019 |
| Tool life prediction using Bayesian updating. Part 1: Milling tool life model using a discrete grid method JM Karandikar, AE Abbas, TL Schmitz Precision Engineering 38 (1), 9-17, 2014 | 72 | 2014 |
| Uncertainty in machining: Workshop summary and contributions TL Schmitz, J Karandikar, N Ho Kim, A Abbas | 43 | 2011 |
| Stability boundary and optimal operating parameter identification in milling using Bayesian learning J Karandikar, A Honeycutt, T Schmitz, S Smith Journal of Manufacturing Processes 56, 1252-1262, 2020 | 40 | 2020 |
| Machine learning classification for tool life modeling using production shop-floor tool wear data J Karandikar Procedia Manufacturing 34, 446-454, 2019 | 29 | 2019 |
| Physics-guided logistic classification for tool life modeling and process parameter optimization in machining J Karandikar, T Schmitz, S Smith Journal of Manufacturing Systems 59, 522-534, 2021 | 28 | 2021 |
| Spindle speed selection for tool life testing using Bayesian inference JM Karandikar, TL Schmitz, AE Abbas Journal of manufacturing systems 31 (4), 403-411, 2012 | 27 | 2012 |
| Tool life predictions using random walk Bayesian updating JM Karandikar, AE Abbas, TL Schmitz Machining Science and Technology: An International Journal 17 (3), 2013 | 22 | 2013 |
| Application of Bayesian inference to milling force modeling JM Karandikar, TL Schmitz, AE Abbas Journal of Manufacturing Science and Engineering 136 (2), 021017, 2014 | 19 | 2014 |
| Remaining useful life predictions in turning using Bayesian inference JM Karandikar, AE Abbas, TL Schmitz International Journal of Prognostics and Health Management (IJPHM) 4 (2), 25 …, 2013 | 19* | 2013 |
| Bayesian inference for milling stability using a random walk approach J Karandikar, M Traverso, A Abbas, T Schmitz Journal of Manufacturing Science and Engineering 136 (3), 031015, 2014 | 17 | 2014 |
| Receptance coupling substructure analysis and chatter frequency-informed machine learning for milling stability T Schmitz, A Cornelius, J Karandikar, C Tyler, S Smith CIRP Annals 71 (1), 321-324, 2022 | 14 | 2022 |
| Milling stability identification using Bayesian machine learning J Karandikar, A Honeycutt, S Smith, T Schmitz Procedia CIRP 93, 1423-1428, 2020 | 14 | 2020 |
| A Bayesian framework for milling stability prediction and reverse parameter identification A Cornelius, J Karandikar, M Gomez, T Schmitz Procedia Manufacturing 53, 760-772, 2021 | 13 | 2021 |
| Cost optimization and experimental design in milling using surrogate models and value of information J Karandikar, T Kurfess Journal of Manufacturing Systems 37, 479-486, 2015 | 13 | 2015 |
| Value of information-based experimental design: Application to process damping in milling JM Karandikar, CT Tyler, A Abbas, TL Schmitz Precision Engineering 38 (4), 799-808, 2014 | 12 | 2014 |
Tony SchmitzUniversity of Tennessee, KnoxvilleVerified email at utk.edu
