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Jaydeep Karandikar
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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
2152016
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
1022015
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
822019
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
802014
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
752012
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
732014
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
432020
Uncertainty in machining: Workshop summary and contributions
TL Schmitz, J Karandikar, N Ho Kim, A Abbas
432011
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
312021
Machine learning classification for tool life modeling using production shop-floor tool wear data
J Karandikar
Procedia Manufacturing 34, 446-454, 2019
302019
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
272012
Tool life predictions using random walk Bayesian updating
JM Karandikar, AE Abbas, TL Schmitz
Machining Science and Technology: An International Journal 17 (3), 2013
222013
Application of Bayesian inference to milling force modeling
JM Karandikar, TL Schmitz, AE Abbas
Journal of Manufacturing Science and Engineering 136 (2), 021017, 2014
192014
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
182022
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
18*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
172014
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
142021
Milling stability identification using Bayesian machine learning
J Karandikar, A Honeycutt, S Smith, T Schmitz
Procedia CIRP 93, 1423-1428, 2020
142020
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
132015
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
132014
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