Jaydeep Karandikar
Title
Cited by
Cited by
Year
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
972016
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
642014
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
572012
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
542014
Tool wear monitoring using naive Bayes classifiers
J Karandikar, T McLeay, S Turner, T Schmitz
The International Journal of Advanced Manufacturing Technology 77 (9), 1613-1626, 2015
522015
Uncertainty in machining: Workshop summary and contributions
TL Schmitz, J Karandikar, N Ho Kim, A Abbas
Journal of manufacturing science and engineering 133 (5), 2011
312011
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
212012
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
202019
Application of Bayesian inference to milling force modeling
JM Karandikar, TL Schmitz, AE Abbas
Journal of Manufacturing Science and Engineering 136 (2), 2014
172014
Tool life predictions using random walk Bayesian updating
JM Karandikar, AE Abbas, TL Schmitz
Machining Science and Technology: An International Journal 17 (3), 2013
162013
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
15*2013
Machine learning classification for tool life modeling using production shop-floor tool wear data
J Karandikar
Procedia Manufacturing 34, 446-454, 2019
132019
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
112014
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), 2014
92014
Process damping coefficient identification using Bayesian inference
JM Karandikar, CT Tyler, TL Schmitz
Proceedings of NAMRI/SME 41, 2013
72013
Remaining useful tool life predictions using Bayesian inference
J Karandikar, A Abbas, T Schmitz
American Society of Precision Engineering 1, 3503, 2012
72012
Incorporating stability, surface location error, tool wear, and uncertainty in the milling super diagram
J Karandikar, R Zapata, T Schmitz
Trans. NAMRI/SME 38, 229-236, 2010
52010
Tool life prediction using Bayesian updating
Jaydeep Karandikar, Ali Abbas, Tony Schmitz
Transactions of the NAMRI/SME 39, 2010
5*2010
The fundamental application of decision analysis to manufacturing
JM Karandikar
University of North Carolina at Charlotte, Charlotte, NC, 2013
32013
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Articles 1–20