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Jaydeep M. Karandikar, Ph.D.

Affiliations: 
2013 Mechanical Engineering University of North Carolina, Charlotte, Charlotte, NC, United States 
Area:
Mechanical Engineering, Industrial Engineering
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"Jaydeep Karandikar"
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Tony L. Schmitz grad student 2013 UNC Charlotte
 (The fundamental application of decision analysis to manufacturing.)

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Aggour KS, Gupta VK, Ruscitto D, et al. (2019) Artificial intelligence/machine learning in manufacturing and inspection: A GE perspective Mrs Bulletin. 44: 545-558
Karandikar J. (2019) Machine learning classification for tool life modeling using production shop-floor tool wear data Procedia Manufacturing. 34: 446-454
Sencer B, Schmitz T, Karandikar J, et al. (2017) Guest editor letter Journal of Manufacturing Science and Engineering-Transactions of the Asme. 140: 20301
Drouillet C, Karandikar J, Nath C, et al. (2016) Tool life predictions in milling using spindle power with the neural network technique Journal of Manufacturing Processes. 22: 161-168
Karandikar J, Kurfess T. (2015) Cost optimization and experimental design in milling using surrogate models and value of information Journal of Manufacturing Systems. 37: 479-486
Karandikar J, McLeay T, Turner S, et al. (2015) Tool wear monitoring using naïve Bayes classifiers International Journal of Advanced Manufacturing Technology. 77: 1613-1626
Karandikar J, Traverso M, Abbas A, et al. (2014) Bayesian inference for milling stability using a random walk approach Journal of Manufacturing Science and Engineering, Transactions of the Asme. 136
Karandikar JM, Schmitz TL, Abbas AE. (2014) Application of bayesian inference to milling force modeling Journal of Manufacturing Science and Engineering, Transactions of the Asme. 136
Karandikar JM, Tyler CT, Abbas A, et al. (2014) Value of information-based experimental design: Application to process damping in milling Precision Engineering. 38: 799-808
Karandikar JM, Abbas AE, Schmitz TL. (2014) Tool life prediction using Bayesian updating. Part 2: Turning tool life using a Markov Chain Monte Carlo approach Precision Engineering. 38: 9-17
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