Jaydeep Karandikar

发表

Tool life predictions in milling using spindle power with the neural network technique

T. Kurfess, M. Mansori, Cyril Drouillet, 2016 .

Machine learning classification for tool life modeling using production shop-floor tool wear data

Jaydeep Karandikar, 2019, Procedia Manufacturing.

Artificial intelligence/machine learning in manufacturing and inspection: A GE perspective

Peng Li, Abha Moitra, Xiao Bian, 2019, MRS Bulletin.

Tool life prediction using Bayesian updating. Part 1: Milling tool life model using a discrete grid method

Tony L. Schmitz, Ali E. Abbas, Jaydeep Karandikar, 2014 .

Process window estimation in manufacturing through Entropy-Sigma active learning

K. Willcox, A. Chaudhuri, Jaydeep Karandikar, 2022, Manufacturing Letters.

Uncertainty in Machining: Workshop Summary and

Tony L. Schmitz, Ali E. Abbas, Jaydeep Karandikar, 2011 .

Stability boundary and optimal operating parameter identification in milling using Bayesian learning

T. Schmitz, Jaydeep Karandikar, Andrew Honeycutt, 2020, Journal of Manufacturing Processes.

Milling stability identification using Bayesian machine learning

T. Schmitz, Jaydeep Karandikar, Andrew Honeycutt, 2020 .

Uncertainty in Machining: Workshop Summary and Contributions

Tony L. Schmitz, Ali E. Abbas, Jaydeep Karandikar, 2011 .

Predicting chatter using machine learning and acoustic signals from low-cost microphones

Jaydeep Karandikar, Anahita Khojandi, Matthew J. Alberts, 2023, The International Journal of Advanced Manufacturing Technology.

Uncertainty evaluation for twist drilling stability model

T. Schmitz, S. Ro, Jaydeep Karandikar, 2020 .

Bayesian Inference for Milling Stability Using a Random Walk Approach

Tony L. Schmitz, Ali E. Abbas, Jaydeep Karandikar, 2014 .

Receptance coupling substructure analysis and chatter frequency-informed machine learning for milling stability

Christopher T. Tyler, T. Schmitz, Jaydeep Karandikar, 2022, CIRP Annals.

Tool wear monitoring using naïve Bayes classifiers

T. Schmitz, Jaydeep Karandikar, S. Turner, 2015 .

Propagation of Johnson-Cook flow stress model uncertainty to milling force uncertainty using finite element analysis and time domain simulation

T. Schmitz, J. Heigel, Jaydeep Karandikar, 2021, Procedia Manufacturing.

Physics-guided logistic classification for tool life modeling and process parameter optimization in machining

T. Schmitz, Jaydeep Karandikar, Scott Smith, 2021, Journal of Manufacturing Systems.

STABILITY ANALYSIS WITH UNCERTAINTY FOR TWIST DRILLING

T. Schmitz, S. Ro, Jaydeep Karandikar, 2020 .

Remaining useful tool life predictions in turning using Bayesian inference

Tony L. Schmitz, Ali Abbas, T. Schmitz, 2020 .

Remaining Useful Tool Life Predictions Using Bayesian Inference

Tony L. Schmitz, Sam Turner, Jaydeep Karandikar, 2013 .

LIFE PREDICTIONS USING BAYESIAN INFERENCE

Jaydeep Karandikar, 2013 .

Tool wear monitoring using naïve Bayes classifiers

Sam Turner, Tony Schmitz, Jaydeep Karandikar, 2014, The International Journal of Advanced Manufacturing Technology.

A Sequential Greedy Search Algorithm With Bayesian Updating for Testing in High-Speed Milling Operations

Michael G. Traverso, Raúl E. Zapata, T. Schmitz, 2010 .

Combining Process Dynamics and Tool Wear in the Milling Super Diagram

Tony L. Schmitz, Raul Zapata, Jaydeep Karandikar, 2010 .

Estimating Johnson-Cook Material Parameters using Neural Networks

Anahita Khojandi, Matt Baucum, Jaydeep Karandikar, 2021, Procedia Manufacturing.

Cost optimization and experimental design in milling using surrogate models and value of information

Thomas R. Kurfess, Jaydeep Karandikar, T. Kurfess, 2015 .

TOOL LIFE PREDICTION USING RANDOM WALK BAYESIAN UPDATING

Tony L. Schmitz, Ali E. Abbas, Jaydeep Karandikar, 2013 .

Spindle speed selection for tool life testing using Bayesian inference

Tony L. Schmitz, Ali E. Abbas, Jaydeep Karandikar, 2012 .

Logistic classification for tool life modeling in machining

Tony Schmitz, Jaydeep Karandikar, Scott Smith, 2021, Procedia CIRP.

Tool life prediction using Bayesian updating. Part 2: Turning tool life using a Markov Chain Monte Carlo approach

Tony L. Schmitz, Ali E. Abbas, Jaydeep Karandikar, 2014 .

Evaluation of automated stability testing in machining through closed-loop control and Bayesian machine learning

T. Kurfess, T. Schmitz, Jaydeep Karandikar, 2022, Mechanical systems and signal processing.

Application of Bayesian Inference to Milling Force Modeling paper describes the application of Bayesian inference to the identification of force

Tony L. Schmitz, Ali E. Abbas, Jaydeep Karandikar, 2014 .

Physics-informed Bayesian machine learning case study: Integral blade rotors

Jaydeep Karandikar, Gregory Corson, Tony Schmitz, 2023, Journal of Manufacturing Processes.

Integration of discrete-event dynamics and machining dynamics for machine tool: Modeling, analysis and algorithms

Christopher T. Tyler, Jaydeep Karandikar, Michael Gomez, 2022, Manufacturing Letters.

Value of information-based experimental design: Application to process damping in milling

Tony L. Schmitz, Ali E. Abbas, Christopher T. Tyler, 2014 .

Process Damping Coefficient Identification using Bayesian Inference

Christopher T. Tyler, T. Schmitz, Jaydeep Karandikar, 2013 .

A Bayesian Framework for Milling Stability Prediction and Reverse Parameter Identification

Jaydeep Karandikar, Michael Gomez, Aaron Cornelius, 2021, Procedia Manufacturing.

Prediction of remaining useful life for fatigue-damaged structures using Bayesian inference

Tony L. Schmitz, Jaydeep Karandikar, Nam H. Kim, 2012 .

Cutting force estimation from machine learning and physics-inspired data-driven models utilizing accelerometer measurements

Jaydeep Karandikar, Tony Schmitz, Reese Eischens, 2024, Procedia CIRP.