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Learning Machining Stability Using a Bayesian Model


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1 Indian Institute of Technology Kanpur, Kanpur, India
     

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Instabilities in machining can be detrimental. Usually, analytical model-predicted stability charts guide selection of cutting parameters to ensure stable processes. However, since inputs to the model seldom account for the speed-dependent behaviour of the cutting process or the dynamics, models often fail to guide stable cutting parameter selection in real industrial settings. To address this issue, this paper discusses how real experimentally classified stable and unstable cutting data with all its vagaries and uncertainties can instead be used to learn the stability behaviour using a supervised Bayes' learning approach. We expand previously published work to systematically characterize how probability distributions, training data size, and thresholding influence the learning capacity of the Bayesian approach. Prediction accuracies of up to 95% are shown to be possible. We also show how the approach nicely extends itself to a continuous learning process. Results can hence inform further development towards self-optimizing and autonomous machining systems.


Keywords

Machining Stability, Bayesian Learning, Machine Learning
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  • Learning Machining Stability Using a Bayesian Model

Abstract Views: 116  |  PDF Views: 0

Authors

Advait Pujari
Indian Institute of Technology Kanpur, Kanpur, India
Harsh Singh Rajput
Indian Institute of Technology Kanpur, Kanpur, India
Mohit Law
Indian Institute of Technology Kanpur, Kanpur, India
Manjesh K. Singh
Indian Institute of Technology Kanpur, Kanpur, India

Abstract


Instabilities in machining can be detrimental. Usually, analytical model-predicted stability charts guide selection of cutting parameters to ensure stable processes. However, since inputs to the model seldom account for the speed-dependent behaviour of the cutting process or the dynamics, models often fail to guide stable cutting parameter selection in real industrial settings. To address this issue, this paper discusses how real experimentally classified stable and unstable cutting data with all its vagaries and uncertainties can instead be used to learn the stability behaviour using a supervised Bayes' learning approach. We expand previously published work to systematically characterize how probability distributions, training data size, and thresholding influence the learning capacity of the Bayesian approach. Prediction accuracies of up to 95% are shown to be possible. We also show how the approach nicely extends itself to a continuous learning process. Results can hence inform further development towards self-optimizing and autonomous machining systems.


Keywords


Machining Stability, Bayesian Learning, Machine Learning

References