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Rajput, Harsh Singh
- Learning Machining Stability Using a Bayesian Model
Authors
1 Indian Institute of Technology Kanpur, Kanpur, IN
Source
Manufacturing Technology Today, Vol 22, No 2 (2023), Pagination: 10 - 16Abstract
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
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- Recovering Cutting Tool Modal Parameters From Randomly Sampled Signals Using Compressed Sensing
Authors
1 Indian Institute of Technology Kanpur, Kanpur, IN
Source
Manufacturing Technology Today, Vol 22, No 2 (2023), Pagination: 17 - 22Abstract
A change in the modal parameters of cutting tools could signal tool wear, tool breakage, or other instabilities. The cutting process must be continuously monitored using vibration signals to detect such changes. Since tools vibrate with frequencies of up to a few kHz, continuous monitoring requires sampling at rates of tens of kHz to respect the Nyquist limit. Processing and storing such large data for decision making is cumbersome. To address this issue, this paper discusses the use of a compressed sensing framework that enables non-uniform random sampling at rates below the Nyquist limit. For cutting tools, we show for the first time using synthesized data that it is possible to reconstruct original signals from as few as 1% of the original data. We numerically test the method to characterize the influence of damping, noise, and multiple modes. Recovered modal parameters from the reconstructed signal agree with signals sampled properly.
Keywords
Compressed Sensing, Modal Parameters, Nyquist Theorem, Sparse Signal, Cutting ToolsReferences
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