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Singh, Manjesh K.
- Learning Machining Stability Using a Bayesian Model
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Authors
Affiliations
1 Indian Institute of Technology Kanpur, Kanpur, IN
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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- Sahu, G. N., & Law, M., (2022). Hardware-in- the-loop simulator for emulation and active control of chatter. HardwareX. https://doi. org/10.1016/j.ohx.2022.e00273
- Sahu, G. N., Vashisht, S., Wahi, P., & Law, M. (2020). Validation of a hardware-in-the- loop simulator for investigating and actively damping regenerative chatter in orthogonal cutting. CIRP Journal of Manufacturing Science and Technology, 29, 115-129. https://doi. org/10.1016/j.cirpj.2020.03.002
- Schmitz T., Cornelius A., Karandikar J., Tyler C. & Smith S. (2022). Receptance coupling substructure analysis and chatter frequency- informed machine learning for milling stability. CIRP Annals, 71(1), 321-324. https://doi. org/10.1016/j.cirp.2022.03.020
- Learning Machining Stability Diagrams From Data Using Neural Networks
Abstract Views :118 |
PDF Views:0
Authors
Affiliations
1 Indian Institute of Technology Kanpur, Kanpur, IN
1 Indian Institute of Technology Kanpur, Kanpur, IN
Source
Manufacturing Technology Today, Vol 22, No 2 (2023), Pagination: 29 - 41Abstract
Machining instabilities are detrimental. model predicted stability charts help identify cutting parameters for stability. Since models disregard speed-varying cutting force characteristics and dynamics, charts fail to guide stable cutting in industrial praxis. This study shows how supervised neural networks can learn stability charts from data. The learning capacity of this machine learning model depends on the size of the training dataset, its train-test split, the learning rate, the activation function, the number of hidden layers, and the number of neurons in each layer. This is the first study to examine how hyperparameters influence learning machining stability diagrams. Learnings from a linear stability dataset are transferrable to nonlinear datasets, demonstrating the prediction model is physics-agnostic. Predictions accuracies of up to 97.2% were obtained. Since the data used to train the model includes all the vagaries and uncertainties of the cutting process, the results can inform self-optimizing and autonomous machining systems.
Keywords
Machining, Stability, Chatter, Machine Learning, Neural Network, HyperparametersReferences
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