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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 LearningReferences
- Aggogeri, F., Pellegrini, N., & Tagliani F. L. (2021). Recent advances on machine learning applications in machining processes. Applied sciences, 11(18), 8764. https://doi.org/10.3390/ app11188764
- Chen, G., Li, Y., Liu X., & Yang, B. (2021). Physics- informed bayesian inference for milling stability analysis. International Journal of Machine Tools and Manufacture, 167. https://doi. org/10.1016/j.ijmachtools.2021.103767
- Denkana, B., Bergmann, B., & Reimer, S. (2020). Analysis of different machine learning algorithms to learn stability lobe diagram. Procedia CIRP, 88. https://doi.org/10.1016/j. procir.2020.05.049
- Friedrich, J., Hinze C., Renner A., Verl A., & Lechler A. (2017). Estimation of stability lobe diagrams in milling with continuous learning algorithms. Robotics and Computer- Integrated Manufacturing, 43, 124-134. https://doi.org/10.1016/j.rcim. 2015.10.003
- Friedrich, J., Torzewski, J., & Verl, A. (2018). Online learning of stability lobe diagrams in milling. Procedia CIRP. https://doi.org/10.1016/j. procir.2017.12.213
- Karandikar, J., Honeycutt, A., Schmitz, T., & Smith, S. (2020). Stability boundary and optimal operating parameter identification in milling using Bayesian learning. Journal of Manufacturing Processes 56. https://doi. org/10.1016/j.jmapro.2020.04.019
- 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
- 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
- Candes, E. J., Romberg, J., & Tao T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2), 489-509. https://doi. org/10.1109/TIT.2005.862083
- Candes, E., & Wakin, M. B. (2008). An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2), 21-30. https://doi.org/ 10.1109/MSP.2007.914731
- Chen, S., & Donoho, D. (1994). Basis pursuit. Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers, 1, 41-44. https://doi.org/10.1109/ACSSC.1994.471413
- Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289-1306. https://doi.org/10.1109/TIT.2006. 871582
- Gill, P. R., Wang, A., & Molnar, A. (2011). The in-crowd algorithm for fast basis pursuit denoising. IEEE Transactions on Signal Processing, 59(10), 4595-4605. https://doi. org/10.1109/TSP.2011.2161292
- Grant, M., & Boyd, S. (2013). CVX: MATLAB software for disciplined convex programming, version 2.0 beta. http://cvxr.com/cvx
- Gupta, P., Law, M., & Mukhopadhyay, S. (2020). Evaluating tool point dynamics using output- only modal analysis with mass-change methods. CIRP Journal of Manufacturing Science and Technology, 31, 251-264. https://doi. org/10.1016/j.cirpj.2020.06.001
- Iglesias, A., Tunç, L. T., ozsahin, O., Franco, O., Munoa J., & Budak E. (2022). Alternative experimental methods for machine tool dynamics identification: A review. Mechanical Systems and Signal Processing, 170, 108837. https://doi.org/10.1016/j.ymssp.2022.108837
- Juang, J., & Pappa, R. S. (1985). An eigensystem realization algorithm for modal parameter identification and model reduction. Journal of Guidance Control and Dynamics, 8(5), 620-627. https://doi.org/10.2514/3.20031
- Lambora, R., Nuhman, A. P., Law, M., & Mukhopadyay, S. (2022). Recovering cutting tool modal parameters from fractionally uncorrelated and potentially aliased signals. Annals of the CIRP, 38, 414-426. https://doi.org/10.1016/j. cirpj.2022.05.014
- Law, M., Gupta, P., & Mukhopadhyay, S. (2020). Modal analysis of machine tools using Visual Vibrometry and output-only methods. Annals of the CIRP, 69, 357-360. https://doi.org/10.1016/j. cirp.2020.04.043
- Law, M., Lambora, R., Nuhman, A. P., & Mukhopadhyay, S. (2022). Modal parameter recovery from temporally aliased video recordings of cutting tools. Annals of the CIRP, 71(1), 329-332. https://doi.org/10.1016/j.cirp. 2022.03.023
- Martinez, B., Green, A., Silva, M. F., Yang, Y., & Mascareñas, D. (2020). Sparse and random sampling techniques for high-resolution, full-field, BSS-based structural dynamics identification from video, Sensors. 20(12), 3526. https://doi.org/10.3390/s20123526
- Metropolis, N., & Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association. 44(247), 335-341. https://doi. org/10.2307/2280232
- Yang, Y., & Nagarajaiah, S. (2015). Output-only modal identification by compressed sensing: non-uniform low-rate random sampling. Mechanical Systems and Signal Processing, 56-57, 15-34. https://doi.org/10.1016/j. ymssp.2014.10.015
- Yazicigil, R. T., Haque, T., Kinget, P. R., & Wright, J. (2019). Taking compressive sensing to the hardware level: breaking fundamental radio- frequency hardware performance tradeoffs. IEEE Signal Processing Magazine, 36(2), 81-100. https://doi.org/10.1109/MSP.2018.2880837