Author Details

Circular saws are thin and hence they vibrate during cutting. Vibrations get imprinted on the side walls of the part being cut, with each tooth leaving its own imprint. When there is a phase shift of vibrations between two successive teeth, regenerative instabilities can occur. However, since the flank face of the tooth also rubs the vibration marks on the side wall, there can also be process-induced damping. Such damping is known to improve the stability of low speed cutting processes. Since metal circular sawing is a low-speed process, it is the aim of this paper to characterize the role of process damping, if any, on regenerative instabilities using an analytical model. The saw is modelled as an annular disc constrained by springs representing guides. The Muller method with deflation is used to solve the governing equations of motion. Model-based analysis suggests that process damping indeed plays a stabilizing role.

Keywords

Regenerative Instabilities, Damping, Vibrations, Circular Sawing.

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References

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Vibration Suppression of a Slender Boring Bar by an Impact Damper

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Authors

Arjun Patel ¹, Mohit Law ¹, Pankaj Wahi ¹

Affiliations
1 Indian Institute of Technology Kanpur, Kanpur, India., IN

Source

Manufacturing Technology Today, Vol 22, No 4 (2023), Pagination: 14-19

Abstract

This paper presents an analytical model to characterize the role of an impact damper in suppressing vibrations of slender boring bars that are used in deep hole boring processes. The boring bar is modelled as a Euler-Bernoulli beam that interacts with a ring impact damper through a spring and damper combine. Parametric analysis suggests that smaller gaps in between the bar and the damper result in higher reduction in the vibration response. Analysis also suggests that for maximum vibration suppressions the damper’s stiffness should be less than that of the bar, and its damping should be greater. Though the vibration suppression capacity of this impact damped boring bar is found slightly wanting when compared to a boring bar damped with an optimally tuned mass damper, model-based analysis as is presented herein is new and generalized and can hence guide further design and development of optimal impact damped boring bars.

Keywords

Impact Damper, Boring Bar, Vibration Suppression, Chatter.

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References

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Patel, A., Talaviya, D., Law, M., & Wahi, P. (2022). Optimally tuning an absorber for a chatter-resistant rotating slender milling tool holder. Journal of Sound and Vibration, 520, 116594. 10.1016/j.jsv.2021.116594.

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Thekkepat, A. A., Devadula, S., & Law, M. (2021). Identifying joint dynamics in bolted cantilevered Systems Under Varying Tightening Torques and Torsional Excitations. Journal of Vibration Engineering & Technologies.

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Obtaining subpixel level cutting tool displacements from video using a CNN architecture

Abstract Views :58 | PDF Views:0

Authors

Varun Raizada ¹, Mohit Law ¹

Affiliations
1 Indian Institute of Technology Kanpur, Kanpur, India, IN

Source

Manufacturing Technology Today, Vol 22, No 3 (2023), Pagination: 14 - 19

Abstract

To register motion from video of vibrating tools, acquisition must ensure that motion is spatially and temporally resolved. However, since tools often vibrate with subpixel level motion, and since cameras often trade speed for resolution, if acquisition is to respect the Nyquist limit to avoid temporal aliasing, then the spatial resolution is often not sufficient to detect small cutting tool motion. To address this problem, this paper shows for the first time that subpixel level tool motion can be inferred instead by using convolution neural networks. We train our model on a database using the phase-based optical flow scheme that is a subpixel level motion registration algorithm. Our model is shown to be capable of detecting small motion correctly. Though the frequency of vibration estimated from the registered motion is correct, further work is necessary on fine tuning model architecture to fix the errors observed in the estimation of damping.

Keywords

Convolution Neural Network, Vibrations, Phase-Based Optical Flow, Visual Vibrometry.

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References

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Law, M., Lambora, R., & Mukhopadhyay, S. (2022). Modal parameter recovery from temporally aliased video recordings of cutting tools. CIRP Annals. https://doi.org/10.1016/j. cirp.2022.03.023

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Learning Machining Stability Using a Bayesian Model

Abstract Views :113 | PDF Views:0

Authors

Advait Pujari ¹, Harsh Singh Rajput ¹, Mohit Law ¹, Manjesh K. Singh ¹

Affiliations
1 Indian Institute of Technology Kanpur, Kanpur, IN

Source

Manufacturing Technology Today, Vol 22, No 2 (2023), Pagination: 10 - 16

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

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References

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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

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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

Abstract Views :110 | PDF Views:0

Authors

Harsh Singh Rajput ¹, Mohit Law ¹

Affiliations
1 Indian Institute of Technology Kanpur, Kanpur, IN

Source

Manufacturing Technology Today, Vol 22, No 2 (2023), Pagination: 17 - 22

Abstract

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 Tools

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References

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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

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Machine Tool Multibody Dynamic Model Updating Using Vision-Based Modal Analysis

Abstract Views :123 | PDF Views:0

Authors

Vishal Singh ¹, Mohit Law ¹

Affiliations
1 Indian Institute of Technology Kanpur, Kanpur, IN

Source

Manufacturing Technology Today, Vol 22, No 2 (2023), Pagination: 23 - 28

Abstract

Machine tool dynamic behaviour is influenced by the structural properties of its subsystems assembled at interfaces as well as by the interface characteristics. Interfaces are commonly modelled as spring-damper connections, parameters of which are usually updated by minimizing the difference between model-predicted and measured dynamics characterized by frequency response functions. This model updating approach requires global mode shapes to be measured by roving the hammer and/or the sensor such as to localize the joint parameters to be updated. Such measurements are time consuming and fraught with errors. As a new, alternative, and simpler way to update joint parameters of a machine tool multibody dynamic model, this paper reports on the use of full-field vision-based modal analysis methods. Mode shapes thus identified agree with those estimated with the traditional experimental modal analysis procedures. The updated machine tool multibody dynamic model is a step towards realizing an accurate digital twin.

Keywords

Machine Tools, Model Updating, Vision-Based Modal Analysis, Dynamics, Joints, Frequency Response Function, Digital Twin

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References

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Gupta, P., & Law M. (2021). Evaluating tool point dynamics using smartphone-based visual vibrometry. Procedia CIRP, 101, 250-253. https://doi. org/10.1016/j.procir.2020.09.196

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Lambora, R., Nuhman, A. P., Law, M., & Mukhopadyay, S. (2022). Recovering cutting tool modal parameters from fractionally uncorrelated and potentially aliased signals. CIRP Journal of Manufacturing Science and Technology, 38, 414-426.

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Law, M., Gupta, P., & Mukhopadhyay, S. (2020). Modal analysis of machine tools using visual vibrometry and output-only methods. CIRP Annals, 69(1), 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. CIRP Annals, 71(1), 329-332.

Nuhman A. P., Singh, A., Lambora, R., & Law, M. (2022). Methods to estimate subpixel level small motion from video of vibrating cutting tools. CIRP Journal of Manufacturing Science and Technology, 39, 175-184.

Learning Machining Stability Diagrams From Data Using Neural Networks

Abstract Views :118 | PDF Views:0

Authors

Namras Amakkattil Shanavas ¹, Mohit Law ¹, Manjesh K. Singh ¹

Affiliations
1 Indian Institute of Technology Kanpur, Kanpur, IN

Source

Manufacturing Technology Today, Vol 22, No 2 (2023), Pagination: 29 - 41

Abstract

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, Hyperparameters

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References

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