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Chatter detection and suppression in machining processes: a comprehensive analysis

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Abstract

Chatter is a phenomenon that occurs during machining operations, causing vibrations that can negatively impact the quality of the machined surface. Detecting and avoiding chatter is crucial for efficient machining processes. Various strategies have been developed to address this issue, including offline chatter prediction, online chatter detection and suppression, and the use of artificial intelligence (AI) solutions in line with Industry 4.0 trends. However, the topic of chatter detection is partially discussed as a section in some review publications, and it does not appear as a kernel focus. With the addition of the latest development in chatter detection and suppression, conducting a rigorous review of chatter is critical. This work entails tracing analytical chatter detection techniques (stability lobe diagram, Nyquist plot, finite element analysis), experimental chatter detection techniques by using various data acquisition signals and from time–frequency signal processing methods (fast Fourier transform, discrete wavelet transform, hilbert-huang transform, short-time Fourier transform, etc.), as well as the most recent AI techniques (artificial neural network, support vector machine, hidden markov model, fuzzy logic, k-nearest neighbor, etc.). A thorough investigation was conducted to determine the limitations of these various techniques and to provide potential solutions for detecting chattering in machining processes. Moreover, The approaches for suppressing chatter (active + passive) during the machining process will also be thoroughly reviewed in this article.

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Availability of data and material

All data generated or analysed during this study are included in article.

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Abbreviations

AI:

Artificial intelligence

SLD:

Stability lobe diagram

FEA:

Finite element analysis

FFT:

Fast Fourier transform

DWT:

Discrete wavelet transform

HHT:

Hilbert-Huang transform

STFT:

Short-time Fourier transform

ANN:

Artificial neural network

SVM:

Support vector machine

HMM:

Hidden Markov model

DOC:

Depth of cut

MRR:

Material removal rate

OFRL:

Open-loop frequency response locus

SDF:

Single degree of freedom

TDS:

Time-domain simulation

OTF:

Oriented transfer function

CNC:

Computer numerical control

SFMC:

Servo feed motor current

AC:

Alternating current

DC:

Direct current

TD:

Time-domain

FD:

Frequency-domain

TFD:

Time-frequency domain

WT:

Wavelet transform

PSD:

Power spectrum density

CNN:

Convolutional neural networks

KNN:

K-nearest neighbors

SOM:

Self-organizing map

TMD:

Tuned mass damper

DVA:

Dynamic vibration absorber

SSV:

Spindle speed variation

PD:

Proportional-derivative

CPU:

Central processing unit

GPU:

Graphics processing unit

TPU:

Tensor processing unit

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Acknowledgements

The authors extend their appreciation to the deanship of scientific research at King Khalid University for funding this work through large group project under grant number (RGP. 2/94/44)

Funding

The authors extend their appreciation to the deanship of scientific research at King Khalid University for funding this work through large group project under grant number (RGP. 2/94/44).

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Authors and Affiliations

  1. National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Abdul Basit, Niaz Bahadur Khan & Sadaqat Ali

  2. Mechanical Engineering Department, College of Engineering, University of Bahrain, Isa Town Campus, P.O. Box 32038, Isa Town, Kingdom of Bahrain

    Niaz Bahadur Khan & Riaz Muhammad

  3. Pedagogical Sciences, Tashkent State Pedagogical University, Bunyodkor Avenue, 27, 100070, Tashkent, Uzbekistan

    Dilsora Abduvalieva

  4. Department of Mechanical Engineering, Lebanese American University, Beirut, Lebanon

    M. Ijaz Khan

  5. Department of Mathematics and Statistics, Riphah International University, I-14, Islamabad, 44000, Pakistan

    M. Ijaz Khan

  6. Department of Civil Engineering, College of Engineering, King Khalid University, Abha, Asir, Saudi Arabia

    Mohammed Jameel

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Basit, A., Khan, N.B., Ali, S. et al. Chatter detection and suppression in machining processes: a comprehensive analysis. Int J Interact Des Manuf (2024). https://doi.org/10.1007/s12008-023-01716-8

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