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Artificial intelligence based tool condition monitoring for digital twins and industry 4.0 applications

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Abstract

The high demand for machining process automation has placed real-time tool condition monitoring as one of the top priorities of academic and industrial scholars in the past decade. But the presence of numerous known and unknown machining variables and challenging operating conditions such as high temperature and pressure makes it a daunting task. However, recent advancements in sensor and digital technologies have enabled in-process condition monitoring and real-time process optimization a highly accurate, robust, and effective process. Hence, the objective of the article is to provide a summary of the factors influencing the performance of cutting tools, critical machining variables to be monitored, techniques applied to monitor tool conditions, and artificial intelligence algorithms used to predict tool performance by analyzing and reviewing the literature. The future direction of intelligent cutting tools and how they would help in building the foundation for advanced smart factory ecosystems such as digital twins and Industry 4.0 are also discussed.

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Data availability

The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.

Code availability

Not applicable.

Abbreviations

MRR:

Material Removal Rate

PVD:

Physical Vapor Deposition

CVD:

Chemical Vapor Deposition

CBN:

Cubic Boron Nitride

PCD:

Polycrystalline Diamond

MQL:

Minimum Quantity Lubrication

OEE:

Overall Equipment Effectiveness

DSA:

Dynamic Signal Analyzer

FFT:

Fast Fourier Transform

SVM:

Support Vector Machine

HMM:

Hidden Markov Model

RBF:

Radius Basis Function

BPNN:

Back Propagation Neural Network

ANN:

Artificial Neural Networks

PCBN:

Polycrystalline Cubic Boron Nitride

CNN:

Convolutional Neural Network

FL:

Fuzzy Logic

AISI:

American Iron and Steel Institute

RA:

Regression Analysis

AI:

Artificial Intelligence

DTA:

Decision Tree Algorithm

DFT:

Discrete Fourier Transform

RF:

Random Forest

DC:

Direct current

CNC:

Computer Numerical Control

GA:

Genetic Algorithm

RMS:

Root Mean Square

RDC:

Ring-Down Count

BPNN:

Back Propagation Neural Network

CCD:

Charged Coupled Device

CMOS:

Complementary Metal-Oxide Semiconductor

HMM:

Hidden Markov Model

PCA:

Principal Component Analysis

ANFIS:

Adaptive Neuro-Fuzzy Inference System

RVM:

Relevance Vector Machine

ELM:

Extreme Learning Machines

NN:

Neural Networks

CNN:

Convolution Neural Networks

RNN:

Recurrent Neural Networks

GLCM:

Gray-Level Co-occurrence Matrix

IoT:

Internet of Things

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  1. Technology Centre (KSSPL GES), Kennametal India Ltd, 560073, Bangalore, India

    Muthuswamy Padmakumar

  2. Department of Mechanical Engineering, Viswajyothi College of Engineering and Technology, 686670, Kochi, India

    Shunmugesh K

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Muthuswamy, P., K, S. Artificial intelligence based tool condition monitoring for digital twins and industry 4.0 applications. Int J Interact Des Manuf 17, 1067–1087 (2023). https://doi.org/10.1007/s12008-022-01050-5

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