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Application of machine vision for tool condition monitoring and tool performance optimization–a review

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Abstract

Rapid tool wear is a major concern of the machining operation, affecting the tooling cost and dimensional tolerance of the components. In line with Industry 4.0, rapid tool failure can be avoided by applying cyber-physical tool condition monitoring (TCM), which detects in-process tool wear evolution using sensors or machine vision systems, determining the actual time for tool replacement. Although sensor-based TCM is quick and adaptive in monitoring tool wear progression online, it cannot detect the failure modes to show the extent of wear severity on the tool’s cutting edge. On the other hand, machine vision systems effectively detect wear mechanisms that accelerate tool failure during machining. Therefore, this paper presents the practical application of machine vision systems in TCM and tool performance optimization (TPO). The findings in this research show that digital microscopes are used to monitor wear mechanisms, complementing TPO techniques in selecting the best cutting parameters that optimize tool performance. However, such techniques are time intensive and inefficient for real-time applications. With recent advances in imaging technology and artificial intelligence, an in-process machine vision-based TCM (MV-TCM) system is receiving more attention in intelligent manufacturing due to its efficient predictive capability. However, it is still in its infancy stage, relying on classical machine learning models, which are ineffective to extract high-level features on the tool wear images for in-process failure modes detection. Therefore, this paper highlights the significance of applying artificial intelligence to enhance MV-TCM capability for online failure modes detection and classification.

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Availability of data and materials

The data reviewed in this manuscript is available from the corresponding author on reasonable request.

Code availability

Not applicable.

Abbreviations

ABC:

Artificial Bee Colony

ANFIS:

Artificial neuro-fuzzy inference system

ANN:

Artificial neural network

CCD:

Central composite design

CCRD:

Central composite rotatable design

DCNN:

Deep convolutional neural network

DL:

Deep learning

DMLNN:

Deep multilayer neural network

DNN:

Deep neural network

DP:

Dynamic programming

DRL:

Deep reinforcement learning

ET:

Evolutionary techniques

GA:

Genetic algorithm

GAN:

Generative adversarial network

GCGAN:

Deep convolutional generative adversarial networks

GCN:

Graphical convolution network

GGRNN:

Gated Graph Recurrent Neural Networks

GNN:

Graphical neural network

HOSVD:

Higher-order singular value decomposition

LP:

Linear programming

ML:

Machine learning

MOEA:

Multi-objective evolutionary algorithm

MST:

Mathematical search techniques

MV-TCM:

Machine vision-based tool condition monitoring

NLP:

Nonlinear programming

NSGA:

Non-dominated sorting genetic algorithm

NSGA II:

Non-dominated sorting genetic algorithms version II

PCA:

Principle component analysis

PSO:

Particle swarm optimization

R-CNN:

Regional convolutional neural network

RBFNN:

Radial basis function neural network

RNN:

Recurrent neural network

RSM:

Response surface methodology

SVD:

Singular value decomposition

TCM:

Tool condition monitoring

TPO:

Tool process optimization

WNN:

Wavelet neural network

YOLO:

You only look once

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Authors and Affiliations

  1. Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham Malaysia, 43500, Selangor, Malaysia

    Tiyamike Banda, Ali Akhavan Farid, Veronica Lestari Jauw & Chin Seong Lim

  2. School of Mechanical Engineering, Dongguan University of Technology, Dongguan, 523808, China

    Chuan Li

Authors
  1. Tiyamike Banda
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  2. Ali Akhavan Farid
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  3. Chuan Li
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  4. Veronica Lestari Jauw
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  5. Chin Seong Lim
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Contributions

All authors contributed to the conceptual idea of the manuscript. The first draft of the manuscript was written by Mr. Tiyamike Banda, and all authors commented on the previous versions. Dr. Ali Akhavan Farid and Dr. Chin Seong Lim worked on supervising, reviewing, and editing the manuscript. Prof. Chuan Li and Dr. Veronica Lestari Jauw worked on co-supervision and editing of the manuscript. All authors read and finally approved the final version of the manuscript.

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Correspondence to Ali Akhavan Farid.

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Banda, T., Farid, A.A., Li, C. et al. Application of machine vision for tool condition monitoring and tool performance optimization–a review. Int J Adv Manuf Technol 121, 7057–7086 (2022). https://doi.org/10.1007/s00170-022-09696-x

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