Machine Vision Techniques for Tool Condition Monitoring

Machine vision for tool condition monitoring encompasses non-contact methods that employ cameras and image processing to assess the health of cutting tools in real time. By capturing high-resolution images of tool flanks, edges and chips, these systems apply segmentation, feature extraction and pattern recognition to quantify wear parameters such as flank wear width, wear area and chipping. Advances in lighting design, multi-angle imaging and inline calibration have enabled on-machine deployment, reducing reliance on manual inspection and minimising production downtime. Recent efforts have integrated infrared thermography to map temperature distributions as proxies for tool degradation, while deep learning architectures—most notably convolutional neural networks—have been trained on visual and thermographic datasets to classify wear states with high accuracy. The coupling of vision systems with Industry 4.0 infrastructures, including edge computing and the internet of things, supports predictive maintenance and optimised tool-change policies, delivering higher throughput, improved surface quality and lower tooling costs across aerospace, automotive and precision manufacturing sectors.

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Machine Vision Techniques for Tool Condition Monitoring publication trend

The graph below shows the total number of articles in machine vision techniques for tool condition monitoring across all publications each year (not limited to Nature Index journals).

Technical terms

Machine vision: Automated acquisition and processing of visual information to assess tool condition.

Convolutional neural network (CNN): A class of deep learning models designed to analyse visual data through layered feature extraction.

Flank wear: The progressive erosion of the cutting tool’s side face that affects dimensional accuracy and surface finish.

Infrared thermography: Imaging technique that captures temperature distribution across a tool surface to infer wear states.

Image segmentation: The process of partitioning an image into regions to isolate features of interest such as wear zones.

References

1. Measurements of Tool Wear Parameters Using Machine Vision System. Modelling and Simulation in Engineering (2019).
2. Automatic Identification of Tool Wear Based on Convolutional Neural Network in Face Milling Process. Sensors (2019).
3. Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process. Sensors (2021).
4. Tool Condition Monitoring of the Cutting Capability of a Turning Tool Based on Thermography. Sensors (2021).
5. State-of-the-art review of applications of image processing techniques for tool condition monitoring on conventional machining processes. The International Journal of Advanced Manufacturing Technology (2023).
6. Methodology for Measuring the Cutting Inserts Wear. Symmetry (2022).