# Exploring the ViDiDetect Tool for Automated Defect Detection in Manufacturing with Machine Vision
## 2 State of the Art
These systems analyze historical wear data, consider machining parameters, and employ predictive models to estimate the remaining tool life accurately. This information enables manufacturers to proactively schedule tool changes and minimize downtime [26; 27; 28]. Three-dimensional imaging and surface metrology: Traditional 2D imaging techniques may not capture the complete wear profile of cutting tools. To overcome this limitation, 3D imaging technologies, such as structured light scanning and confocal microscopy, have been employed. These methods enable the acquisition of three-dimensional surface data, facilitating detailed wear analysis and precise measurement of wear parameters, including wear depth, wear volume, and wear rate. Integrating surface metrology techniques with machine vision systems enhances the accuracy and reliability of tool wear assessment [29; 30]. Deep learning and anomaly detection: Deep learning algorithms, particularly those based on recurrent neural networks (RNNs) and generative adversarial networks (GANs), have demonstrated promising results in tool wear assessment. These algorithms can learn complex patterns and anomalies from large datasets, enabling the detection of subtle wear features and identifying abnormal wear conditions. By training machine vision systems with extensive wear data, these algorithms can enhance the system's ability to identify wear patterns accurately and predict wear progression [31; 32]. Multi-sensor fusion: To further enhance the accuracy and reliability of tool wear assessment, machine vision systems are being combined with other sensing technologies, such as acoustic emission sensors, vibration sensors, and temperature sensors. By fusing data from multiple sensors, a comprehensive picture of tool condition can be obtained. This multi-sensor approach provides a more holistic understanding of tool wear, enabling more informed decision-making and proactive maintenance strategies [33; 34; 35]. Despite these advancements, challenges still exist in the application of machine vision systems for cutting tool wear assessment. Variations in lighting conditions, tool geometries, and complex wear patterns can pose difficulties for accurate wear detection. Standardizing image acquisition protocols, developing robust algorithms, and addressing these challenges through ongoing research and development efforts will further enhance the capabilities and reliability of machine vision systems for tool wear assessment. Table 1 presented the representation of the state-of-the-art advances in machine vision systems for cutting tool wear assessment. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & \multicolumn{2}{c}{**State-of-the-Art Advances**} \\ \hline Imaging Technology & - & High-resolution cameras and microscopy systems \\  & - & Multispectral and hyperspectral imaging \\ \hline Image Processing Algorithms & - & Edge detection, thresholding, feature extraction \\ \hline Image Processing Algorithms & - & Advanced machine learning, including CNNs \\ \hline Real-Time Monitoring & - & Continuous wear assessment during machining \\  & - & High-speed image processing and parallel computing \\ \hline Integration with Manufacturing & - & Integration with CAM and CNC systems \\ \hline Systems & - & Closed-loop feedback for process optimization \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The state-of-the-art advances in machine vision systems for cutting tool wear assessment. In conclusion, the state of the art in machine vision systems for cutting tool wear assessment has witnessed remarkable progress. Through advancements in imaging technology, image processing algorithms, real-time monitoring, integration with manufacturing systems, and automated tool life prediction, these systems are revolutionizing the way tool wear is assessed in industrial environments. As research and development continue to push the boundaries of this technology, manufacturers can expect further improvements in productivity, tool life optimization, and overall manufacturing efficiency.