# Exploring the ViDiDetect Tool for Automated Defect Detection in Manufacturing with Machine Vision
## 3 Proposed Solution to the Problem
### Design Assumptions  
The station equipped with a vision system has the ability to communicate via digital inputs/outputs with a PLC controller, in order to provide information about the need to replace the tool or the occurrence of irregularities detected by the system. The vision system is a system based on a smart camera equipped with the possibility of implementing machine learning algorithms. The camera is capable of high-resolution image capture and is configured to focus on a specific region of interest, which is the machined surface of the car suspension knuckle. The camera captures images of the machined surface at regular intervals. These images are taken both before and after tool changes to assess tool wear over time. The camera's illuminator was selected in such a way that, compatible with the lighting in the production hall of plant, it directed a beam of white LED light to the tested element of the detail (car knuckle clamp) in a way that allowed for the best contrast between the background and the edge of the tested surface. Due to the need to mount the camera at a distance that allows the details to move freely along the path of the automatic line, it was necessary to select a properly adapted lens in such a way that the examined part was clearly visible and was always in the field of view of the camera. The smart camera is configured in such a way that data acquisition occurs automatically based on the signal from the inductive sensor. The image analysis function utilizes deep learning-based defect detection. During the training phase, the system is taught what a good (undamaged) part looks like. Optionally, it can also be taught what bad (damaged) parts look like for comparison. To improve classification accuracy, a mask is applied to narrow the region of interest to the edge of the machined surface. This helps the system focus on the critical area for tool wear assessment. The defect detection tool then analyzes the images and identifies any deviations from the trained model. It returns a deviation score and a Pass/Fail result for each image. Images with defects (indicating tool wear) are flagged as \"bad\". The system is set up to log and store the images and assessment of the results. It uses an FTP server for efficient data transfer and storage. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & \multicolumn{2}{c}{**State-of-the-Art Advances**} \\ \hline Automated Tool Life Prediction & - & Predictive analytics and machine learning models \\  & - & Proactive tool change scheduling \\ \hline
3D Imaging and Surface & - & Structured light scanning, confocal microscopy \\ Metrology & - & Precise measurement of wear parameters \\ \hline Deep Learning and Anomaly & - & RNNs and GANs for complex pattern recognition \\ Detection & - & Improved wear pattern identification \\ \hline Multi-Sensor Fusion & - & Integration with acoustic, vibration, temperature \\  & - & Sensors for comprehensive tool condition analysis \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The proposed machine vision system automates the process of tool wear assessment. It continuously monitors tool conditions without disrupting the manufacturing process, reducing the need for manual inspections. By using advanced image processing techniques and deep learning algorithms, the system can detect subtle wear patterns that might be missed by human inspectors. This leads to more accurate and consistent assessments. The system offers real-time monitoring of tool wear. It provides instantaneous feedback on tool conditions, allowing operators to make timely decisions regarding tool changes. The machine vision system can be integrated with other manufacturing systems, such as CNC machines and PLC controllers. This integration facilitates data exchange, process optimization, and adaptive machining strategies. The system's ability to predict tool life based on historical wear data and machining parameters enables proactive maintenance. Manufacturers can schedule tool changes to minimize downtime.