# Exploring the ViDiDetect Tool for Automated Defect Detection in Manufacturing with Machine Vision
## 5 Conclusions  
Machine vision systems that enable the implementation of neural networks, equipped with deep learning algorithms, allow for recognizing, analyzing, and interpreting images in a way similar to human vision. Through the use of vision systems, neural networks are able to automatically detect objects, recognize faces, classify images, analyze movement, and generate image descriptions. Deep learning algorithms learn from large amounts of data, which allows them to work more precisely and efficiently. Vision systems using these advanced techniques are used in many fields, such as medicine, industry, security, facial recognition, autonomous cars, and many others. In order to carry out the selected task, the Cognex D900 series camera in monochrome version was selected due to its high resolution, adaptability, and operation of the illuminator as well as communication capabilities (file transfer--FTP Client). The illuminator and other elements necessary to carry out the data acquisition process were selected for the vision system. On the basis of the collected data, the analysis and learning of the neural network was carried out. Analyzing the obtained results, aimed at assessing the possibility of using a vision system to assess the wear of the cutting tool, it was found that there is a possibility of implementing this system in production. Implementation of such a system and ensuring communication between the vision system and the controller or superior plant management system can lead to the optimization of costs incurred for tool replacement. Using the vision system, it is also possible to fully automate the tool change process. The vision system based on the Cognex smart camera correctly classifies defects on the test surface resulting from the wear of the cutting blade, which was confirmed in the presented research. The system measures the area of damage and chips on the edges of the processed surfaces and, on this basis, performs an appropriate classification. The In-Sight Vision Suite software was used to configure the camera parameters and program the logic of its operation. Using the available functions, it is possible to implement a neural network using the ViDiDetect tool. An important element in the network training process is the proper determination of the region of interest and the imposition of a mask that will allow ignoring irrelevant places in the region of interest. Some of the industries that can derive substantial benefits from this innovative work include manufacturing industries, such as metalworking, automotive, aerospace, and  
Figure 12: A selected example of neural network classification results after applying a mask. electronics, which have heavily relied on cutting tools. Implementing a vision-based tool wear assessment system can lead to increased efficiency, reduced downtime, and cost savings. Industries where product quality is paramount, such as pharmaceuticals, food processing, and consumer electronics, can use this technology to ensure the integrity and consistency of their products, reducing defects and recalls. Heavy machinery and equipment in mining and construction undergo significant wear and tear. Machine vision systems can help monitor and optimize the maintenance of critical components, enhancing safety and productivity. The study, while showcasing the potential of machine vision systems and deep learning algorithms in recognizing and interpreting images for various applications, has some limitations. These limitations include potential data variability, the complexity of defects, and questions about the model's generalization to unseen scenarios. Additionally, the application of masks, although improving classification results, may present challenges in adapting to diverse surfaces and defect types. Future aspects of research in this domain could involve collecting a more diverse dataset, exploring advanced defect detection techniques, developing adaptive masking strategies, and conducting a thorough cost-benefit analysis for real-world implementation.