# Exploring the ViDiDetect Tool for Automated Defect Detection in Manufacturing with Machine Vision
## 1 Introduction  
In today's industrial landscape, optimizing manufacturing processes and improving productivity are of paramount importance. Among the various factors influencing production efficiency, the condition of cutting tools plays a vital role. Cutting tools are subjected to rigorous wear and tear during machining operations, leading to a decline in their performance and adversely affecting the quality of the final product. Timely and accurate assessment of tool wear is crucial for ensuring efficient machining operations, reducing downtime, and maximizing tool life [1]. Traditionally, manual inspection methods have been employed to assess cutting tool wear. These manual methods, which often involve human operators visually inspecting tools, are not only time-consuming but are also highly subjective and prone to human error. Studies have shown that manual tool wear observation can result in inconsistencies and inaccuracies in wear measurements, leading to suboptimal machining processes and reduced product quality. Statistics reveal that manual inspection methods contribute significantly to production inefficiencies. In a survey conducted across multiple manufacturing facilities, it was found that manual tool wear observation accounts for approximately 30% of unscheduled machine downtime due to incorrect assessments and the time needed for inspections. Moreover, the subjectivity of these methods introduces variability in tool wear assessment across different operators, making it challenging to establish consistent maintenance schedules [2; 3]. Machine vision refers to the technology that enables machines to visually perceive and interpret their surroundings. By leveraging advanced image processing techniquesand artificial intelligence algorithms, machine vision systems can analyze images or video footage of cutting tools and accurately detect and quantify various wear parameters. This technology offers a non-contact and non-destructive approach to tool wear assessment, allowing continuous monitoring of tools without disrupting the machining process [4; 5; 6]. The application of machine vision systems for cutting tool wear assessment has revolutionized the way manufacturers manage their machining operations. These systems can capture high-resolution images of cutting tools at regular intervals and compare them to reference images of new tools. By analyzing the acquired images, machine vision algorithms can identify and quantify wear patterns such as flank wear, crater wear, chipping, and edge rounding. This information provides valuable insights into the tool's degradation over time, allowing operators to proactively plan tool changes and reduce unplanned downtime [7]. Furthermore, machine vision systems can be integrated with existing manufacturing systems and databases, enabling real-time monitoring and data analysis. The collected wear data can be used for trend analysis, predictive maintenance, and process optimization. Manufacturers can leverage this information to identify optimal tool life, improve tool selection, and optimize machining parameters, leading to enhanced productivity, reduced costs, and improved product quality [1]. In this article, application of machine vision systems for cutting tool wear assessment is presented. The underlying principles, the components of a typical machine vision system, and the image processing techniques used for wear detection and quantification are explored. Furthermore, the benefits and challenges associated with implementing machine vision systems in industrial settings are discussed. By comprehensively examining the current state-of-the-art technology, valuable insights into the potential of machine vision systems for improving manufacturing processes and optimizing tool life are aimed to be provided in this article. As a case study, an element of the car suspension (knuckle) was proposed, and the dependence of the impact of the wear of the cutting tool on the quality of the tested surface was examined.