The Role of Artificial Intelligence in Quality Control Automation: Impact of AI Technologies on Traditional Defect Detection Methods and their Integration into Production Processes

The study aimed to assess the effectiveness of artificial intelligence (AI) technologies in automating quality control in production. The study analysed the application of AI technologies to automate quality control in production processes. The study analysed modern approaches to the implementation of computer vision, deep learning algorithms and predictive analytics to improve the accuracy of defect detection, reduce the influence of the human factor and ensure continuous product monitoring. The study determined that the use of computer vision, deep learning and predictive analytics systems helps to improve accuracy, optimise costs and reduce production defects. The study determined that the use of computer vision, deep learning and predictive analytics has the potential to improve the accuracy of defect detection, reduce the impact of the human factor and optimise the cost of product quality control. The analysed examples of the integration of smart technologies in various industries demonstrate the effectiveness of such solutions, provided they are properly adapted to the production environment. The study established that traditional quality control methods have limitations and need to be modernised by supplementing them with digital solutions. Modelling has demonstrated that the introduction of AI technologies can help to increase the efficiency of production processes, reduce reject rates and improve equipment maintenance. Comparative analysis demonstrated a potential reduction in product inspection time and the number of undetected defects using computer vision systems and deep learning algorithms. The possibilities of predictive analytics, which, according to the results of the literature analysis, can ensure timely maintenance of equipment and prevent its failures, were analysed. The study concluded that the combination of traditional control methods with intelligent technologies reduces production losses and can increase the overall productivity of enterprises if properly adapted.