Abstract
The use of artificial intelligence technologies to improve the effectiveness of quality control in industrial enterprises is being considered. The article analyzes the methods and algorithms used to automate the detection and classification of defects in products. Special attention is paid to convolutional neural networks (CNN) and recurrent neural networks (RNN), which have demonstrated significant advances in image processing and time series, respectively. Real-world examples of the introduction of artificial intelligence (AI) into production processes are presented, demonstrating improvements in the accuracy and speed of defect detection. The evaluation of the effectiveness of the AI application showed a significant reduction in the cost of correcting defects and improving product reliability. The analysis of successful projects has shown that the integration of AI into quality control systems leads to optimization of production processes and improvement of economic results. The article offers practical recommendations on the implementation of AI in quality control, as well as directions for future research, including identifying gaps and prospects for further development of AI technologies in the context of defect detection. The publication provides valuable information for specialists in the field of quality control and development of AI systems seeking to improve efficiency and accuracy in industrial production