Application Of Automation And Computer Vision In Reducing Failures In The Production Process Of Safety Belts

Product quality is one of the primary criteria considered by customers when choosing an item. Additionally, it is an essential factor for companies to stand out in a highly competitive market. In the Manaus Industrial Hub (PIM), in a machine used for producing safety belts, defect detection is a crucial stage in the production process. To enhance this task, Artificial Intelligence (AI) was implemented, standing out for its high efficiency in analyzing and processing data in industrial environments. The data was captured in image format by a camera, and using Deep Learning (DL) techniques, an intelligent algorithm capable of detecting faults was developed. Due to its autonomous learning capability and ability to identify and characterize defects, this algorithm represents the future of automated inspection. It has already achieved significant success in applications such as object identification and classification, facial recognition, and fault diagnostics. Given this context, the aim of this study is to propose an ideal solution to minimize failures in the production process of safety belts. The proposal seeks to automate the currently manual step using the concept of computer vision with AI, ensuring greater efficiency and reliability in the production process. Materials and Methods: The research, development, and application of AI with the algorithm in the case study were conducted in the R&D laboratory of the company located in the Manaus Industrial Hub (PIM). The project utilized product inputs, a camera equipped with a lens for capturing images, and a computer for data storage and algorithm development. Results: The application of AI in this environment uses computer vision systems to process image data. For this, a program was developed in Python with the PySimpleGUI library. The trained model was evaluated based on loss and accuracy metrics on the test set, achieving values of 0 and 100%, respectively. During testing, new belts were used, reaching 100% accuracy in the results. Conclusion: The proposed model showed excellent results. With data processed by AI using Deep Learning (DL) techniques, real-time inspection of the belts was achieved. Additionally, the network achieved perfect accuracy and recall in all tests conducted on the belts, demonstrating the effectiveness of the solution