: The tile business is a critical part of the national economic development, as it is one of the areas that provides employment, produces goods, and exports them. Regardless of its significance, there are challenges in the industry that are caused by issues with production, which in most cases is caused by low-quality materials used or by mishandling of the products during transportation. Historically, visitors to the site have been able to detect tile cracks using human eyes, which, in addition to being expensive and time-consuming, can also be unreliable. This paper proposes a trustworthy approach to identifying tile cracks using the YOLOv11 model to solve these challenges. It is a combination of enhanced CNN preprocessing and the YOLOv11 model to enhance the effectiveness of crack detection. It takes advantage of the capabilities of the YOLOv11 model to detect cracks on tiles and aims to use a wide range of tile images in different lighting conditions, textures, and types of defects. The approach uses a powerful tile image analysis approach, and the high detection precision with the bounding box method is 88.30%, and the mask method precision is 88.77% with a small number of false positives.
V. Pal, Pankaj Mudholkar· Journal of Computer Science· 0 citations
Abstract. CPMS have become a major facilitator of smart factories through a combination of physical operations and the computational intelligence and real-time communication. Combination of Internet of Things (IoT) technologies and machine intelligence offers novel possibilities to realize adaptive, efficient and autonomous manufacturing processes. Nevertheless, the co-ordination of the physical and cyber layer is a serious issue because of the dynamic characteristic of industrial set ups and the high amount of heterogeneous sensor data. This paper proposes a cyber-physical architecture based on the IoT, which uses machine intelligence to monitor, analyze, and control manufacturing systems in real-time. The approach that is proposed is based on sensor-driven data acquisition, intelligent processing, and adaptive decision-making to optimize system performance. To reduce the production inefficiency, energy usage as well as fault occurrence, a multi-objective formulation is created in the context of keeping operational constraints. The structure is tested on a realistic manufacturing case based on real-time data input. Findings indicate the improvement of operational efficiency, the capability of fault detection and the response time is reduced, compared to the traditional methods. The methodology suggested can be both scaled and powerful to serve next-generation intelligent manufacturing systems.
Pankaj Mudholkar· Materials Research Proceedin...· 0 citations