## 1 Introduction
### Paper Contributions  
The main contributions of the paper are described below:  
* Automated process for quality control using AI. This study explores integrating AI services through a customized model for defect detection in the cast, replacing human operator-based inspection. Aligned with Industry 5.0 principles, the proposed approach leverages advanced technologies to automate quality control processes while minimizing human intervention;
* Development of the Nonconformity Indicator Algorithm (NIA). The NIA algorithm introduces a novel calculation method that combines defect probabilities from multiple cast orientations captured in consecutive frames. This multi-frame analysis improves accuracy by mitigating the effects of transient anomalies, such as lighting variations or cast misalignment;
* The Azure Custom Vision Service has never been explored in the literature for cast quality assessment. This novel approach introduces advanced techniques that extend beyond traditional defect detection methods. The proposed approach leverages state of-the-art ML and image analysis capabilities. The proposed NIA achieves an accuracy of 98.18% and a _Precision_ of 98.44%, with a _Recall_ of 96.56% in detecting subtle cast defects under variable manufacturing conditions;
* Scalable IoT infrastructure. The system explains device compatibility challenges and proposes solutions leveraging Azure IoT Hub. It enables communication between heterogeneous devices programmed in C++, Bash scripts, and C#. This architecture ensures adaptability between devices in Industry 5.0 environments;
* Operational impact. The system lowers operational costs by reducing human intervention and automating defect detection. The proposed IoT-based infrastructure enables predictive maintenance;
* First-of-its-kind IoT infrastructure setup. The proposed IoT framework enables real-time communication between machines housed in different locations based on a tailored algorithm dedicated to our architecture, which combines powerful sensor technologies, data processing algorithms, and cast defect detection. This innovation supports distributed manufacturing for globally connected production systems in line with Industry 5.0 principles. The paper is organized as follows: Section 2 outlines the materials and methods used in the research. It begins by analyzing compatibility challenges in integrating devices programmed in various languages across different platforms (Section 2.1). Azure IoT Hub in Industry 5.0 manufacturing is presented in Section 2.2 as a solution for seamless communication among heterogeneous devices. Section 2.3 discusses implementing Azure Custom Vision for cast defect detection and introduces the proposed NIA. Section 3 is dedicated to the results. Section 3.1 provides the results of the successful application of Azure Custom Vision and NIA. Section 3.2 illustrates the integration of various devices into a cohesive IoT ecosystem by Azure IoT Hub, while Section 3.3 discusses overcoming compatibility challenges among devices programmed in C++, Bash scripts, C#, etc. Section 4 focuses on the discussion and research limitations. Section 5 concludes the paper, underscoring how the proposed IoT-based system advances quality control in cast manufacturing. Combining real-time sensor fusion with AI-driven methods supports the move from Industry 4.0 to Industry 5.0 principles. Furthermore, future work is presented. While the scalability of Azure IoT Hub is an inherent property, our contribution demonstrates how this scalability addresses specific challenges in manufacturing. The proposed IoT infrastructure was designed to integrate heterogeneous devices programmed in different languages (C++, Bash scripts, and C#) with unique configurations tailored for communication and coordination across platforms. Additionally, the NIA is a novel component specifically developed for this infrastructure. The NIA can be applied for a wide range of manufacturing parts and is not limited only to the cast.