## 5 Conclusions  
This study aims to assess if combining sensors and advanced algorithms into an IoT framework may improve quality control in cast manufacturing environments utilizing AI methodologies. This mainly contributes to the field of Industry 5.0, introduced by this paper as an AICT model for quality control in foundries. The main contributions are as follows:  
* The AIoT infrastructure is developed by integrating Azure Functions, Azure Event Grid Trigger, and Azure IoT Hub. This setup demonstrates the ability to ensure compatibility between heterogeneous devices. * Implementing the NIA algorithm optimized for defect detection in cast parts. This contribution is possible through customized ML algorithms tailored to the issue of defects. * This contribution demonstrates the viability of using Microsoft Azure to automate quality control processes. It highlights the collaborative industry through Microsoft Azure Hub. Integrating these solutions enables remote communication, which facilitates the transition from Industry 4.0 to Industry 5.0. Through these contributions, our study opens new directions for the integration of AI and IoT in industrial production. In this way, it supports the Industry 5.0 goal of combining intelligent automation with human expertise to create a production environment that improves the quality of human life. The system improved defect detection accuracy by developing the NIA algorithm. This algorithm employs a unique calculation formula considering multiple cast positions as it moves along the conveyor belt. Capturing consecutive frames allowed the algorithm to aggregate probabilities across different cast orientations, mitigating the impact of transient anomalies and environmental variables. The field has gained new knowledge by creating a novel algorithm. The contribution improves fault detection via multi-positional analysis of castings in motion. Furthermore, the study proposes a practical framework for integrating various IoT devices in manufacturing. The integration utilizes cloud services to address compatibility difficulties. This method is consistent with Industry 4.0 ideals, which encourage automation, data interchange, and smart manufacturing processes. Our study found that an IoT-based system, including real-time sensor fusion and a tailored AI algorithm, increases quality control procedures in cast production settings. The technology detects defects with high accuracy. The proposed infrastructure demonstrates high metric values by using Azure Custom Vision. The proposed solution requires less human participation by the Industry 4.0 principle. This work's implications outline how integrating advanced technologies leads to efficient production processes. The methodologies and findings serve as a foundation for further studies to improve industrial quality control systems, ultimately contributing to the transition of Industry 4.0 to Industry 5.0. Future studies could focus on enhancing the defect detection models by incorporating advanced image preprocessing techniques to mitigate the effects of environmental variability. Implementing edge computing solutions to perform initial data processing on-site may reduce latency and dependency on cloud connectivity. Moreover, expanding the sensor fusion approach to include additional sensor types, such as infrared or 3D imaging sensors, could provide more data for defect analysis to explore more AI models. **Author Contributions:** Conceptualization, C.-M.R. and G.R. ; methodology, C.-M.R. and A.S.; software, C.-M.R. ; validation, C.-M.R. and A.S.; formal analysis, C.-M.R. and A.S.; investigation, A.S. and G.R. ; resources, A.S.; data curation, C.-M.R. ; writing--original draft preparation, C.-M.R. and A.S.; writing--review and editing, A.S.; visualization, C.-M.R. and A.S.; supervision, C.-M.R. and G.R. ; project administration, C.-M.R. ; funding acquisition, G.R. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Petroleum-Gas University of Ploiesti, Romania.