## 1 Introduction
### Automation in Cast Component Manufacturing Process  
The concept of AIIIoT integrates methods with automation technology in cast component manufacturing. AI-automation through the IoT framework increases quality control without direct human intervention. This aspect is supported by research [46], which underscores that modern foundry technologies increasingly focus on precision castings requiring a high degree of automation. This is further reinforced by [PERSON] and [PERSON] [47], who note that the complexity of manufacturing processes in foundries has improved quality production outcomes. Another aspect of Industry 4.0 that is maintained in Industry 5.0 is preserved by the AIIIoT concept, recycling residual products, which promotes environmental sustainability. The introduction of computer vision systems has brought advancements to the sandcasting workflow. [PERSON] et al. [48] describe a vision-driven approach that leverages image processing to accurately positions molds before pouring. This automated technique supports real-time adjustments and maintains strict quality requirements. Similarly, [PERSON] et al. [49] highlight the importance of tracking parts throughout foundry operations via additively manufactured digital codes, which allow data collection and casting quality analysis. The role of automation also extends to optimizing gating systems in steel castings. Modern foundry engineering uses sophisticated technologies, including automated gating systems, which improve molten metal flow and reduce defects in the final product [50]. Furthermore, integrating IoT technologies into foundries facilitates real-time monitoring and process automation [51]. [PERSON] et al. [52] emphasized advanced robotics in foundry operations, especially material handling and transport. In this way, removing the operator aligns with one of the core principles of Industry 4.0. They show that foundry cranes with automation capabilities reduce physical effort. It should be noted, however, that not all processes allow the complete exclusion of human operators, a point promoted in Industry 5.0. The quality of cast components ensures defect prevention. This is a measure of prevention against catastrophic failures in critical applications such as turbine blades and vehicle components [53]. Modern techniques, such as Rheocasting (a semi-solid casting process) have reduced the gas porosity in aluminum alloys. This advancement has contributed to improving microstructural integrity. These innovations increase component performance. At the same time, it aligns with sustainable manufacturing practices by reducing waste and energy consumption [54; 55]. Such practices are promoted in both Industry 4.0 and 5.0. Implementing optical systems and 3D scanning has revolutionized defect detection by assessing dimensional accuracy in cast components [56]. These technologies facilitate swiftly identifying and rectifying issues. Consequently, it reduces scrap rates. Integrating data-driven approaches and ML techniques has strengthened the ability to control casting quality [57]. Factors such as mold design, pouring temperature, and cooling rate influence the final properties of cast components [58]. Research has underscored the importance of process optimization and statistical methods to monitor and control these parameters. Standards specific to Industry 5.0 focus on human beings and the collaboration between humans and cobots. The defining element of Industry 5.0 is the human, who must benefit from physical, mental, and emotional well-being [59]. These ideals are promoted as an additional layer added to Industry 4.0 and defined in version 5.0, which integrates AI into most human activities [60]. This paper presents a perspective on the implications of achieving these ideals, with applicability in defect detection in the cast production process. The first issue concerns identifying elements that exhibit defects. Traditionally, this identification would be limited to image analysis.