Digital Transformation: Challenges About Iot and Ai in High Pressure Die Casting (HPDC)

This study explores and systematically reviews advancements in applying artificial intelligence (AI) and Internet of Things (IoT) technologies to the high-pressure die casting (HPDC) process. Widely utilized in aluminum automotive part production, HPDC presents significant challenges, including defect reduction, process optimization, and predictive maintenance. The primary objective of this research is to examine how AI and IoT can address these challenges by enhancing decision-making, improving quality control, and increasing operational efficiency. The systematic review, based on PRISMA guidelines, analyzed 16 peer-reviewed studies that focus on the integration of machine learning (ML) and deep learning (DL) techniques in HPDC. The findings reveal critical applications, such as defect detection, predictive modeling for optimizing the solidification cycle, and fault diagnosis. Despite the potential benefits, challenges remain, including high implementation costs, data quality issues, and the limited interpretability of AI models. Furthermore, the analysis underscores the predominance of private datasets and highlights the need for public, anonymized repositories to foster collaborative research. This study contributes by providing a comprehensive overview of AI applications in HPDC, identifying existing barriers, and proposing directions for future research. These findings reinforce the transformative potential of AI in advancing Industry 4.0 and offer valuable insights for academia and industry, paving the way for more sustainable and efficient HPDC operations.