Rohollah Ghasemi
Senior Lecturer in Mechanical Engineering
The AI-CAST project aims to develop new methods for using artificial intelligence to monitor and optimise high-pressure die casting of magnesium alloys in real time. The goal is to create a more reliable and sustainable manufacturing process through smart data analysis and machine learning.
"With AI and machine learning, we can replace traditional working methods and make the casting of magnesium alloys both smarter and more resource-efficient. This means lower costs, less waste, and lighter, more sustainable products, while also strengthening Europe’s green transition and the competitiveness of its industry.
Rohollah Ghasemi, Project Manager and Senior Lecturer in Mechanical Engineering
The AI-CAST project aims to establish a real-time (on-line) monitoring AI-based approach to implement and develop a robust optimisation quality assurance strategy for producing sound HPDC Mg components.
Sustainable manufacturing concepts
This development will be achieved through the integration of AI-models (using Data-Driven Analytics (DDA) and Machine Learning (ML) techniques) into the HPDC manufacturing process.
The target alloy(s) selected for this project are high-performance challenging-to-cast Mg alloy(s). Casting of this unique alloy(s) is one of today’s main challenges for both industrial and academic sectors dealing with sustainable and robust manufacturing concepts where the weight reduction combined with improved performance are important concerns.
Our planned strategy in this project improves the overall casting quality through reducing at least casting defects mainly gas and shrinkage porosities issues.
