Rohollah (Amir) Ghasemi is the researcher aiming to improve quality assurance in high-pressure die casting (HPDC) through artificial intelligence and machine learning. His research project, AI-CAST, seeks to strengthen the competitiveness of Swedish industry while contributing to a more sustainable society.<\/p>\n
High-pressure die casting is widely used in sectors such as automotive and manufacturing, particularly for producing lightweight high-performance components in aluminum and magnesium alloys. But the process is highly complex. Small porosities\u2014tiny gas-filled or shrinkage cavities inside the material\u2014can weaken components and lead to high rejection rates.<\/p>\n
\u201dTraditionally, manufacturers have relied on trial and error to mitigate these issues\u2014a method that is slow, expensive, and often ineffective. The AI-CAST project instead aims to create an AI-driven, real-time quality assurance system that monitors and optimizes process parameters as the casting takes place\u201d says Amir Ghasemi, Senior Lecturer in Mechanical Engineering at the University of Sk\u00f6vde.<\/p>\n
After completing a postdoctoral position at J\u00f6nk\u00f6ping University, Amir joined Husqvarna Group in 2019, working as a materials specialist. There, he gained first-hand experience of the real challenges facing the industry. This became a turning point:<\/p>\n
\u201cI realized that traditional approaches are often too time-consuming and costly. I saw an opportunity to apply AI to develop robust and intelligent solutions to truly complex production problems,\u201d says Amir.<\/p>\n
The project brings together the University of Sk\u00f6vde, Husqvarna Group, NovaCast Systems, and UB Verktyg. Each partner contributes unique expertise: the University leads the research with its knowledge of AI and materials science; Husqvarna provides practical casting expertise, machinery, and real-world data; UB Verktyg contributes know-how in mold design and tooling; and NovaCast Systems supplies advanced simulation software to predict outcomes before physical trials are performed.<\/p>\n
This integrated approach ensures that the project is both scientifically innovative and firmly grounded in industrial needs.<\/p>\n
AI-CAST combines data-driven analytics and machine learning techniques with the goal of identifying a \u201crobust process window\u201d that ensures consistently high-quality production, with fewer defects and lower energy use. The target is to reduce porosity levels by at least 20 percent.<\/p>\n
For industry, this means lower production costs, less material waste, and higher product quality\u2014factors that improve competitiveness in the global market. For academia, the project generates new knowledge about how AI can be applied to complex physical processes, which is also integrated into teaching and thesis projects at the University of Sk\u00f6vde.<\/p>\n
\u201cOur vision is to establish a new foundation for how complex alloys are manufactured. In the long run, we want to contribute to a more sustainable industry, where resources are used more intelligently and Swedish manufacturing becomes more competitive and innovative,\u201d Amir explains.<\/p>\n
For Amir, the combination of academic and industrial collaboration is key to success.<\/p>\n
\u201cTechnical expertise alone is not enough. You also need mutual understanding and strong communication if research is to be transformed into practical solutions,\u201d he concludes.<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"