In industrial manufacturing processes, the quality of the output product depends greatly on process parameters including input material properties, operating parameters, and environmental conditions. These parameters may not actually be determined accurately, and may even change continuously during operation in an uncontrolled manner. Therefore, one of the major challenges is adjusting the manufacturing process parameters to achieve the required product quality. The conventional trial-and-error method used for this task is time-consuming, costly, and highly dependent on the mechanical system, and the skills and experience of the operator. Researching and developing optimal methods that allow effective and quick adjustment of manufacturing process parameters to achieve required product quality is the main goal of the EDPOMP research project. These methods are developed based on highly accurate numerical models based on artificial intelligence. This project will focus on three advanced and popular manufacturing technologies in heavy industry including metal additive manufacturing (3D printing), sheet metal stamping/forming, and automated welding.
Currently, according to the general trend, Vietnam is implementing a strong digital transformation process towards Industry 4.0 with the goal of promoting manufacturing enterprises in Vietnam to design and produce their own products to meet the needs of domestic and export demand. The EDPOMP project will conduct in-depth research on the fundamental physical processes in advanced manufacturing technologies and will develop this knowledge into numerical simulation tools that use machine learning to optimize input parameters for manufacturing processes. The results of this project will help improve production efficiency, especially for the medical device manufacturing, automotive engineering, and aerospace industries.
