A student from Suzhou University of Science and Technology has helped develop an artificial intelligence solution to improve efficiency and reduce costs in small and medium-sized manufacturing enterprises.

Zhu Weijie, a student at the university's School of Electronic and Information Engineering, joined a long-running research project on MES injection molding in 2024 after being recommended by his mentor. Together with his professor, Zhu visited more than 20 small and medium-sized injection molding companies in cities such as Suzhou and Changzhou to better understand the challenges faced by manufacturers.

During the visits, the team observed that experienced technicians often relied on trial and error to adjust machine parameters. Meanwhile, many factories used equipment from different brands that could not share data, and expensive smart manufacturing systems remained out of reach for smaller companies.

To address these problems, Zhu and his team proposed using artificial intelligence to create a lightweight, affordable solution. Instead of building a large, complex software platform, they focused on a core function: intelligent parameter recommendation.

Working with partner companies, the team built a database containing more than 70,000 sets of injection molding process parameters. By constructing a knowledge graph, the AI system was trained to understand the relationships between different production variables.

Early results were not perfect. The pressure parameters recommended by the AI often differed significantly from real production values. Under their mentor's guidance, the team introduced a reverse correction module that recorded manual adjustments made by experienced technicians. These corrections were then used to retrain the AI model, enabling it to improve over time.

The intelligent recommendation module has now been put into trial use in two companies.

Behind this progress is a broader support system at the university. The School of Electronic and Information Engineering encourages students to convert internship and practical training experience into academic credits, and many graduation projects are directly aligned with real industry needs.