Integrating Advanced Mathematics, AI Algorithms, and Intelligent Manufacturing: Development and Teaching Practice of a Digital Training Platform

Abstract

Because AI, digital technologies and intelligent manufacturing are developing very fast in recent years, higher education now faces increasing demands for interdisciplinary talents who can integrate mathematical knowledge with computational and engineering skills that are important for solving real problems in industrial settings. But advanced mathematics courses are taught separately from AI algorithms. Students cannot see the practical value of mathematical concepts when they learn these theories in class, and they do not know how to use these methods to solve engineering problems in intelligent manufacturing systems. We build an integrated training platform for education and it combines advanced mathematics, AI algorithms and intelligent manufacturing from perspective of digital-intelligent industry-education integration, which means we try to connect theoretical knowledge with real industrial practice in teaching process and help students understand the application background of each mathematical concept they learn. The platform includes calculus, linear algebra, probability, statistics and optimization. These mathematical concepts are connected with AI algorithm training and intelligent manufacturing applications so that students can see how mathematics is used in real systems and understand the relationship between abstract theory and concrete engineering practice. We design a project-based teaching model for this platform that can help students learn through real projects and gain practical experience in solving engineering problems with mathematical methods and AI algorithms in a systematic way that connects different knowledge points together and builds their engineering thinking. It can guide students through complete learning process consisting of mathematical modelling, data processing, algorithm implementation, manufacturing simulation, and result evaluation. We conduct a teaching practice to check if it works and the results show the integrated platform can improve students' understanding of mathematical concepts and help them see the practical meaning of abstract theories in engineering applications that require both mathematical thinking and computational methods to solve complex problems in intelligent manufacturing. Also, it can enhance their ability to apply AI algorithms. It is worth noting that their engineering problem-solving skills are strengthened through practice in the simulation environment that requires students to integrate mathematical methods and AI techniques to handle real manufacturing data and optimize the production process according to practical constraints and engineering standards that are commonly used in industry. Also, the platform provides an interactive learning environment with practice. It promotes the integration of theoretical knowledge with real-world industrial problems that students may encounter in their future careers and helps them build systematic thinking for solving complex engineering tasks with mathematical and computational tools in a collaborative way that mimics real industrial projects and develops their professional skills. The proposed approach can be used as a reference for interdisciplinary STEM education and the development of digital training platforms in higher education that need to integrate multiple disciplines and build practical teaching systems for training talents in the digital-intelligent era with industry-education integration characteristics.