Author(s)

YanQiao Ji

Affiliation(s)

Liaoning Equipment Manufacturing Vocational and Technical College, Shenyang 110161, Liaoning, China.

Corresponding Author

YanQiao Ji

ABSTRACT

The construction of a demonstrative virtual simulation training base for vocational education is a crucial task aimed at adapting to national strategies and digital economic development requirements, serving the cultivation of composite technical and skilled talents in the new era, and promoting the high-quality development of vocational education in the context of the rapid development of new generation information technologies represented by artificial intelligence, big data 5G, virtual reality technology, etc. In recent years, the national and local governments have continuously strengthened policy guidance, invested a large amount of financial support, and actively transformed their thinking to implement digital transformation. Major universities have explored new ways to use virtual reality new technologies to reform traditional education and teaching models, solve the "three highs and three difficulties" problems in vocational education teaching and training courses, improve teaching quality, and cultivate skilled talents that meet the needs of enterprises.In this context, we have designed and developed an intelligent manufacturing virtual simulation training platform featuring digital twin capabilities, based on the existing intelligent manufacturing training base. This platform comprises one production line with four unit islands, enabling it to simulate the entire operation process of each unit island. Through practical teaching activities involving 436 students from various majors, the platform has proven to effectively enhance students’ practical skills.

KEYWORDS

Intelligent manufacturing; Virtual simulation; Digital twins; Training platform

CITE THIS PAPER

YanQiao Ji. Research and practice on the construction of a virtual simulation training platform for intelligent manufacturing. Eurasia Journal of Science and Technology. 2024, 6(4): 31-35. DOI: 10.61784/ejst3023.

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