Development and evaluation of virtual reality international equipment manufacturing training platforms based on deep learning

Traditional equipment manufacturing training methods face many problems, such as high resource consumption, high safety risks, time and space limitations, difficulty in personalized teaching, and incomplete evaluation. This article aims to develop an intelligent international equipment manufacturing training platform by combining deep learning and virtual reality (VR) technology to address these issues. Firstly, the virtual reality technology was utilized to create immersive virtual environments, reducing reliance on physical equipment and venues, lowering costs and resource consumption, while eliminating security risks in real operations. Students could conduct practical training anytime and anywhere in a virtual environment, overcoming the limitations of traditional training in terms of time and space, and enhancing the flexibility and convenience of learning. Then, through deep learning algorithms CNN (convolutional neural network) and RNN (recurrent neural network), the platform could analyze students' operational data in real-time and provide personalized feedback and guidance. The teaching content and difficulty were adjusted according to the learning progress and performance of the students, ensuring that each student can have a suitable learning experience. In addition, deep learning algorithms could automatically evaluate students' operational skills and learning effectiveness, and generate detailed evaluation reports, making the evaluation process more objective and fair, and avoiding the influence of subjective factors. The training platform developed in this article could also simulate complex equipment manufacturing scenarios and troubleshooting processes, allowing students to significantly improve their scores in operational accuracy, efficiency, fault recognition ability, and problem-solving ability. The average improvement percentage of students in various evaluation indicators was about 43.83%. Through the development and evaluation of this platform, this article aims to significantly improve the efficiency, safety, and learning effectiveness of equipment manufacturing training, providing an innovative solution for international equipment manufacturing training.