Digital Twin and AI-Optimized Digital Design Method for Agricultural Machinery
Abstract：To address issues such as response lag and structural adaptability in the digital design of agricultural machinery, this study investigates digital twin-driven modeling, AI multi-objective optimization mechanisms, and modular collaborative design methods. It elaborates on the integration paths of these three aspects in dynamic simulation, performance prediction, and parameter optimization. The specific applications of intelligent systems in wheat sowing, facility agriculture control, and hilly terrain operations are introduced. With cases such as a sowing row spacing error controlled within ±4 mm, spray uniformity exceeding 96.3%, and a 94.2% qualification rate for mountainous area operations, it is evident that the proposed method system has optimized the equipment's environmental adaptability, operational stability, and decision-making algorithm efficiency.
Keywords：Digital TwinAgricultural MachineryDigital DesignAI OptimizationCollaborative Design
Publication Date：2025-09-25
Online Publishing Date：2025-11-07(First online date of this platform, not the publication date of the document)
Pages：4( 30-33 )