基于机器学习的建筑电气施工电缆绞磨机设计优化研究

Research on Design Optimization of Cable Twisting Mill for Building Electrical Construction Based on Machine Learning

建筑电气施工电缆绞磨机是工程建设中的关键设备。它的设计性能直接影响施工效率和质量。然而,当前绞磨机在结构强度、传动效率、控制精度等方面仍存在不足,因此提出一种基于机器学习的绞磨机设计优化方法。通过有限元分析进行结构优化,采用机电一体化设计提高传动效率,引入深度学习算法实现自适应控制。仿真实验结果表明,优化后绞磨机的静动态性能显著提升,疲劳寿命大幅延长。

The cable twisting machine for electrical construction in building is a key equipment in engineering construction,and its design performance directly affects construction efficiency and quality.However,the current twisting machines still have shortcomings in structural strength,transmission efficiency,and control accuracy.This paper proposes a machine learning-based optimization method for twisting machine design,optimizing the structure through finite element analysis,improving transmission efficiency through mechatronic integration design,and introducing deep learning algorithms for adaptive control.The simulation results show that the static and dynamic performance of the optimized grinder is significantly improved,and the fatigue life is significantly prolonged.