轨道交通车辆关键结构件智能制造工艺优化研究

Research on Optimization of Intelligent Manufacturing Process for Key Structural Components of Rail Transit Vehicles

摘要

针对轨道交通车辆关键结构件生产中存在的节拍波动大、不良率偏高、设备利用率不足等问题，本文围绕转向架构架制造过程，构建了基于数字孪生、智能检测与柔性调度协同的工艺优化方法。通过建立焊接参数与变形量的映射模型，结合机器视觉与多源传感实现过程质量前移识别，并采用遗传算法对多工位资源进行动态配置，形成面向质量、效率与成本的综合优化路径。应用结果表明，优化后生产节拍由12.0 h/件缩短至7.5 h/件，不良率由3.5%降至1.2%，设备稼动率由65%提升至88%，一次交检合格率由93.1%提升至98.4%。研究表明，该方法能够有效提升关键结构件生产的稳定性与柔性化水平，为轨道交通装备智能制造提供参考。

Abstract

In response to the problems such as large cycle time fluctuations, high defect rate, and insufficient equipment utilization in the production of key components for rail transit vehicles, this paper focuses on the manufacturing process of the bogie frame and constructs a process optimization method based on digital twin, intelligent detection, and flexible scheduling. By establishing a mapping model between welding parameters and deformation amounts, combining machine vision and multi-source sensing to achieve pre-recognition of process quality, and using genetic algorithms to dynamically configure resources at multiple workstations, a comprehensive optimization path for quality, efficiency, and cost is formed. The application results show that the production cycle time has been shortened from 12.0 hours per piece to 7.5 hours per piece, the defect rate has decreased from 3.5% to 1.2%, the equipment utilization rate has increased from 65% to 88%, and the first-time inspection pass rate has increased from 93.1% to 98.4%. The study indicates that this method can effectively enhance the stability and flexibility of the production of key components, providing a reference for the intelligent manufacturing of rail transit equipment.

关键词

轨道交通车辆 / 关键结构件 / 数字孪生 / 智能检测 / 柔性调度

Key words

rail transit vehicles / key structural components / digital twin / intelligent detection / flexible scheduling

参考文献

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