基于探伤数据统计过程控制的动车组转向架零部件质量改进方法研究

Quality Improvement Method for EMU Bogie Components Based on Statistical Process Control of Nondestructive Testing Data

  • 摘要:
    目的 动车组转向架探伤数据因格式不一、数据统计不规范且与制造过程脱节,难以支撑转向架零部件的质量改进。为提高转向架产品质量及零部件造修过程可控性,有必要研究一种有效的探伤数据统计方法。
    方法 首先,依据部件属性及探伤方法,对探伤数据进行系统性分类与标准化整理。其次,针对“缺陷数量”与“不合格率”两类离散型数据,确立了以C控制图和P控制图为核心的分析框架,为构建质量改进模型奠定了数据与理论基础。在构建了质量改进模型后,提出了覆盖数据采集、模型监控、异常响应、整改验证全过程的闭环改进方法。该方法首先规范了数据采集与预处理流程,进而基于控制图模型建立了实时监控与异常响应机制,最终结合PDCA(计划—执行—检查—行动)循环原则,在实际生产场景中验证了所提质量改进模型的有效性。
    结果及结论 通过对转向架34个构架的缺陷数据进行C控制图分析,识别了因特定操作者技能不足导致的构架缺陷超限,操作者经培训后数据恢复正常。通过对牵引梁的不合格率进行P控制图分析,识别了因部件材质问题导致的不合格率超限,材质经优化后数据恢复正常。所提质量改进模型可为转向架造修过程的数字化、智能化产线建设提供数据分析的理论基础与实践案例。

     

    Abstract:
    Objective Due to inconsistent formats, nonstandard statistical practices, and disconnection from manufacturing processes, the nondestructive testing (NDT) data for EMU (electric multiple unit) bogies are insufficient to support the quality improvement of bogie components. To improve the quality of bogie products and enhance the controllability of component manufacturing and repair processes, it is necessary to investigate an effective statistical method for NDT data.
    Method First, NDT data are systematically classified and standardized according to component attributes and testing methods. Second, for 'defect count' and 'non-conformity rate' two types of discrete data, an analytical framework centered on C control charts and P control charts is established, providing the data and theoretical foundation for constructing a quality improvement model. After the establishment of the model, a closed-loop improvement method covering the entire process of data collection, model monitoring, abnormal response, and corrective action verification is proposed. This method first standardizes data collection and preprocessing procedures, then establishes a real-time monitoring and abnormal response mechanism based on control chart models, and finally, in accordance with the PDCA (plan-do-study-act) cycle principle, verifies the effectiveness of the proposed quality improvement model in actual production scenarios.
    Result & Conclusion  Through C control chart analysis of the defect data from 34 bogie frames, out-of-control frame defects caused by insufficient skills of the specific operators are identified, and the operators restored data to normal after training. Through P control chart analysis of the non-conformity rate of traction beams, out-of-control non-conformity rates caused by material issues are identified; after material optimization, the data returned to normal. The proposed quality improvement model can provide a theoretical basis and practical case studies in data analysis for the construction of digitalized and intelligent production lines in bogie manufacturing and repair processes.

     

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