激光诱导击穿光谱技术对火车车轮钢中成分的原位统计分布分析表征

Characterization of Original Position Statistical Distribution of Composition in Train Wheel Steel by Laser-Induced Breakdown Spectrum

随着铁路规模的迅速扩大,对列车运行的可靠性及耐久性的要求也越来越高,车轮作为铁路机车车辆行走系统的核心部件,它与轨道之间的摩擦既要保证安全又要提升速度,车轮材料的性能直接影响车轮对磨损和滚动接触疲劳损伤的敏感性,其服役性能也受到高度关注。研究表明,车轮钢材料的成分及分布状态可对其组织性能产生显著影响,因此,运用激光诱导击穿光谱分析技术结合原位统计分布分析方法,通过其可多元素同步快速分析的高效性、较好的空间分辨能力、较大区域内的扫描分析能力等技术优势,结合统计分布分析方法,实现对车轮钢材料成分及其分布状态的快速表征。选取垂直于车轮轮辋外侧面作为分析面,对其进行低倍检测观察到在远离踏面的区域存在明显的粗大树枝晶结构,其组织结构存在不均匀性,并以此作为特征分析区域进行取样,采用320目的氧化铝砂纸进行表面处理,利用LIBSOPA系统进行成分分布分析。首先,在不同剥蚀条件下对各元素特征谱线的光谱信号强度及其稳定性进行比对分析,优化选定了20个预剥蚀10个剥蚀作为实验条件;其次,采用内标法建立了火车车轮钢中Si,Mn,P,S,Cr,Ni,Mo,Cu和V等九个元素的定量分析方法并对车轮钢样品中的元素含量进行测定,其定量结果与直读光谱分析的结果具有较好的一致性;最后对样品进行了区域扫描并运用统计偏析度对各元素的成分分布状态进行了统计表征,成分分布分区统计结果显示所有元素靠近踏面区域的统计偏析度均小于远离踏面区的统计偏析度,结合成分二维分布图可知,测试样品远离踏面区域的成分分布不均匀,其结果与低倍检验方法观察到的结果对应性较好。运用LIBSOPA技术实现了对火车车轮钢材料中多元素的成分分布表征,为快速判定车轮钢材料成分及分布状态提供了全新的思路和表征手段。

With the rapid expansion of the railway scale,the requirements for the reliability and durability of train operation are getting higher and higher.As the core component of the railway vehicle system,the friction between the wheel and the track must ensure safety and increase the speed.The performance of the wheel material directly affects the sensitivity of the wheel to wear and rolling contact fatigue damage,and its service performance is also highly concerned.Studies have shown that the composition and distribution of wheel steel materials can significantly impact the performance of its microstructure.Therefore,this paper aims to use the laser-induced breakdown spectroscopy technology to quickly analyze the high efficiency of multi-element,better spatial resolution,scanning analysis capabilities in a larger area and other technical advantages,combined with statistical distribution analysis method,to achieve rapid characterization of the composition and distribution of wheel steel materials.In this paper,the vertical surface of the wheel rim was selected as the analysis surface.The low time’s test showed that there were obvious thick dendrite structures in thearea away from the tread surface,and the organization structure had unevenness,and use this as a feature analysis area for sampling.320 mesh alumina sandpaper was used for surface treatment,and the LIBSOPA system was used for component distribution analysis.First,under different ablation conditions,the spectral signal intensity and stability of each element’s characteristic spectral line were compared and analyzed,and 20 pre-ablation and 10 ablations were optimized as experimental conditions;second,using established the standard internal method to characterize the quantitative results of nine elements such as Si,Mn,P,S,Cr,Ni,Mo,Cu,V in wheel steel.The quantitative results and the results of direct-reading spectrum analysis have good consistency;In the end,the sample was scanned regionally,and the statistical distribution of each element’s composition distribution was statistically characterized.The statistical results of the composition distribution partition showed that the statistical segregation degree of all elements near the tread area was less than that away from the tread area.Based on the statistical segregation degree and the two-dimensional distribution map of the components,it can be seen that the distribution of the components of the test sample away from the tread area is uneven,and the results correspond well with the results observed by the low-times test method.In this paper,the LIBSOPA technology is used to realize the composition distribution characterization of multi-element in the train wheel steel material,which provides a new idea and characterization method for quickly determining the composition and distribution state of the wheel steel material.