目的 研究膝关节模拟实验条件下超高分子量聚乙烯（UHMWPE）磨损颗粒的形态及分形表征，对磨损颗粒进行分类特征分析，探讨分形维数与磨损状态的相关性。方法 采用膝关节模拟器以实现模拟膝关节磨损运动，股骨、衬垫分别选用医用锻造CoCrMo合金和UHMWPE。磨损颗粒提取依据标准ISO 17853进行，利用磨粒轮廓自动提取与分形识别系统对磨损颗粒进行分形分析，采用网格划分、聚类分析和遗传模拟退火算法3种模型对磨粒进行分类识别。结果 UHMWPE单体磨粒的分形特征明显，尺寸较大的条形磨屑过渡到尺寸较小的类球形磨粒时，雷达分形维数不断减小，球状磨粒的分形维数 D接近0。遗传模拟退火算法模型磨粒群体分类划分的分形维数内加权误差平方和最小，聚类特征明显。当磨损运行周期较低时，分形维数较大的条状、针叶状和纤维状磨粒占比较大，磨损以犁沟和剥落磨损为主；随磨损周期的延长，大分形维数磨粒占比下降，低分形维数的片状、块状和类球状磨粒占比上升，磨损向疲劳磨损和黏着磨损转变，磨损状态过渡到复合磨损期。当进入稳定磨损期后，各类形态磨粒的占比例变化不大。由于小尺寸磨粒的数量增加，群体分形维数有所减小。结论 以改进雷达图分形方法为基础的磨粒轮廓自动提取与分形识别系统，可用于人工关节磨损颗粒的形态轮廓提取、分形维数计算和参数统计，为人工关节磨粒的识别和诊断提供新的数字化分析工具。

Objective To study the morphology and fractal characterization of UHMWPE wear particles by simulation experiment on knee joint, analyze the classification characteristics of wear particles and discuss the correlation between fractal dimension and wear state. Methods The knee joint simulator was used to realize the knee joint wear motion. The forged CoCrMo alloy and UHMWPE were selected as artificial joint prosthesis materials. The wear particle extraction was based on ISO 17853. The automatic extraction and fractal identification system of wear particles were used to investigate fractal characteristics of wear particles. Three kinds of models were established to classify and recognize wear particles, by using the meshing method, cluster analysis and genetic simulated annealing algorithm, respectively. Results The fractal characteristics of UHMWPE single wear particles was very obvious. The fractal dimension calculated by radar fractal method decreased, during the transition from the larger size of strip debris to the smaller size of roundness debris, and the fractal dimension D of spherical debris was close to zero. The weighted sum of squared error values of fractal dimension for wear particle population classification was the smallest by the model of genetic simulated annealing algorithm, and the clustering feature was very obvious. When the wear cycles were low, the large fractal dimension of strip, needle and fibrous abrasive debris with larger fractal dimension had the largest proportion, and the main abrasion modes were the ploughing and spalling wears. With the extension of wear period, the proportion of wear particles with large fractal dimension decreased, and the proportion of flaky, blocky and near-spherical wear particles with low fractal dimension increased clearly. The wear mechanism changed to the fatigue and adhesive wear, and the wear state transited to the composite wear period. During the stable wear period, the proportion of all kinds of abrasive grains changed little. Due to the increase in the number of small particles, the fractal dimension decreased in stable wear state. Conclusions Based on the improved radar graph method, the fractal dimension of wear particles with different profiles could be obtained by automatic extraction and fractal identification system of wear particles. The research findings can be used in shape extraction, fractal dimension calculation and parameter statistics, as well as providing a new digital analysis tool for identification and diagnosis for wear particles of artificial prosthesis.

国家自然科学基金项目（51275514），江苏省前瞻性产学研研究项目（BY2015024-01）