Prediction of coefficient of friction based on footwear outsole features
Prediction of coefficient of friction based on footwear outsole features
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DOI：
10.1016/j.apergo.2019.102963
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发表时间：
2020-01-01
影响因子：
3.2
通讯作者：
Beschorner, Kurt E.
中科院分区：
文献类型：
--
作者：
Iraqi, Arian;Vidic, Natasa S.;Beschorner, Kurt E.
鞋类的牵引力测试是昂贵的，这可能对某些用户评估鞋类造成障碍。本研究的目的是开发一种统计模型，预测边界润滑条件下的有效摩擦系数（ACOF）的基础上廉价的测量鞋外底功能。几何和材料硬度参数进行了测量，从58个鞋类设计标记为防滑。使用机器人摩擦测量装置来量化以芥花油作为污染物的ACOF。逐步回归方法用于开发基于外底参数和地板类型的模型来预测ACOF。使用k折交叉验证方法测试回归模型的预测能力。结果表明，87%的ACOF的变化解释了三个鞋外底参数（胎面表面积，鞋跟形状，硬度）和地板类型。这种方法可以为安全从业人员提供评估工具，以评估鞋类牵引力并提高工人的安全性。
Traction testing of footwear is expensive, which may create barriers for certain users to assess footwear. This study aimed to develop a statistical model that predicts available coefficient of friction (ACOF) under boundary lubrication conditions based on inexpensive measurements of footwear outsole features. Geometric and material hardness parameters were measured from fifty-eight footwear designs labeled as slip-resistant. A robotic friction measurement device was used to quantify ACOF with canola oil as the contaminant. Stepwise regression methods were used to develop models based on the outsole parameters and floor type to predict ACOF. The predictive ability of the regression models was tested using the k-fold cross-validation method. Results indicated that 87% of ACOF variation was explained by three shoe outsole parameters (tread surface area, heel shape, hardness) and floor type. This approach may provide an assessment tool for safety practitioners to assess footwear traction and improve workers' safety.