The coefficient of friction (CoF) obtained from tribological tests conducted on textured and porous surfaces was analysed using the machine learning Radial Basis Function (RBF) method. Non-textured and non-porous samples were taken as reference surfaces. Test parameters, such as entrainment velocity and slide-roll ratio (SRR), along with geometric characteristics of surface features (e.g. texture width and depth, coverage area, circularity, spatial distribution and directionality, among others), were selected as training dataset for the machine learning RBF model. The surface features were divided into designed patterns (dimples and grooves) manufactured by laser texturing, and randomised cavities (surface pores) resulted from the sintering process. The principal outcomes of this study are the effective use of the machine learning RBF method for tribological applications, as well as a critical discussion on its feasibility for the experimental dataset selected and the preliminary results obtained. Main results show that the Hardy multiquadric radial basis function provided an overall correlation coefficient of 0.934 for 35 poles. The application of the suggested machine learning technique and methodology can be extended to other experimental results available in the literature to train more robust models for predicting tribological performances of textured and structured surfaces.

使用机器学习径向基函数 (RBF) 方法分析从在纹理和多孔表面上进行的摩擦学测试中获得的摩擦系数 (CoF)。取无纹理和无孔样品作为参考表面。选择测试参数，例如夹带速度和滑滚比 (SRR)，以及表面特征的几何特征（例如纹理宽度和深度、覆盖区域、圆形度、空间分布和方向性等）作为训练数据集机器学习 RBF 模型。表面特征分为通过激光纹理制造的设计图案（凹坑和凹槽），以及由烧结过程产生的随机空腔（表面孔隙）。本研究的主要成果是机器学习 RBF 方法在摩擦学应用中的有效使用，以及对其选择的实验数据集的可行性和获得的初步结果的批判性讨论。主要结果表明，Hardy 多二次径向基函数为 35 个极点提供了 0.934 的整体相关系数。建议的机器学习技术和方法的应用可以扩展到文献中可用的其他实验结果，以训练更稳健的模型来预测纹理和结构化表面的摩擦学性能。主要结果表明，Hardy 多二次径向基函数为 35 个极点提供了 0.934 的整体相关系数。建议的机器学习技术和方法的应用可以扩展到文献中可用的其他实验结果，以训练更稳健的模型来预测纹理和结构化表面的摩擦学性能。主要结果表明，Hardy 多二次径向基函数为 35 个极点提供了 0.934 的整体相关系数。建议的机器学习技术和方法的应用可以扩展到文献中可用的其他实验结果，以训练更稳健的模型来预测纹理和结构化表面的摩擦学性能。