A recent systematic experimental characterisation of technological thin films, based on elaborated design of experiments as well as probe calibration and correction procedures, allowed for the first time the determination of nanoscale friction under the concurrent influence of several process parameters, comprising normal forces, sliding velocities, and temperature, thus providing an indication of the intricate correlations induced by their interactions and mutual effects. This created the preconditions to undertake in this work an effort to model friction in the nanometric domain with the goal of overcoming the limitations of currently available models in ascertaining the effects of the physicochemical processes and phenomena involved in nanoscale contacts. Due to the stochastic nature of nanoscale friction and the relatively sparse available experimental data, meta-modelling tools fail, however, at predicting the factual behaviour. Based on the acquired experimental data, data mining, incorporating various state-of-the-art machine learning (ML) numerical regression algorithms, is therefore used. The results of the numerical analyses are assessed on an unseen test dataset via a comparative statistical validation. It is therefore shown that the black box ML methods provide effective predictions of the studied correlations with rather good accuracy levels, but the intrinsic nature of such algorithms prevents their usage in most practical applications. Genetic programming-based artificial intelligence (AI) methods are consequently finally used. Despite the marked complexity of the analysed phenomena and the inherent dispersion of the measurements, the developed AI-based symbolic regression models allow attaining an excellent predictive performance with the respective prediction accuracy, depending on the sample type, between 72% and 91%, allowing also to attain an extremely simple functional description of the multidimensional dependence of nanoscale friction on the studied variable process parameters. An effective tool for nanoscale friction prediction, adaptive control purposes, and further scientific and technological nanotribological analyses is thus obtained.

最近对技术薄膜的系统实验表征，基于精心设计的实验设计以及探针校准和校正程序，首次允许在包括法向力、滑动速度在内的几个工艺参数的同时影响下确定纳米级摩擦和温度，从而提供了由它们的相互作用和相互影响引起的复杂相关性的指示。这为在这项工作中对纳米域中的摩擦进行建模创造了前提条件，目的是克服当前可用模型在确定纳米级接触中涉及的物理化学过程和现象的影响方面的局限性。然而，由于纳米级摩擦的随机性和相对稀疏的可用实验数据，元建模工具无法预测实际行为。因此，基于获得的实验数据，结合了各种最先进的机器学习 (ML) 数值回归算法的数据挖掘被使用。数值分析的结果通过比较统计验证在一个看不见的测试数据集上进行评估。因此，表明黑盒 ML 方法以相当好的准确度水平提供了对研究相关性的有效预测，但此类算法的内在性质阻止了它们在大多数实际应用中的使用。因此，最终使用了基于遗传编程的人工智能 (AI) 方法。尽管所分析的现象非常复杂且测量结果具有固有的分散性，但开发的基于 AI 的符号回归模型仍可实现出色的预测性能，并且根据样本类型，在 72% 到 91% 之间的相应预测准确度允许还获得了纳米级摩擦对研究的可变过程参数的多维依赖性的极其简单的功能描述。从而获得了用于纳米级摩擦预测、自适应控制目的和进一步科学和技术纳米摩擦学分析的有效工具。在 72% 和 91% 之间，还允许获得纳米级摩擦对研究的可变工艺参数的多维依赖性的极其简单的功能描述。从而获得了用于纳米级摩擦预测、自适应控制目的和进一步科学和技术纳米摩擦学分析的有效工具。在 72% 和 91% 之间，还允许获得纳米级摩擦对研究的可变工艺参数的多维依赖性的极其简单的功能描述。从而获得了用于纳米级摩擦预测、自适应控制目的和进一步科学和技术纳米摩擦学分析的有效工具。