The modelling of machining processes by means of machine-learning algorithms is still based on principles that are especially adapted to mechanical approaches, in which very few inputs are varied with little repetition of experimental conditions. These principles might not be ideal to achieve accurate machine-learning models and they are certainly not aligned with the practicalities of industrial machining in factories. In this research the effect of a new strategy to improve machine-learning model accuracy is studied: experimental repetition. Tool-life prediction in the face-turning operations of AISI 1045 steel discs, depending on different cooling systems and tool geometries, is selected as a case study. Both the side rake and the relief angles of HSS tools are optimized using the Brandsma facing test under dry, MQL, and flooding conditions. Different machine-learning algorithms, such as regression trees, kNNs, artificial neural networks, and ensembles (bagging and Random Forest) are tested. On the one hand, the results of the study showed that artificial neural networks of Radial Basis Functions presented the highest model accuracy (11.4 mm RMSE), but required a very sensitive and complex tuning process. On the other hand, they demonstrated that ensembles, especially Random Forest, provided models with accuracy in the same range, but with no tuning procedure (12.8 mm RMSE). Secondly, the effect of an increased dataset size, by means of experimental repetition, is evaluated and compared with traditional experimental modelling that used average values. The results showed that some machine-learning techniques, including both ensemble types, significantly improved their accuracy with this strategy, by up to 23%. The results therefore suggested that the use of raw experimental data, rather than their averaged values, can achieve machine-learning models of higher accuracy for tool-wear processes.

借助机器学习算法对加工过程进行建模仍然基于特别适用于机械方法的原理，在这种方法中，输入的变化很小，而重复的实验条件却很少。这些原则对于实现精确的机器学习模型可能不是理想的，并且肯定与工厂中工业机械的实用性不符。在这项研究中，研究了一种提高机器学习模型准确性的新策略的效果：实验重复。根据不同的冷却系统和刀具几何形状，选择AISI 1045钢圆盘的端面车削操作中的刀具寿命预测作为案例研究。使用在干燥，MQL和溢流条件下的Brandsma端面测试，可以优化HSS工具的侧倾角和后角。测试了不同的机器学习算法，例如回归树，kNN，人工神经网络和合奏（装袋和随机森林）。一方面，研究结果表明，径向基函数的人工神经网络具有最高的模型精度（11.4 mm RMSE），但需要非常敏感和复杂的调整过程。另一方面，他们证明了集成，尤其是随机森林，提供了具有相同范围内精度的模型，但没有调整过程（12.8 mm RMSE）。其次，评估通过实验重复增加数据集大小的效果，并将其与使用平均值的传统实验模型进行比较。结果表明，某些机器学习技术，包括两种集成类型，通过此策略，其准确性显着提高了多达23％。因此，结果表明，使用原始实验数据而不是它们的平均值可以为工具磨损过程实现更高精度的机器学习模型。