The acceptance of the machined surfaces not only depends on roughness parameters but also in the flatness deviation (Δ_fl). Hence, before reaching the threshold of flatness deviation caused by the wear of the face mill, the tool inserts need to be changed to avoid the expected product rejection. As current CNC machines have the facility to track, in real-time, the main drive power, the present study utilizes this facility to predict the flatness deviation—with proper consideration to the amount of wear of cutting tool insert’s edge. The prediction of deviation from flatness is evaluated as a regression and a classification problem, while different machine-learning techniques like Multilayer Perceptrons, Radial Basis Functions Networks, Decision Trees and Random Forest ensembles have been examined. Finally, Random Forest ensembles combined with Synthetic Minority Over-sampling Technique (SMOTE) balancing technique showed the highest performance when the flatness levels are discretized taking into account industrial requirements. The SMOTE balancing technique resulted in a very useful strategy to avoid the strong limitations that small experiment datasets produce in the accuracy of machine-learning models.

加工表面的合格性不仅取决于粗糙度参数，还取决于平面度偏差（_Δfl）。因此，在达到由平面铣刀的磨损引起的平面度偏差的阈值之前，需要更换工具刀片以避免预期的产品报废。由于当前的CNC机床具有实时跟踪主驱动力的功能，因此本研究利用该功能来预测平面度偏差-适当考虑了切削刀具刀片边缘的磨损量。对平面度偏差的预测将作为回归和分类问题进行评估，同时还研究了不同的机器学习技术，例如多层感知器，径向基函数网络，决策树和随机森林集合。最后，当将平坦度离散化并考虑到工业需求时，随机森林乐团与综合少数族裔过采样技术（SMOTE）平衡技术相结合显示出最高的性能。SMOTE平衡技术产生了一种非常有用的策略，可以避免小型实验数据集在机器学习模型的准确性方面产生的强大限制。