In subtractive manufacturing, differences in machinability among batches of the same material can be observed. Ignoring these deviations can potentially reduce product quality and increase manufacturing costs. To consider the influence of the material batch in process optimization models, the batch needs to be efficiently identified. Thus, a smart service is proposed for in-situ material batch identification. This service is driven by a supervised machine learning model, which analyzes the signals of the machine’s control, especially torque data, for batch classification. The proposed approach is validated by cutting experiments with five different batches of the same specified material at various cutting conditions. Using this data, multiple classification models are trained and optimized. It is shown that the investigated batches can be correctly identified with close to 90% prediction accuracy using machine learning. Out of all the investigated algorithms, the best results are achieved using a Support Vector Machine with 89.0% prediction accuracy for individual batches and 98.9% while combining batches of similar machinability.

在减法制造中，可以观察到相同材料的批次之间的可加工性差异。忽略这些偏差可能会降低产品质量并增加制造成本。要考虑物料批次在过程优化模型中的影响，需要有效地识别物料批次。因此，提出了一种用于现场物料批次识别的智能服务。该服务由有监督的机器学习模型驱动，该模型分析机器控制信号，尤其是扭矩数据，以进行批次分类。通过在不同的切割条件下使用五个不同批次的相同指定材料进行切割实验，验证了该方法的有效性。使用此数据，可以训练和优化多个分类模型。结果表明，使用机器学习可以以接近90％的预测准确度正确识别所调查的批次。在所有研究的算法中，使用支持向量机可获得最佳结果，单个批次的预测准确度为89.0％，而可加工性相似的批次则组合为98.9％。