Energy efficiency has become crucial in the metal cutting industry. Machining power has therefore become an important metric because it directly affects the energy consumed during the operation of a machine tool. Attempts to predict machining power using machine learning have relied on the training datasets processed from actual machining data to derive the numerical relationship between process parameters and machining power. However, real fields hardly provide training datasets because of the difficulties in data collection; consequently, traditional learning approaches are ineffective in such data-scarce or -absent environment. This paper proposes a transfer learning approach for the predictive modeling of machining power. The proposed approach creates machining power prediction models by transferring the knowledge acquired from prior machining to the target machining context where machining power data are absent. The proposed approach performs domain adaptation by adding workpiece material properties to the original feature space for accommodating different machining power patterns dependent on the types of workpiece materials. A case study demonstrates that the training datasets obtained from the fabrication of steel and aluminum materials can be successfully used to create the power-predictive models that anticipate machining power for titanium material.

能源效率已成为金属切削行业的关键。因此，机加工能力已成为重要的指标，因为它直接影响机床操作过程中消耗的能量。使用机器学习来预测加工能力的尝试依赖于从实际加工数据中处理的训练数据集，以得出加工参数与加工能力之间的数值关系。但是，由于数据收集的困难，实际领域很难提供训练数据集。因此，传统的学习方法在这种缺乏数据或缺乏数据的环境中是无效的。本文提出了一种转移学习方法，用于加工能力的预测建模。所提出的方法通过将从先前的加工中获取的知识转移到缺少加工能力数据的目标加工环境中来创建加工能力预测模型。所提出的方法通过将工件材料属性添加到原始特征空间中来执行域自适应，以适应取决于工件材料类型的不同加工功率模式。案例研究表明，从钢和铝材料的制造中获得的训练数据集可成功用于创建预测钛材料加工能力的功率预测模型。所提出的方法通过将工件材料属性添加到原始特征空间中来执行域自适应，以适应取决于工件材料类型的不同加工功率模式。案例研究表明，从钢和铝材料的制造中获得的训练数据集可成功用于创建预测钛材料加工能力的功率预测模型。所提出的方法通过将工件材料属性添加到原始特征空间中来执行域自适应，以适应取决于工件材料类型的不同加工功率模式。案例研究表明，从钢和铝材料的制造中获得的训练数据集可成功用于创建预测钛材料加工能力的功率预测模型。