Energy consumption prediction of a CNC machining process is important for energy efficiency optimization strategies. To improve the generalization abilities, more and more parameters are acquired for energy prediction modeling. While the data collected from workshops may be incomplete because of misoperation, unstable network connections, and frequent transfers, etc. This work proposes a framework for energy modeling based on incomplete data to address this issue. First, some necessary preliminary operations are used for incomplete data sets. Then, missing values are estimated to generate a new complete data set based on generative adversarial imputation nets (GAIN). Next, the gene expression programming (GEP) algorithm is utilized to train the energy model based on the generated data sets. Finally, we test the predictive accuracy of the obtained model. Computational experiments are designed to investigate the performance of the proposed framework with different rates of missing data. Experimental results demonstrate that even when the missing data rate increases to 30%, the proposed framework can still make efficient predictions, with the corresponding RMSE and MAE 0.903 kJ and 0.739 kJ, respectively.

中文翻译：

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不完整数据的数控加工过程能耗预测


CNC 加工过程的能耗预测对于能源效率优化策略非常重要。为了提高泛化能力，能量预测建模需要获取越来越多的参数。而研讨会上收集的数据可能会因为误操作、网络连接不稳定、频繁传输等原因而不完整。本文提出了一种基于不完整数据的能源建模框架来解决这一问题。首先，对不完整的数据集进行一些必要的初步操作。然后，基于生成对抗性插补网络（GAIN）估计缺失值以生成新的完整数据集。接下来，利用基因表达编程（GEP）算法根据生成的数据集训练能量模型。最后，我们测试所获得模型的预测准确性。计算实验旨在研究所提出的框架在不同丢失数据率下的性能。实验结果表明，即使当缺失数据率增加到30%时，所提出的框架仍然可以做出有效的预测，相应的RMSE和MAE分别为0.903 kJ和0.739 kJ。