考虑数据不完备的数控铣削能耗预测方法

doi:10.3901/JME.2025.19.341

机械工程学报 ›› 2025, Vol. 61 ›› Issue (19): 341-351.doi: 10.3901/JME.2025.19.341

• 制造工艺和装备 • 上一篇

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考虑数据不完备的数控铣削能耗预测方法

赵继烜^1,2, 李聪波^1,2, 吴畏^1,2, 张友^1,2, 敬培锋^1,2

1. 重庆大学高端装备机械传动全国重点实验室重庆 400044;
2. 重庆大学机械与运载工程学院重庆 400044

收稿日期:2024-10-18 修回日期:2025-03-20 发布日期:2025-11-24
作者简介:赵继烜，男，1998年出生。主要研究方向为绿色制造、智能制造。E-mail：trronxuan@163.com
李聪波(通信作者)，男，1981年出生，博士，教授，博士研究生导师，主要研究方向为绿色制造、智能制造、制造系统能效。E-mail：congboli@cqu.edu.cn
基金资助:
国家自然科学基金(92367107)、中央高校基本科研业务费专项资金(2023CDJYXTD-003)和高端装备机械传动全国重点实验室自主研究课题重点(SKLMT-ZZKT-2024Z06)资助项目。

Numerical Control Milling Energy Consumption Prediction Method Considering Incomplete Data

ZHAO Jixuan^1,2, LI Congbo^1,2, WU Wei^1,2, ZHNAG You^1,2, JING Peifeng^1,2

1. State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400044;
2. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044

Received:2024-10-18 Revised:2025-03-20 Published:2025-11-24

摘要/Abstract

摘要： 针对数控铣削过程中由于设备故障及操作不当等原因造成采集的数据不完整，从而导致数控铣削能耗预测精度较低的问题。通过分析其能耗特性与数据缺失情况，提出了一种考虑数据不完备的数控铣削能耗预测方法。首先，基于去噪自编码期望最大多重插补(EMMIDA)对缺失值进行估计，生成新的完整数据；其次，利用FEDformer算法构建能耗预测模型，实现数控铣削加工能耗预测。最后，在不同缺失率下，将所提方法与其他常用数据填补方法及预测算法进行对比。实验结果表明，即使丢失的数据率增加到30%，EMMIDA填补方法的MAE相比多重插补(MICE)、生成对抗插补网络(GAIN)、K近邻算法(KNN)分别降低了20.3%、25.9%、44.6%，RMSE降低了17.8%、36.5%、42.1%；FEDformer能耗预测模型的MAE、RMSE较Transformer降低了4.36%、5.74%，R²提高了7.73%，且均优于Informer、LTSM和CNN，证明了该方法能有效降低数控铣削数据缺失带来的能耗预测误差。

关键词: 数控铣削, 能耗预测, 数据缺失, EMMIDA插补, FEDformer

Abstract: In the process of CNC milling, the collected data is incomplete due to equipment failure and improper operation, which leads to the problem of low accuracy of CNC milling energy consumption prediction. By analyzing the energy consumption characteristics of CNC milling and the circumstances associated with missing data, a prediction method of CNC milling energy consumption considering data incompleteness is proposed. Firstly, the missing values are estimated based on the denoising auto-encoder expectation maximum multiple interpolation (EMMIDA) to generate new complete data. Secondly, The FEDformer algorithm is used to construct the energy consumption prediction model, and the energy consumption prediction of CNC milling is realized. Finally, the proposed method is compared with other common data filling method and prediction algorithms at different miss rates. Experimental results show that even if the miss rate increases to 30%, the MAE of the EMMIDA filling method is reduced by 20.3%, 25.9% and 44.6% respectively compared with multiple imputation by chained equations (MICE), generative adversarial imputation networks (GAIN), and K-nearest neighbors (KNN), and the RMSE is reduced by 17.8%, 36.5% and 42.1% respectively compared with the Transformer. The MAE and RMSE of the FEDformer energy consumption prediction model are reduced by 4.36% and 5.74% compared with the Transformer, and the R² is increased by 7.73%, which are better than Informer, LTSM and CNN, the proposed method can effectively reduce the prediction error caused by the missing energy consumption data of CNC milling.

Key words: CNC milling, energy consumption prediction, missing data, EMMIDA imputation, FEDformer

中图分类号:

TH547

赵继烜, 李聪波, 吴畏, 张友, 敬培锋. 考虑数据不完备的数控铣削能耗预测方法[J]. 机械工程学报, 2025, 61(19): 341-351.

ZHAO Jixuan, LI Congbo, WU Wei, ZHNAG You, JING Peifeng. Numerical Control Milling Energy Consumption Prediction Method Considering Incomplete Data[J]. Journal of Mechanical Engineering, 2025, 61(19): 341-351.

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考虑数据不完备的数控铣削能耗预测方法

Numerical Control Milling Energy Consumption Prediction Method Considering Incomplete Data

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