基于混合机器学习模型的数控轴能耗时间序列预测

IF 2.1 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Machines Pub Date : 2023-11-08 DOI:10.3390/machines11111015
Robin Ströbel, Yannik Probst, Samuel Deucker, Jürgen Fleischer
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引用次数: 0

摘要

计算机数控(CNC)机床轴的能量相关时间序列预测是向自主和智能生产转变的重要推动因素。特别是,需要对能源消耗进行精确预测,以确定产品对环境的影响及其生产的优化。为此,提出了一种基于待执行程序代码预测数控轴高频时间序列的新方法。该方法包括对输入的NC代码进行模拟预处理,以确定每个轴的加速度、速度和加工力。结合材料去除率,这些变量被输入到机器学习(ML)模型中,该模型可以提供特定于轴的高频时间序列预测。与常用方法相比,它可以在时域内对任意刀具路径或目标分辨率下的机床可变能耗进行预测。实验表明,该方法在具有鲁棒学习基础的情况下,具有较高的学习精度。对于X、Y和z轴,气切误差分别为0.2%、- 1.09%和0.09%,材料去除误差分别为0.15%、- 3.55%和0.08%。系统地识别进一步改进的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time Series Prediction for Energy Consumption of Computer Numerical Control Axes Using Hybrid Machine Learning Models
The prediction of energy-related time series for computer numerical control (CNC) machine tool axes is an essential enabler for the shift towards autonomous and intelligent production. In particular, a precise prediction of energy consumption is needed to determine the environmental impact of a product and the optimization of its production. For this purpose, a novel approach for predicting high-frequency time series of numerically controlled axes based on the program code to be executed is presented. The method involves simulative preprocessing of the input NC code to determine each axis’s acceleration, velocity, and process force. Combined with the material removal rate, these variables are input for a machine learning (ML) model that delivers axis-specific high-frequency time series predictions. Compared to common approaches, it is thus possible to make predictions for the variable energy consumption of machine tools for any tool path or target resolution in the time domain. Experiments show that this approach achieves a high precision when a robust learning data basis is available. For the X-, Y-, and Z-axis, errors of 0.2%, −1.09%, and 0.09% for aircut and of 0.15%, −3.55%, and 0.08% for material removal can be achieved. The potentials for further improvement are identified systematically.
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来源期刊
Machines
Machines Multiple-
CiteScore
3.00
自引率
26.90%
发文量
1012
审稿时长
11 weeks
期刊介绍: Machines (ISSN 2075-1702) is an international, peer-reviewed journal on machinery and engineering. It publishes research articles, reviews, short communications and letters. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. Full experimental and/or methodical details must be provided. There are, in addition, unique features of this journal: *manuscripts regarding research proposals and research ideas will be particularly welcomed *electronic files or software regarding the full details of the calculation and experimental procedure - if unable to be published in a normal way - can be deposited as supplementary material Subject Areas: applications of automation, systems and control engineering, electronic engineering, mechanical engineering, computer engineering, mechatronics, robotics, industrial design, human-machine-interfaces, mechanical systems, machines and related components, machine vision, history of technology and industrial revolution, turbo machinery, machine diagnostics and prognostics (condition monitoring), machine design.
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