The paper addresses the problem of estimating the energy consumed by production resources in manufacturing so that alternative process designs can be compared in terms of energy expenditure. In particular, the proposed methodology focuses on Computer Numerical Controlled (CNC) machining centers. Classical approaches to energy modeling require high expertise and large development effort since, for example, data acquisition is resource-specific and must be repeated frequently to avoid obsolescence. An automated and flexible data-driven methodology is designed in this work. A data-driven method is employed to learn a hybrid and stochastic model of a CNC machining center’s energetic behavior. The learned model is used to provide offline energy consumption estimates of simulated part-programs before the actual execution of the cutting. Numerical results show the performance of the proposed method on a set of case studies. The methodology is also applied to a real industrial application, including data collected during machine production. Note to Practitioners—This article provides a flexible and autonomous data-driven approach to building models representing the energetic behavior of production resources, particularly CNC machining centers. The learned models can predict machine energy consumption while executing complex part-programs. The algorithm uses data that are commonly acquired by contemporary machine monitoring systems and does not require ad-hoc experimental tests for training. Specifically, it requires the spindle rotary speed signal, part load/unload signal, and spindle (or machine) power signal during the learning phase, whilst the estimation phase uses only the load/unload and spindle speed simulated signals.

中文翻译：

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通过自动机学习对加工中心进行数据驱动的能源建模


本文解决了估计制造过程中生产资源消耗的能源问题，以便可以在能源消耗方面比较替代工艺设计。特别是，所提出的方法侧重于计算机数控（CNC）加工中心。传统的能源建模方法需要较高的专业知识和大量的开发工作，因为数据采集是特定于资源的，必须经常重复以避免过时。这项工作设计了一种自动化且灵活的数据驱动方法。采用数据驱动方法来学习 CNC 加工中心能量行为的混合随机模型。学习模型用于在实际执行切割之前提供模拟零件程序的离线能耗估算。数值结果显示了所提出的方法在一组案例研究中的性能。该方法还应用于实际的工业应用，包括机器生产过程中收集的数据。从业者须知——本文提供了一种灵活且自主的数据驱动方法来构建代表生产资源（尤其是 CNC 加工中心）能量行为的模型。学习模型可以在执行复杂的零件程序时预测机器能耗。该算法使用当代机器监控系统通常获取的数据，不需要专门的实验测试来进行训练。具体来说，在学习阶段需要主轴转速信号、零件加载/卸载信号和主轴（或机器）功率信号，而估计阶段仅使用加载/卸载和主轴速度模拟信号。