Abstract There is a growing requirement for models representing machine tool behavior to learn from operational data and adapt themselves in the use stage to various views defined by production managers, machining process designers, and operators. This study formulates these models as evolvable mapping from operational data to process definitions in views pertaining to multiple stakeholders at various temporal scales. A case study presents such a model for the behavior of a five-axis machining center defined as per multiple stakeholder views that predicts energy usage accordingly, while evaluating prediction accuracy of the model implemented using a supervised machine learning algorithm.

中文翻译：

---

应用于能源使用预测的机床行为的可进化模型

摘要 对代表机床行为的模型越来越需要从操作数据中学习，并在使用阶段适应生产经理、加工过程设计师和操作员定义的各种视图。本研究将这些模型制定为从操作数据到过程定义的可演化映射，这些映射与不同时间尺度的多个利益相关者有关。一个案例研究展示了一个五轴加工中心的行为模型，根据多个利益相关者的观点定义，相应地预测能源使用，同时评估使用监督机器学习算法实施的模型的预测准确性。