Bhinge Raunak, Park Jinkyoo, Law Kincho H, Dornfeld David A, Helu Moneer, Rachuri Sudarsan
Laboratory for Manufacturing and Sustainability, University of California, Berkeley, CA, USA.
Engineering Informatics Group, Stanford University, Stanford, CA, USA.
J Manuf Sci Eng. 2017 Apr;139(4). doi: 10.1115/1.4034933. Epub 2016 Nov 9.
Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.
机床的能量预测能为制造企业带来诸多优势，从节能工艺规划到机床监测。过去已提出基于物理的能量预测模型来理解机床的能量使用模式。然而，机床及其运行环境中的不确定性使得可靠预测目标机床的能耗变得困难。利用收集大量上下文能耗数据的机会，本文讨论一种数据驱动的方法来开发机床的能量预测模型。首先，我们提出一种能高效且有效地收集和处理从机床及其传感器提取的数据的方法。然后，我们提出一个可用于预测加工通用零件时机床能耗的数据驱动模型。具体而言，我们使用高斯过程（GP）回归这一非参数机器学习技术来开发预测模型。接着，能量预测模型在多个工艺参数和操作上进行了推广。最后，我们将这个推广后的模型与一种评估不确定区间的方法相结合，以预测使用森精机NVD1500机床加工任何零件时所消耗的能量。此外，在工艺规划期间可使用相同模型来优化加工过程的能源效率。
