Due to rising costs and the need for a more sustainable use of resources, there is an increasing focus on energy use in industrial production. As a result, energy-related data, for example from machine tools, is increasingly being collected. In addition to information for the energy evaluation of individual systems and processes, load profiles of machine tools offer further opportunities for process monitoring, such as tracking of production lots. As sensors for electrical power monitoring can be retrofitted without interfering with the process or the machine control unit, load profiles offer a cost-effective data source for data mining and machine learning applications. In order to support the generalisability of such applications, this paper describes the load profiles of machine tools and presents an overview on characteristics and the variety of load profiles of turning, grinding and milling machines in industrial use cases. Load profiles of 18 machine tools from machinery, automotive and aerospace production were analysed with regard to statistical characteristics during machining cycles. In particular, typical value ranges and statistical figures of load profiles and the influence of the sampling rate on the time series are presented.
Science Journal 