The ability to predict performance of manufacturing equipment during early stages of process planning is vital for improving efficiency of manufacturing processes. In the metal cutting industry, measurement of machining performance is usually carried out by collecting machine-monitoring data that record the machine tool's actions (e.g. coordinates of axis location and power consumption). Understanding the impacts of process planning decisions is central to the enhancement of the machining performance. However, current methodologies lack the necessary models and tools to predict impacts of process planning decisions on the machining performance. This paper presents the development of a virtual machining model (called STEP2M model) that generates machine-monitoring data from process planning data. The STEP2M model builds upon a physical model-based analysis for the sources of energy on a machine tool, and adopts STEP-NC and MTConnect standardised interfaces to represent process planning and machine-monitoring data. We have developed a prototype system for 2-axis turning operation and validated the system by conducting an experiment using a Computer Numerical Control lathe. The virtual machining model presented in this paper enables process planners to analyse machining performance through virtual measurement and to perform interoperable data communication through standardised interfaces.
IntroductionIn manufacturing, automated measurement of process and resource performance is becoming increasingly important to improve efficiency of manufacturing processes and equipment (Muchiri and Pintelon 2008). In machining processes, the applied use of metrics allows observation and quantification of outcomes of a machine tool's actions. Primarily, the measurement of these metrics requires the collection and analysis of machine-monitoring data that record the events and movements of machine tool components in relation to planned machining operations. On the other hand, process planning decisions greatly influence efficiency of machining operations (Xu, Wang, and Newman 2011). Proper determination of process sequence and process parameter selection can occur during the process planning stage by measuring and analysing the machine-monitoring data to make machining performance better.Energy-conscious process planning enables to perform machining processes with better energy efficiency, thereby significantly reducing the industrial consumption of energy (Newman et al. 2012). For this reason, previous works have developed predictive and optimisation models to improve energy-related metrics such as energy consumption and power consumption. Power consumption is a key dominant metric in the analysis of energy efficiency, and consists of the basic power consumed by a machine tool's actions and the cutting power needed to remove a workpiece (Kara and Li 2011;Diaz et al. 2013).Mativenga and Rajemi (2011) proposed a methodology for selecting process parameters to minimise energy footprint using a tool life equation. Bhushan (2013) present...