View article

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 …