The present research work was concerned with the development of an improved predictive model for energy estimation in the machining process. The need for a comprehensive predictive model which can account for generic cutting conditions together with all machine tool related factors was felt and has been attempted here. The proposed model was able to consider all influencing factors such as machine tool specific factors, axes configurations, acceleration effects of feed drives, and machine tool accessories. This component of energy from the machine tool was combined with the cutting energy estimated from cutting forces. This experimentally validated model can estimate energy consumption for any generic case directly from computer numerical control (CNC) programs or process plans. The newly developed model is used to study various machining situations to demonstrate its effectiveness.
Continuous need to increase productivity and reliability in machining has led to high-performance machines that are often characterized by high energy demands. As a result, energy minimization is identified as one of the key goals in machining. With the availability of improved predictive models for energy estimation in machining, energy-conscious process planning for machining is now possible. The present work focuses on the assessment of process plans of machined parts from energy consumption point of view. An experimentally validated model for energy estimation is first presented. Using this model two important process planning variables on energy consumption in machining has been studied. Firstly selection of tool paths including curvilinear tool paths has been considered from energy consumption point of view. Secondly, strategies for the selection of cutting parameters for roughing, semi-finishing and finishing from energy consumption perspective are discussed.
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