Energy Consumption Prediction System of Mechanical Processes Based on Empirical Models and Computer-Aided Manufacturing

Abstract

Energy consumption prediction at the process planning stage is the basis of mechanical process optimization aiming at saving energy and reducing carbon emission. The accuracy and efficiency of the prediction method will be the most concerning issues. This paper presents an energy consumption prediction system of mechanical processes based on empirical models and computer-aided manufacturing (CAM). The system was developed based on analysis of energy-related data and data acquiring methods. The energy consumption sources of mechanical processes are divided into two parts: energy of auxiliary machine movements and intrinsic process movements. Considering data sources, there are two kinds of data acquiring methods: acquiring data from database or from CAM files. Process energy state is introduced to support calculation of energy consumption and presentation of calculation results. Example of the system was developed based on Microsoft SQL Server 2008 and ugs nx 7.0, and several examples of energy prediction of mechanical processes were also presented. The results demonstrate that the proposed system developing method is effective in predicting energy consumption of mechanical processes with high accuracy and high efficiency.