Energy Efficiency Intelligent Estimation Model Established for Production Machine Tools

• Main Authors: Jian-Wei Huang, 黃建瑋
• Other Authors: Tser-Son Wu
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/5y6s86

碩士 === 崑山科技大學 === 機械工程研究所 === 103 === International machine tool producer is toward green production the currently. The high-efficiency, low pollution, low energy consumption, energy recovery and reuse of resources are developed and machine tools are expected to be included in the energy consumption restrain. Therefore, it is needed to establish the energy efficiency of machine tool testing methods. In the study, machine tool work is electric motor idle racing and the cutting process load consumption and all kind of cutting parameters on machine tool energy consumption were analyzed. The model of machine tool energy consumption and cutting parameters was established based on BP neural network, and calculating process of empirical formula was simplified. This can be as a reference for the future to build a complete test lab. Research works were divided into three parts, first object is establishing the production machine tools cutting energy consumption characteristics model and cutting energy measurement analysis of functional module. The second object is to build a machine tool energy consumption characteristic equation with wisdom parameters calculation method and verify the accuracy. Experimental results showed that form direct energy consumption pattern analysis, test results for the three materials, the spindle energy consumption of cutting copper was accounted for 21.8%, aluminum was accounted for 19.6%, iron was accounted for 31.8%. The iron was harder than the copper and aluminum, therefore, when the cutting spindle consume more power. The average error between the neural network output and the measured value is 1.8% for the trained data set and 4.9% for the untrained data set. These two small errors show a good function mapping between spindle rotation velocity, feed rate, cutting depth and energy consumption is really obtain by the neural network.