| CNC machining tools are the basic equipments in modern manufacturing cells. CNC machining system which takes CNC machining tool as a main part consumes a considerable amount of energy when machining a workblank into component. Energy-saving manufacturing mode is spring up with the increasingly severe energy and environment situation. Researches have shown that process parameters not only affect surface quality, cost and efficiency in practical machining, they are also closely revelant to energy efficiency. Optimization of process parameters is regarded as one of important means of realizing energy saving for machine tooling system. Therefore, exploring the optimization methods to achieve high energy efficiency becomes an inevitable choice of China’s machinery manufacturing.With the support from key projects of the National High-Tech R&D Program of China(863 Program)(No. 2014AA041506) and National Natural Science Foundation of China(No. 51475059), the optimization methods for energy efficiency of CNC machining are studied. They have important research significance that can provide theoretical reference for the transformation, upgrading and achieving high energy efficiency of China’s machinery manufacturing enterprises.Firstly, the characteristics of CNC machining system power consumption from both direct energy and indirect energy are studied. Then the fuctions of energy efficiency, inclusing specific energy consumption for direct energy and specific energy consumption considering indirect energy are established.Secondly, for the complex characteristics of energy in CNC machining process, an experitmental method for optimization of parameters is proposed. Taguchi method is used for design experiments, the response surface methodology is applied for regressions. Based on this, a multi-objective optimization model which takes parameters as optimization variables, practical machining requirements as constraints, energy efficiency and processing time as objectives is set up. The model is solved using particle swarm optimization algorithm.Thirdly, a self-adaptive optimization method of process parameters is put forwad based on mass machining data in workshop. Data preprocessing is implemented, and then the relations between energy efficiency and parameters under are explored based on fuzzy clustering algorithm and multivariate statistical analysis methods. The energy optimization knowledge is acquired based on fuzzy association rules algorithm. With summering the aforementioned knowledge, rule-base and fuzzy controller for energy efficiency are designed.Finally, in order to verify the feasibility and practicability of the proposed methods above, the parametric optimization methods of CNC machining based on data analysis in this paper are applied and analyzed through the actual case. These methods can provide theory support for reducing the energy consumption of CNC milling process and improving the utilization efficiency of CNC machine energy. |
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