Characteristics And Methodology For Predicting Energy Efficiency Of Mechanical Machining System

The mechanical machining systems are numerous and are used in a wide range of applications in industry, the total amount of energy consumption by machining systems is extremely high, and the environmental problem caused by the energy consumption is more and more serious. In addition, researches demonstrated that the mechanical machining systems usually exhibit very low energy efficiency and they have great potential for energy savings and environmental emission reduction. For the reasons mentioned above, many universities and organizations all over the world have done a lot of researches on the energy efficiency of mechanical machining systems. The research on how to promote energy efficiency of mechanical machining systems is an important part of the study of green manufacturing, it is in line with the requirements of sustainable development, and the research is of great significance. This paper does some researches on the characteristics and methodology for predicting the two main energy efficiency indicators, the main contents are as following.In this paper, the power models of each stage in manufacturing process which can be used to study the predictable features of power consumption in each manufacturing process on the mechanism of energy consumption are presented based on the analysis of the energy consumption period feature and the energy consumption components of each processing stage, and the idea for predicting the energy consumption of each stage are provided. Integrating the predictable features of each stage of manufacturing process, the predictable feature and the necessary requirement for predicting the energy efficiency of mechanical machining systems are studied.The specific energy consumption characteristics of mechanical machining systems are analyzed and the dynamic characteristics of material-cutting power and influence o f processing conditions(machine tools, material of workpiece, cutting parameters et al.) on the material-cutting energy are both taken into consideration, on the basis of the analysis, an integrated method for predicting the specific energy consumption is established which includes the integrated model and the methods for predicting the each component are presented.The energy usage ratio prediction model is presented based on the definition of energy usage ratio and the characteristics analysis of energy consumption in each stage of manufacturing process, then the prediction methods for each stage of manufacturing process are presented, so that the method for predicting energy usage ratio of the whole manufacturing process is presented. After that, this paper takes the application of the energy usage ratio in energy-saving decision making of cutting parameters as an example to validate the prediction method has broad application prospect.Besides, the method for predicting loading loss energy consumption which is the key elements of energy efficiency of mechanical machining system is studied. To resolve the dilemmas of the existing methods which need to conduct a large amount of experiments and a comprehensive on- line measurement to obtain the power parameters, this paper presents a mapping method for predicting loading loss energy consumption. The method needs to construct a standard machining circumstance firstly, then mapping the standard machining circumstance to the target machine tool and conduct the standard experiments to obtain loading loss coefficient of target machine tools, so that the additional load loss energy consumption of the target machine tool under any machining circumstance can be predicted. There is no need to install dynamometers to measure the cutting force any more by using the mapping method to acquiring the loading loss coefficients of target machine tools, so that the mapping method can simplify the experiment processes to a great degree, in addition, the method and without limit of dimension and structure of machine tools.The case studies of each section indicate that the models and methods mentioned above are all practical and have relatively high prediction precision.Finally, the energy efficiency prediction supporting system is developed, which includes the design of framework of the system, function modules and the development of function modules.The methods mentioned above are capable of providing method support for promoting energy efficiency, establishing an energy consumpt ion quota, evaluating energy efficiency, energy-saving optimization of process parameters and energy-saving scheduling, and have broad application prospect.