Prediction And Optimization Method Of Energy Efficiency Of Discrete Manufacturing System

Discrete manufacturing system is an important basis for production of mechanical products and parts. Machinery and equipment, as the main body of the discrete manufacturing system, whose quantity ranks first in our country, have consumed a great deal of energy. Meanwhile, the energy utilization rate of machinery and equipment is very low, less than 30% in average, which implies that there exists great potential in energy saving and the amount of carbon emissions caused by the consumption of energy consumption is amazing. Therefore, aiming at energy saving and optimization of discrete manufacturing system of low carbon manufacturing optimization research in view of the energy supplying shortage and rising prices of domestic and foreign environment, which is of great significance. For above mentioned reasons, the prediction of energy efficiency and energy saving optimization method of discrete manufacturing system are investigated. The major contributions are as follows:(1) Energy consumption information collection network is built. The energy flow of the three energy consumption levels(equipment layer, task layer and auxiliary production layer) of discrete manufacturing system is analyzed in detail and energy consumption model of each levels is establish as well as determining energy consumption information collection scheme of each levels. The paper collects energy consumption information from three aspects. Firstly, Obtain the total energy consumption of the machine tool with a smart meter; Secondly, write the code to acquire data concerning with energy consumption information of spindle and feed of machine tool under the OEM software environment which is provided by Siemens. Thirdly, use the handset scan electronic tags to obtain machining information. After the energy consumption information is collected, according to the equipment layout of the manufacturing plant, the corresponding hardware deployment scheme and the network transmission protocol are designed and the energy consumption information is stored in the background database to calculate the energy consumption and analyze the energy efficiency of machinery and equipment.(2) The difficulty problem of obtaining the energy efficiency of discrete manufacturing system equipment layer, a novel method is proposed based on the recursive method with discounted measurements. Take the equipment manufacturing industry as an example, First of all, the estimation model of the cutting power is given in view of the power balance equation of the machine tool main drive system and the additional load loss function; Further more, considering taking into account the additional load loss coefficients in model cannot be directly measured, the recursive method with discounted measurements is adopted to identify the additional load loss coefficients as well as estimating the cutting power. Afterwards, the energy efficiency of machine tool is calculated according to its definition. Experimental results show that the algorithm proposed in this paper can achieve higher identification accuracy than the traditional LS and the efficiency of machine tool that calculated by using this method is much closer to true value compared with the corresponding value obtained by using other methods.(3) Aiming at the inefficiency of production equipment of discrete manufacturing system, a energy efficiency optimization method of discrete manufacturing system is proposed. In the constraints of actual environment, the paper takes the cutting speed, feed speed and cutting depth as the optimization variables, the energy efficiency(energy consumption and processing time) as the optimization objective. A random walk multi-objective particle swarm optimization algorithm is proposed to solve the optimization model and the Analysis Hierarchy Process(AHP) decision method is adopted to select the optimal processing parameters from the Pareto front solution. In the practical machining environment, the effectiveness and superiority of the proposed algorithm in solving the optimization problem of the processing parameters of discrete manufacturing system are verified.