Modeling And Multi-objective Optimization Of Machine Tool Energy Consumption Considering Tool Wear

Manufacturing industry is the main part of the national economy,but also the main source of energy consumption and environmental pollution.It is the focus and difficulty of energy conservation.Therefore,how to improve energy utilization,reduce energy consumption,and solve the problems faced by sustainable development are the major challenges we should face.In the manufacturing proc ess,the use of cutting tools takes up the main proportion.Tool will wear along with the machining process.The tool wear will reduce the efficiency of the machine tool,reduce machining accuracy,and increase energy consumption and production costs.Therefore,it is important to consider the influence of tool wear on energy consumption in the process of studying the energy consumption of machine tools.In this thesis,the energy consumption components of machine tools were analyzed,and the energy consumption of machine tools was analyzed according to the different processing conditions of machine tools.At the same time,the previous models of machine tool energy consumption are mostly studied from two angles,cutting force and processing parameters,ignoring the influence of tool state on the energy consumption of machine tools during machining process.This thesis analyzed the wear of cutting tools in the process of machine tool processing,and studied the influence of tool wear on the energy consumption of machine tools.In this thesis,the influence of machine tool parameters and tool wear on machine tool energy consumption was studied by cutting test.The design and analysis of the experiment were carried out by response surface methodology,and the cu tting test was designed to prove the accuracy of the model.Through the analysis of the experimental data and function model,the relationship between the machining parameters of the machine tool and the energy consumption of the cutting process was obtained.At the same time,designed the blank cutting test and establish the energy consumption model of the machine tool’s empty cutting process,and finally got a complete machine tool energy consumption model considering the tool wear.The surface roughness of workpiece is an important index to evaluate the quality of workpiece.Surface roughness is closely related to mechanical properties,wear resistance,fatigue strength,contact stiffness,vibration and noise,and has an important influence on service life and reliability of mechanical products.In this thesis,the influence of machine tool processing condition on the surface roughness of workpiece was studied.The functional model of workpiece surface roughness was established by experimental method.Based on the established model of machine tool power and surface roughness of workpiece,a tool condition monitoring method was established to monitor the real-time monitoring of cutting tool state.In the process of machining,a reasonable selection of cutting parameters can not only guarantee the quality of the machined surface,improve the machining efficiency of the machine tools,but also reduce energy consumption and realize energy conservation and emission reduction.Based on the model of workpiece surface roughness and machine energy consumption,this thesis analyzed the multi-objective optimization problem about machining parameters considering the machining efficiency,surface quality of the workpiece and the machine tool consumption.Optimized machining parameters by particle swarm algorithm,using the grey correlation analysis method to determine the target weight.Finally,the recommended values of machining parameters satisfying the processing requirements are given.