Research On Power Consumption Model And Applications In Machining Process Considering Tool Wear

The power prediction of machine tool processing is the key basis for the refinement of energy consumption management and reducing energy consumption of manufacturing process.It is of great significance to the upgrading of the developing level of our manufacturing industry,the enhancement of the industrial core competitiveness and the improvement of the energy utilization efficiency of the manufacturing industry.In the existing research,different from the actual working situations,most of the power prediction model of machine tool processing is built under the ideal tool condition,which leads to large prediction error and low application value.Meanwhile,the prediction principle of the existing model is based on the statistical correlation between the material removal rate and the power consumption of the machine processing,and it lacks the influence factors and mechanism analysis on the theoretical level of the processing power.Nowadays,the theoretical modeling and accurate prediction of machine tooling power consumption under actual working conditions has become one of the important issues,related to the development of manufacturing equipment,process optimization and upgrading,and the improvement of intelligent on-line monitoring of tooling process.In this paper,the core research object is the power consumption prediction of NC machine tool and the influence of tool wear state on power consumption.The theoretical modeling problem of the power consumption of machine tool is deeply discussed.The analysis of the cutting force modeling of the worn tool is carried out to construct the prediction model of the power consumption of the worn tool,closer to the real working condition.At the same time,based on the backward derivation of the model,the tool wear condition monitoring and tool life prediction can be achieved by machine tool processing power consumption,then the effectively controls the machined surface quality.Specifically,the main research contents and innovative achievements are as follows:First,a theoretical model of power prediction for machine tool processing is set up.In view of the problem that the power consumption of machine tools cannot be accurately predicted in the process of cutting,according to the existing research,taking the theoretical mechanism of cutting tool as the base point,the correlation between the cutting parameters as the precondition,using the mathematical modeling method,based on the analysis of the source of the power consumption in different running states of the machine tool and the cutting motion characteristics of the cutter,this paper presents a theoretical prediction model of the power consumption based on air cutting power model of machine tool determined by the spindle speed and feed speed,and the cutting load power consumption of the machine tool process constructed by the instantaneous cutting force.Second,the calibration method of unknown influence factors of machine tool power consumption prediction theory model is proposed.In order to determine the unknown influence factors in the theoretical model,the experimental research on the power consumption of the machine tool in the new cutting tool is carried out and the statistical regression analysis is made on the power data set of the actual measurement.The complete model with the calibrated factor coefficient is used to verify and compare the general working condition of the machine tool processing power prediction model.It is proved that the modeling theory and the prediction model proposed in this paper are reasonable and feasible.Third,the prediction model of the cutting force of the wear tool is set up.Considering the sensitivity of cutting force to tool wear characteristics in actual processing,using the idea of the orthogonal cutting force ? the oblique cutting force ? the oblique angle wear force,the cutting force prediction problem mainly concerning tool surface wear characteristics is studied.On the basis of the analysis about the interaction force between the wear area and the working surface,introducing the force on the flank of worn tool impacted by the springback of machined surface,the prediction model of the cutting force of the cutting tool is established.The coefficients of the axial force and the cutting edge angle are found to be strongly correlated.According to the oblique cutting force prediction model,the prediction model of 3D cutting force for wear cutter is built.Under the experiment of the milling force of the worn tool,the rationality and accuracy of the milling force model is verified by the error analysis of the prediction value of the milling force and the measured value in the different wear state of the tool.Fourth,a prediction model for machining power consumption of wear cutting tools is established.In view of the influencing pattern of the wear on the cutting power in the whole life cycle of the tool,using influence function of flank wear on initial cutting power consumption,and the power consumption model of the machine tool at the initial state of the tool,the cutting power is fitted to different wear stages in the whole life cycle of the tool,The specific form and unknown coefficient of the influence function of tool wear characteristics are determined,then the machining power consumption model of the tool wear machine is established.Finally,the accuracy level of the power consumption prediction of the machine tool in the tool life cycle of the tool life cycle is verified by the experiment,and the validity of the prediction model is proved by the error analysis.Fifth,a prediction method for tool wear in milling is presented.Based on the two-way circulation effect of the power model of the tool cutting machine tool,a prediction method of tool wear is proposed,which can make use of the power consumption of the machine tool and predict the tool surface wear in the cutting process accurately.Through the experiment of the wear cutting of the vertical milling cutter with different cutting parameters,the prediction method is verified.The experimental results show that the tool wear state can be predicted more accurately by measuring the power consumption of the machine tool.To sum up,this study carried out the more precise prediction of the power consumption and tool wear of the machine tool.It provides an important theoretical basis and experimental method to effectively optimize the energy consumption of the machine tools,determine the tool life accurately,and then to control the quality of the machining surface.