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Study On Molding Process Optimization And Bonding Strength Of Metal Matrix Composite Bushing

Posted on:2020-08-19Degree:MasterType:Thesis
Country:ChinaCandidate:C YangFull Text:PDF
GTID:2381330596491664Subject:Mechanical engineering
Abstract/Summary:PDF Full Text Request
The metal matrix composite bushing is a composite material with high strength,self-lubricating and wear resistance,which solves the limitation of single metal material,can be applied to various working conditions,and has a wide application prospect.However,in practical applications,the bonding strength between the metal matrix and the plastic layer is relatively poor,resulting in a series of problems such as unstable performance of the metal matrix composite bushing and shortened service life.Therefore,in order to improve the bonding strength,the metal matrix composite bushings are formed by pressing and bending,and the forming process is optimized.The main research contents and conclusions of this paper are as follows:(1)Based on the theoretical study of the bond strength of metal matrix composites and plastic rheology,the influence of the microstructure of metal-based surface and chemical treatment on the bond strength was analyzed.The method of treating the surface of the metal base by high energy shot peening and a silane coupling agent was determined to enhance the bonding strength.The influence of the structure of the plastic layer blending system and the influence of molding temperature,pressure and time on the viscosity of the plastic was studied,and the range of the pressing forming process parameters which are favorable for the bonding strength was determined.(2)The optimization goals and process parameters are determined.The effect of cast steel shot diameter,shot peening pressure,shot peening time and plasticizing temperature on the bond strength of composites was studied by orthogonal test.The combination of process parameters with the best bonding strength was determined.A BP neural network model between process parameters and bonding strength is established to establish the nonlinear mapping relationship between process parameters and bonding strength.The function equation between the process parameters and the bonding strength is fitted by the numerical fitting,and the influence law of the interaction between the process parameters on the bonding strength is analyzed.The combination of process parameters with the highest bonding strength is predicted,and the optimization results are verified by experiments.(3)The properties of the experimental materials are analyzed and the molding process steps are determined.That is,the model of the metal substrate is selected first,and then the high energy shot peening and the intermediate bonding layer spraying are performed according to the orthogonal experimental scheme.The experimental materials were then pretreated,and the molds of press forming and bending forming were designed.Finally,the press forming and bending forming experiments were carried out.(4)The metal matrix composite performance testing equipment and test methods are introduced.It was determined that the tensile strength of the metal matrix composite was tested by the vertical stretching method to provide raw data for the orthogonal experiment.Finally,the metal matrix composite sample is formed under the optimal combination of process parameters,and the friction coefficient test is carried out under different loads and speeds.Research indicates: The predicted values of the BP neural network model are in good agreement with the experimental test values,and the degree of coincidence is high.It is proved that the model can predict the bond strength values under different process parameter combinations.The results of numerical fitting indicate that: When the diameter of the cast steel shot is 4.79mm-5.23 mm,the shot peening pressure is 0.47MPa-0.53 MPa,the shot peening time is 10.5min-11.5min,and the plasticizing temperature is 268°C-293°C,the bonding strength of the formed metal matrix composite sleeve is the best.
Keywords/Search Tags:Metal matrix composite, Bonding strength, Process optimization, BP neural network, Numerical fitting
PDF Full Text Request
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