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Experimental Study On Energy Consumption And Surface Quality Of Ultrafine Cemented Carbide During Grinding

Posted on:2021-02-12Degree:MasterType:Thesis
Country:ChinaCandidate:X TianFull Text:PDF
GTID:2381330605458498Subject:Mechanical engineering
Abstract/Summary:PDF Full Text Request
The resource consumption of manufacturing industry is huge,and the energy utilization rate of machine tools is low.Due to its higher strength,hardness,fracture toughness,bending strength and excellent wear resistance compared with ordinary cemented carbide,ultrafine cemented carbide has a wider application field.Cemented carbide is a difficult-to-machine material,and its main processing method is made by grinding wheel.But,grinding is a process with high energy consumption and high emission.In this paper,the ultrafine cemented carbide grinding process is taken as the research object,and the variation rules of grinding parameters on energy consumption and grinding surface quality are studied.According to the trade-off between the surface quality of grinding and the energy consumption of grinding,the multi-index orthogonal optimization of residual stress,surface roughness and grinding specific energy is performed to provide a reference for high-quality low-energy grinding of ultrafine cemented carbide.The main work of this paper is as follows:1.Study on energy consumption in grinding process.The composition of energy consumption in grinding process and the proportion of power in each working state of grinding are studied and analyzed.Then,the effects of grinding parameters and Co content on the unit width normal grinding force F'nand unit width tangential grinding force F't,grinding specific energy esand grinding effective machining energy efficiency?are discussed.At the same time,the primary and secondary factors of grinding force F'nand tangential grinding force F't,grinding specific energy es and grinding effective machining energy efficiency?are analyzed by three-dimensional regression method.Finally,BP neural network algorithm is used to predict the grinding specific energy esof ultrafine cemented carbide.The results show that,compared with ordinary cemented carbide,ultrafine cemented carbide has smaller grinding specific energy es,and the grinding specific energy esdecreases with the increase of grinding depth apand workpiece feed speed vw,and increases with the increase of grinding wheel linear speed vs.The influence of grinding depth apand workpiece feed speed vwon grinding specific energy esis the same,and the influence of grinding wheel linear velocity vsis the least.In actual machining,increasing the workpiece feed speed vwand grinding depth ap,reducing the wheel linear velocity vscan reduce the specific energy es,thus reducing the total energy consumption of grinding.The grinding efficiency?increases with the increase of grinding depth ap,workpiece feed speed vwand grinding wheel linear speed vs.Among them,the effect of grinding depth ap on grinding efficiency ? is the largest,and the effect of workpiece feed speed vwand grinding wheel linear speed vsis the same.When the material removal rate MRR is fixed,increasing the grinding depth apand reducing the workpiece feed rate vwcan improve the grinding efficiency?,thus improving the energy utilization.2.Research on grinding surface quality.The effects of grinding parameters and Co content on the surface roughness and residual stress in parallel and vertical directions are discussed.The primary and secondary factors of the surface roughness and residual stress in parallel and vertical directions are analyzed by three-dimensional regression method.The influence of grinding parameters on the surface morphology was studied.The results show that the material removal mode is basically plastic,but there are also a few brittle fracture removal modes in the grinding process.The residual stress?decreases with the increase of grinding wheel linear velocity vs,and increases with the increase of grinding depth apand workpiece feed speed vw.Among them,the influence of grinding depth apon the residual compressive stress?is the largest,the linear velocity of grinding wheel vsis the second,and the feed velocity of workpiece vwis the smallest.With the increase of Co content,the residual stress?increases.When the material removal rate is the same as MRR,increasing the workpiece feed rate vwand reducing the grinding depth apcan reduce the residual stress?,thus obtaining better surface quality.The surface roughness in the vertical direction is greater than that in the horizontal direction.The surface roughness increases with the increase of workpiece feed speed vw,grinding depth apand the decrease of grinding wheel linear velocity vs.Among them,the feed speed of workpiece vwhas the greatest influence on the surface roughness Ra,the linear speed of grinding wheel vsis the second,and the grinding depth apis the smallest.The surface roughness Raincreases with the increase of Co content.When the material removal rate MRR is fixed,increasing the grinding depth apand reducing the workpiece feed speed vwcan reduce the surface roughness Ra in the parallel and vertical directions,so as to obtain better surface quality.3.Optimize grinding energy consumption and grinding surface quality.The machining quality is characterized by the surface roughness Raand the surface residual stress?,and the sustainability is characterized by the grinding specific energy es.The three indexes of grinding specific energy es,surface roughness Raand surfaceresidual stress?are weighed and analyzed by using the grey correlation theory and multi index orthogonal optimization method.The results show that the grinding wheel linear velocity vshas the greatest impact on the comprehensive optimization,followed by the grinding depth ap,The workpiece feed speed vwis the minimum.Under the experimental conditions,the optimal combination of grinding parameters is as follows:grinding depth ap=5?m,feed speed vw=48 mm/s,grinding wheel speed vs=30 m/s.
Keywords/Search Tags:grinding energy consumption, surface quality, BP neural network prediction, grey correlation theory, multi index optimization
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