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A New Grinding Wheel Cutting System For Pouring Riser Of Casting Group

Posted on:2022-06-07Degree:MasterType:Thesis
Country:ChinaCandidate:W X WangFull Text:PDF
GTID:2481306566461824Subject:Mechanical engineering
Abstract/Summary:PDF Full Text Request
Investment casting is widely used as a near-net-shape technology.It can be used to cast parts with high precision and complex structure,almost without the limitation of material types.The structure of the investment casting group is complex,the dies and casting are connected by casting riser.The traditional way to remove the casting riser of the investment casting group is to use the manual down-pressure abrasive cutting off machine.However,there are some defects in the cutting process,such as high labor intensity,no safety protection measures,thick dust,automation is not implemented and low production efficiency,which affect the development of the industry.To solve the above problems,according to the environmental characteristics of the operation site and mechanical performance index,a new grinding wheel cutting system for pouring riser of casting group was designed and developed.The cutting system can realize the cutting of the casting group pouring riser in a closed environment without manual intervention,which ensures the safety of workers.The new turning clamp and handling machinery arm are cooperated with each other,improving the cutting efficiency.Firstly,based on the research of the development process and manual cutting experiment of grinding wheel cutting machine for casting riser at domestic and abroad,combined with the actual production demand,a kind of casting group riser cutting device is designed.In this device,the cutting machine and fixture are used to cooperate with each other to realize the automatic removal of casting group pouring riser.The model of each part and a virtual prototype are builded by Solidworks software.The cutting process is performed kinematics simulation by Adams software,and the grinding wheel wear is compensated by means of feeding to ensure that the best cutting point of the grinding wheel was always used to cut the casting group riser.The feasibility of the new casting group riser cutting device is verified by the simulation results.Secondly,to further improve the level of automation,aiming at the problem of difficult disassembly of the casting group,a device which can automatically load and unload the casting group is designed on the basis of the original fixture.A Six-DOF robot arm model is established by improved D-H method and the workspace of manipulator is obtained by performing forward kinematics analysis and inverse kinematics analysis.The displacement,angular velocity and acceleration curve of each kinematic pair are obtained by using the quintic interpolation method.The convolutional neural network is used to segment and identify the images of the casting group,which ensures the accuracy of the clamping state of the casting group.From the micro point of view,the chip removal and material stress in the grinding process are analyzed,taking single grain as the research object,the relative motion method is used to reduce the influence of time on cutting speed.Based on the cutting simulation software,the material properties are determined by the J-C constitutive model,and the cylindrical grinding model of corundum abrasive is constructed to simulation the chip formation mechanism of cylindrical grinding with single grain at different speeds.Finally,the experimental platform was built,the orthogonal experiment was carried out to study the interaction among feed speed,linear speed of grinding wheel and cutting force,as well as their effects on the surface roughness of runner and riser and the wear of grinding wheel.Choosing reasonable cutting parameters to optimize the cutting process can solve the problems of uncertain cutting parameters,unstable cutting quality and short service life of grinding wheel.
Keywords/Search Tags:Abrasive cutting off machine, Kinematics analysis, Trajectory planning, Image segmentation, Single grain, Multi-parameter optimization
PDF Full Text Request
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