Dynamic Characteristics Analysis And Optimization Of Vertical Lathe

In engineering, we often need to use the function of automatic loading and unloading inthe lathe. In this paper, the research object of vertical lathe is specially designed for processingrotary disk type parts, which has the function of automatic loading and unloading. In order tomeet the quality of the product in the development of modern society, processing capability ofCNC machine tools need to be improved continuously. Realize Dynamic characteristicsanalysis and timely optimization in the design process is the majority of the engineering staffexpectations, this article launches the research related to machine tool equipment.According to the related theories of structural vibration firstly, define the system inherentfrequency and vibration type with solving the characteristic value and characteristic vector ofsystem. The system of forced vibration equation is transformed into a dynamic model ofmechanical structures by using the finite element method.By introduce the distribution andforms of work, design and build3D models of machine parts and machine, then build thefinite element model in finite element software in consideration of the mesh size and theassembly of contact. Obtaining the first three natural frequencies of the key parts inconstrained modal analysis and find weak positions of the structure based on vibration type.Offer the instructions and reference of how to add the prestress in modal analysis andharmonic response analysis. Through vibration test instrument, measure the velocity responseof bed and the beam. demonstrate and analysis the results of finite element analysis throughthe test frequency response curve.Combine layout and processing condition of vertical lathe, based on the finite elementanalysis, make further research on dynamic optimization design of machine tool. Compareresults of different internal ribs structural shape and layout of the bed in finite element analysis,find the optimum scheme of Stiffened plate layout. In the size optimization, put the maximumfrequency as a purpose, by means of BP neural network and using genetic algorithm tooptimize the size of supporting system, Find four design variables of bed and beam supportsystem after optimization, the results basically reached the intended purpose.