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Multi-target Preform Optimization For Spherical Hinge Mandrel Based On Kriging Model Methodology

Posted on:2017-01-28Degree:MasterType:Thesis
Country:ChinaCandidate:Z Y ZhangFull Text:PDF
GTID:2271330503964053Subject:Materials engineering
Abstract/Summary:PDF Full Text Request
Spherical hinge mandrel is an important component of thrust rod in equalizing suspension of heavy vehicle, which is apt to crack in complex working condition as the vehicle is traveling forwards. And spherical hinge mandrel is one of typical precision forging of vehicle spindle parts, for the reason that its axial section dimension changes greatly, it requires well preform of blank. Moreover, as the rapid development of China industry, the demand of heavy vehicle is dramatically increased, in order to keep equalizing suspension safe and stable, spherical hinge mandrel needs high standard of forming quality and overall performance. So far, researches on spherical hinge mandrel are far from enough, many domestic enterprises still formulate spherical hinge mandrel forging process on basis of practice experience in long term, and the reasonable preform design of similar vehicle spindles is rarely on foundation of traditional trial-and-error method which easily causes long circle of forging development and manufacture, and low material utilization. In the theme of digital manufacture today, it is significant that how to combine spherical hinge mandrel with preform optimization designs for make more exact master of every point in manufacture. The forging process of spherical hinge mandrel is complicated, maneuverability, forming quality and stability, material utilization, forming loading and die stress influence the producing process profoundly, all of them are considerable optimization objectives.The forming technology of complicated spindles on domestic and aboard, precision plastic forming technology, preform optimization design and multi-target optimization methods were simply introduced in this paper. Firstly, precision forging process of spherical hinge mandrel was discussed and compression preform method was selected towards pre-distribution of materials. Through numerical simulation, different control-experiments were designed to study blank dimension changes in compression preform, and then, the volume distribution relational expression was derived. From the derived expression, the technological parameters effective in final forging were confirmed and reasonable ranges of preform parameters were gotten to establish orthogonal test in the aim of filling ratio and material utilization. By factor index analysis and second-order prediction of orthogonal test, the optimums of less important factors and optimum ranges of more influenced factors were decided. Secondly, above orthogonal test initial sample points were selected using LHS method to conduct several numerical simulation as response of maximum forming load and die effective stress, then Kriging surrogate models were established and the accuracy was verified. Eventually, the multi-target optimization was related with surrogate models through genetic algorithm, and the final optimums of preform parameters were obtained in iterative operation.In the end, the practical production made comparison between optimized technological parameters and original designed parameters, and the results of simulation and practical operation turn out coincided and the forming quality of forging take extremely improved.This paper makes an integrate of spherical hinge mandrel, numerical simulation, orthogonal test, surrogate model method and genetic algorithm, and takes an aim of optimizing preform design, improve material utilization and optimizing forging forming quality. The achievement of research of spherical hinge mandrel enlightens practical forging production and provides theoretical instruction and practical evidence of similar forging manufacture and relevant preform design.
Keywords/Search Tags:Spherical hinge mandrel, Compression preform, Kriging surrogate model, Genetic algorithm
PDF Full Text Request
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