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Preform Design of the Bevel Gear for the Warm Forging using Artificial Neural Network  

김동환 (진주국제대학교 자동차공학과)
김병민 (부산대학교 정밀정형 및 금형가공연구소)
Publication Information
Journal of the Korean Society for Precision Engineering / v.20, no.7, 2003 , pp. 36-43 More about this Journal
Abstract
In this paper, the warm forging process sequence has been determined to manufacture the warm forged product for the precision bevel gear used as the differential gear unit of a commercial automobile. The preform shape of bevel gear influences the dimensional accuracy and stiffness of final product. So, the design parameters related preform shape such as aspect ratio and chamfer length having an influence the formability of forged product are analyzed. Then the optimal conditions of design parameters have been selected by artificial neural network (ANN). Finally, to verify the optimal preform shape, the experiments of the warm forging of the bevel gear have been executed. The proposed method can give more systematic and economically feasible means for designing preform shape in metal forming process.
Keywords
Preform design; Artificial neural network; Bevel gear; Warm forging; Finite volume method;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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