• Title/Summary/Keyword: 정밀단조

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Preform Design of the Bevel Gear for the Warm Forging using Artificial Neural Network (신경망을 이용한 정밀 베벨기어의 온간단조 예비성형체 설계)

  • 김동환;김병민
    • Journal of the Korean Society for Precision Engineering
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    • v.20 no.7
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    • pp.36-43
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    • 2003

  • 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.

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A fundamental study on semi-solid forging with light and hardly formable materials (난가공성 경량소재의 반용융 단조에 관한 기초 연구)

  • Choi, J.C.;Cho, H.Y.;Min, G.S.;Park, H.J.;Choi, J.U.
    • Journal of the Korean Society for Precision Engineering
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    • v.13 no.7
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    • pp.29-35
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    • 1996

  • For semi-solid forging with aluminuim alloys, it is required to develope the globular grain structure. It was studide that cold upsetting ration in SIMA process has effect on the globularization of grain structure. Globular microstructure was generated without cold upsettings for commercial aluminium alloys. In the case of A12024, the range of grain size was 40 .approx. 50 .mu. m. The grain growth in growth in globular microstructure depend on heating time. Spur gear was forged in semi-solid state to investigate the forging condition for A12024 with hydraulic press.

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