检索结果-南通市图书馆

Basic rules for rheologic forging process of semisolid alloy

在线全文

学校读者我要写书评

暂无评论

引用

Journal of University of Science and Technology Beijing 2004年第6期11卷 566-571页

作者： shumingxing LizhongZhang JianboTan ChuanlinZheng HanwuLiu PengZhang YunhuiDu SemisolidProcessingResearchCenter BeijingJiaotongUniversityBeijing100044China

Semisolid mold forging is a major type of semisolid processing, which isdifferent from neither traditional mold forging nor traditional permanent casting. However,processing defects are often seen in work pieces because of lacking available rules for the processdesign and control. Some basic rules for the process design and control, simply named the shortestflowing length, pressure filling and the minimum uplifting mold pressure, are advanced in the paperbased on amount of researches and experiments. The equations to determine the major processparameters are given out such as the filling pressure, forming pressure and locking mold pressurefor the process design and control. The rules and equations are experimentally proved available andapplicable by several actual work pieces produced by the semisolid forging process.

关键词： semisolid alloy rheologic forming mold-forge process design rule

Influence of Diffusion Time on Steel-Aluminum Solid to Liquid Bonding Interfacial Structure

在线全文

学校读者我要写书评

暂无评论

引用

Journal of Materials Science & Technology 2002年第5期18卷 468-470页

作者： PengZHANG Yunhui shumingxing LizhongZHANG DabenZENG Jianzhong LiminBA Dept. of Mech. Eng. Northern Jiaotong Univ. Beijing 100044 China Dept. of Mech. Eng. Tsinghua Univ. Beijing 100084 China Metal Forming Dept. Northeastern Univ. Shenyang 110004 China Anshan Automobile Fittings Factory Anshan 114014 China

The bonding of steel plate to aluminum liquid was conducted using rapid solidification. The influence of diffusion time on interfacial structure was studied. The results showed that under the condition of 750癈 for the temperature of aluminum liquid and 200癈 for the preheat temperature of steel plate, when diffusion time was shorter than 4.3 s, there was only Fe-AI solid solution at the interface. When diffusion time was longer than 4.3 s, Fe-AI compound began to form at the interface. The relationships between diffusion time t and thickness of Fe-AI compound layer H are H=-9.72+2.62t-0.08t2 (4.3 s15 s).

关键词： Diffusion Rapid solidification Interfacial structure.

Thickness of compound layer in steel-aluminum solid to liquid bonding

在线全文

学校读者我要写书评

暂无评论

引用

Journal of University of Science and Technology Beijing 2003年第5期10卷 48-52页

作者： PengZhang YunhuiDu HanwuLiu shumingxing DabenZeng JianzhongCui LiminBa DepartmentofMechanicalEngineering NorthernJiaotongUniversityBeijing100044China DepartmentofMechanicalEngineering TsinghuaUniversityBeijing100084China MetalFormingDepartment NortheasternUniversityShenyang110006China AnshanAutomobileFittingsFactory.Anshan114014 China

The bonding of solid steel plate to liquid aluminum was studied using rapidsolidification. The surface of solid steel plate was defatted, descaled, immersed (in K_2ZrF_6 fluxaqueous solution) and stoved. In order to determine the thickness of Fe-Al compound layer at theinterface of steel-aluminum solid to liquid bonding under rapid solidification, the interface ofbonding plate was investigated by SEM (Scanning Electron Microscope) experiment. The relationshipbetween bonding parameters (such as preheat temperature of steel plate, temperature of aluminumliquid and bonding time) and thickness of Fe-Al compound layer at the interface was established byartificial neural networks (ANN) perfectly. The maximum of relative error between the output and thedesired output of the ANN is only 5.4%. From the bonding parameters for the largest interfacialshear strength of bonding plate (226℃ for preheat temperature of steel plate, 723℃ for temperatureof aluminum liquid and 15.8 s for bonding time), the reasonable thickness of Fe-Al compound layer10.8 μm was got.

关键词： bonding of steel plate to liquid aluminum rapid solidification thickness ofFe-Al compound layer artificial neural networks