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第32届中国控制与决策会议

作者： Bo-Wen Yuan Zhong-Liang Zhang Xing-Gang Luo Yang Yu Jing-Ying Sun Xiao-Hua Zou Xiao-Dong Zou Department of Information Science and Engineering Northeastern University Department of Foundry BMW Brilliance Automotive Ltd Department of Management Hangzhou Dianzi University

low pressure die casting（LPDC） is widely used in crankcase *** to the unstable production situation and the complex shape of crankcase,porosity defect usually occurs in LPDC process which leads to loss in quality and *** this paper,we apply data mining methods to predict the porosity defect in advance,so that we can take actions to prevent it from reoccurring in the next production part,thereby increasing the quality and *** do it,firstly,we collect production data from a real-world casting ***,we prepare the data for prediction by feature extraction and feature ***,we apply an ensemble algorithm named Forest of Local Trees（FLT） for defect ***,we present a thorough experimental study of the proposed *** results show that our method outperforms other five algorithms on five real-world datasets in terms of three indicators,recall,precision and F-measure.

关键词： low pressure die casting Defect prediction Classification Data mining

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Study on Numerical Simulation of Mold Filling and Solidification Processes under pressure Conditions

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Journal of Materials Science & Technology 2002年第5期18卷 413-416页

作者： ShoumeiXIONG BaichengLIU LiangrongJIA XinghuoWEN Dept. of Mech. Eng. Tsinghua Univ. Beijing 100084 China

The mold filling and solidification simulation for the high pressure die casting (HPDC) and low pressure die casting (LPDC) processes were studied. A mathematical model considering the turbulent flow and heat transfer phenomenon during the HPDC process has been established and parallel computation technique was used for the mold filling simulation of the process. The laminar flow characteristics of the LPDC process were studied and a simplified model for the mold filling process of wheel castings has been developed. For the solidification simulation under pressure conditions, the cyclic characteristics and the complicated boundary conditions were considered and techniques to improve the computational efficiency are discussed. A new criterion for predicting shrinkage porosity of Al alloy under low pressure condition has been developed in the solidification simulation process.

关键词： Mold filling Solidification Numerical simulation High pressure die casting low pressure die casting

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Determining casting defects in near-net shape casting aluminum parts by computed tomography

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Frontiers of Mechanical Engineering 2018年第1期13卷 48-52页

作者： Jiehua LI Bernd OBERDORFER Daniel HABE Peter SCHUMACHER Institute of casting Research Montanuniversitat Leoben LeobenA-8700 Austria Austrian Foundry Research Institute Leoben A-8700 Austria

Three types of near-net shape casting alumi- num parts were investigated by computed tomography to determine casting defects and evaluate quality. The first, second, and third parts were produced by low-pressure die casting （Al-12Si-0.8Cu-0.5Fe-0.9Mg-0.7Ni-0.2Zn alloy）, die casting （A356, A1-7Si-0.3Mg）, and semi-solid casting （A356, A1-TSi-0.3Mg）, respectively. Unlike die casting （second part）, low-pressure die casting （first part） sig- nificantly reduced the formation of casting defects （i.e., porosity） due to its smooth filling and solidification under pressure. No significant casting defect was observed in the third part, and this absence of defects indicates that semi- solid casting could produce high-quality near-net shape casting aluminum parts. Moreover, casting defects were mostly distributed along the eutectic grain boundaries. This finding reveals that refinement of eutectic grains is necessary to optimize the distribution of casting defects and reduce their size. This investigation demonstrated that computed tomography is an efficient method to determine casting defects in near-net shape casting aluminum parts.

关键词： near-net shape casting aluminum parts cast-ing defects low pressure die casting die casting semi-solidcasting computed tomography

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