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首页> 外文期刊>Journal of Materials Engineering and Performance >Physical Model Based on Data-Driven Analysis of Chemical Composition Effects of Friction Stir Welding
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Physical Model Based on Data-Driven Analysis of Chemical Composition Effects of Friction Stir Welding

机译：基于摩擦搅拌焊接化学成分效应数据驱动分析的物理模型

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摘要

Variations in chemical compositions can lead to changes in the mechanical properties during friction stir welding (FSW). To facilitate control over the final mechanical properties of the friction stir weld, the relationship between the chemical compositions and final mechanical properties must be investigated. An artificial neural network was used for a data-driven analysis of the effects that chemical compositions have on the mechanical properties of FSW. A precipitate evolution model was implemented to examine the detailed contributions of different elements to the final mechanical properties. Experiments with different chemical compositions were conducted to validate the established models. Through both numerical and experimental analyses, it was determined that the yield strength in the stir zone increased with an increase in Mg/Si owing to the formation of Mg2Si. The mechanical properties also increased with Si, Mg, and Cu contents in the solid solution. The mechanical properties decreased with an increase in the Fe and Mn contents owing to the formation of an intermetallic compound alpha-Al-x(MnFe)(y)Si-z. The final mechanical properties were determined by both the welding temperature and chemical compositions. By utilizing a physical model based on a data-driven analysis, the mechanical properties could be optimally controlled.
机译：搅拌摩擦焊（FSW）过程中，化学成分的变化会导致机械性能的变化。为了便于控制搅拌摩擦焊的最终机械性能，必须研究化学成分和最终机械性能之间的关系。采用人工神经网络对化学成分对搅拌摩擦焊机械性能的影响进行数据驱动分析。采用沉淀演化模型来研究不同元素对最终力学性能的详细贡献。为了验证所建立的模型，进行了不同化学成分的实验。通过数值和实验分析，确定搅拌区的屈服强度随着Mg/Si的增加而增加，这是由于Mg2Si的形成。随着固溶体中Si、Mg和Cu含量的增加，合金的力学性能也随之提高。由于形成了金属间化合物α-Al-x（MnFe）（y）Si-z，机械性能随着Fe和Mn含量的增加而降低。最终的机械性能由焊接温度和化学成分决定。通过使用基于数据驱动分析的物理模型，可以对机械性能进行最佳控制。

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