Incorporating the Johnson–Cook Constitutive Model and a Soft Computational Approach for Predicting the High-Temperature Flow Behavior of Sn-5Sb Solder Alloy: A Comparative Study for Processing Map Development

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• 作者：H. Vafaeenezhad ; S. H. Seyedein ; M. R. Aboutalebi…
• 关键词：Solder alloy ; hot deformation ; flow stress ; artificial neural network ; Johnson–Cook model
• 刊名：Journal of Electronic Materials
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：46
• 期：1
• 页码：467-477
• 全文大小：
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Optical and Electronic Materials; Characterization and Evaluation of Materials; Electronics and Microelectronics, Instrumentation; Solid State Physics;
• 出版者：Springer US
• ISSN：1543-186X
• 卷排序：46

文摘

The high-temperature flow behavior of Sn-5Sb lead-free solder alloy has been investigated using isothermal hot compression experiments at 298 K to 400 K and strain rate between 0.0005 s−1 and 0.01 s−1. The flow stress under these test conditions was modeled using constitutive equations based on the Johnson–Cook (J–C) model and an artificial neural network (ANN). Three input factors, i.e., temperature, strain rate, and true strain, were incorporated into the network, and the flow stress was considered as the system output. One hidden layer was adopted in the simulations. Furthermore, a comparative study was carried out on the potential of the two proposed models to characterize the high-temperature flow behavior of this alloy. The capability of the models was assessed by comparing the simulation predictions using a correlation coefficient (R2). The stresses predicted by both models presented good agreement with experimental results. In addition, it was found that the ANN model could predict the high-temperature deformation more precisely over the whole temperature and strain rate ranges. However, this is strongly dependent on the availability of extensive, high-quality data and characteristic variables.