Abstract

Tempcore process is an environmental friendly, simple and energy efficient technology for producing high strength reinforcing steel rebars without requiring costly alloying addition. The mechanical properties of Tempcore treated steel rebar have been previously investigated using various models, although they are still restricted to specific steel compositions, bar sizes, and/or process parameters. In this study, a methodology is developed to predict the internal microstructure and overall mechanical properties (i.e., hardness, ultimate tensile strength, and yield strength) of Tempcore treated bars for any steel compositions, bar sizes, process parameters, and simulation assumptions. Three sequential models are proposed: (1) thermal model to predict thermal profiles of bars using computational fluid dynamics CFD simulation, (2) metallurgical model to estimate the internal microstructure change across the bar section using both; the JMatPro^® and a derived equation that calculates the martensite volume fraction (V_m%) of a functionally graded steel bar, and (3) Regression models based on the rule of mixture to predict mechanical properties. The validation results show a good agreement between calculated and experimental results; the mean absolute percentage errors are 2.8% for hardness, 2.8% for ultimate stress and 3.8% for yield stress. Eventually, the proposed methodology presented a sustainable, easy, fast, and cost-efficient solution to attain the required mechanical properties of a steel bar treated by Tempcore process.

Graphic Abstract

摘要

Tempcore工艺是一种环境友好，简单且节能的技术，用于生产高强度增强钢筋，而无需添加昂贵的合金。Tempcore处理过的钢筋的机械性能先前已使用各种模型进行了研究，尽管它们仍然限于特定的钢成分，钢筋尺寸和/或工艺参数。在这项研究中，开发了一种方法来预测Tempcore处理过的钢筋的内部微观结构和整体机械性能（即硬度，极限抗拉强度和屈服强度），以用于任何钢成分，钢筋尺寸，工艺参数和模拟假设。提出了三个顺序模型：（1）使用计算流体动力学CFD模拟来预测棒材热分布的热模型，（2）冶金模型使用两者来估计整个棒材内部的组织变化；JMatPro^®和一个导出的方程式，用于计算功能梯度钢的马氏体体积分数（V _m％），以及（3）基于混合规则的回归模型以预测机械性能。验证结果表明计算结果与实验结果吻合良好；平均绝对百分比误差为：硬度2.8％，极限应力2.8％，屈服应力3.8％。最终，所提出的方法论提出了一种可持续，简便，快速且具有成本效益的解决方案，以实现通过Tempcore工艺处理的钢筋所需的机械性能。

图形摘要