Fundamentals of grain coalescence and applications to aluminum alloy industrial solidification processes

晶粒聚结基础及其在铝合金工业凝固过程中的应用

• 批准号: RGPIN-2016-03656
• 负责人: Phillion, Andre
• 金额: $ 2.04万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=662080
• 关键词: Fundamentals; grain; coalescence; applications; aluminum

Near-net-shape casting provides the most cost-effective way to manufacture light metal alloy components of complex shape. However, casting defects such as hot tearing reduce product quality and process efficiency. Hot tearing is characterized as a spontaneous failure of semi-solid metallic alloys due to the rupture of liquid films between grains, and occurs near the end of solidification where deformation becomes localized to the grain boundaries. The occurrence of hot tearing is intimately linked with the semi-solid's gradual evolution from isolated grains surrounded by liquid to a coherent and percolated solid network, a process known as grain coalescence. If grain coalescence is retarded and therefore finishes at lower temperatures, hot tearing susceptibility is significantly increased.***The objective of this Discovery Grant research program is to develop new knowledge of the process of grain coalescence, followed by application of the new knowledge to improve the quality of high-strength Al-Cu casting alloys that are used in the automotive and aerospace sectors. Over the next 5 years, my research team will utilize experimentation and simulation to quantify the influence of alloy content and processing parameters on the dynamic evolution of grain coalescence, and the impact of this process on semi-solid constitutive behaviour and hot tearing. Through this research, 7 HQP will be trained in developing processing/structure/defect phenomenological models, and in applying these relationships within mathematical models of aluminum alloy casting processes.***Research Approach: As a first step, a numerical model will be developed to predict grain coalescence. Concurrently, model materials with known microstructure will be created and then used as part of in-situ (restrained casting tests) and ex-situ (semi-solid tensile tests) characterization of the coalescence transition. Then, the experimental and numerical results will be used to propose a constitutive law of semi-solid mechanical behaviour based on the dynamic evolution of coalescence. Finally, the new tools will be applied to a finite element simulation of the sand casting process used to produce components made from B206 (an industrial Al-Cu casting alloy) for a tidal turbine application to demonstrate the improved ability to predict hot tearing in an industrial process.***Industrial Impact: Hot tearing is a major defect that lowers the productivity of aluminum alloy casting and wrought products. In many cases (e.g. high reliability complex shape castings), products cannot even be manufactured because of the high occurrence of this defect. The results of this research are anticipated to lead to a greatly improved understanding of coalescence and hence hot tearing, and will provide a predictive tool for use by industry in macro-scale process simulations of Al-Cu castings.**

近终形铸造为制造复杂形状的轻金属合金部件提供了最具成本效益的方法。然而，热撕裂等铸造缺陷降低了产品质量和工艺效率。热撕裂的特征是由于晶粒之间的液膜破裂而导致半固态金属合金的自发失效，并且发生在凝固末期附近，此时变形局限于晶界。热撕裂的发生与半固体从被液体包围的孤立颗粒逐渐演变为连贯和渗透的固体网络密切相关，这一过程称为颗粒聚结。如果颗粒聚结被延迟并因此在较低温度下完成，则热撕裂敏感性显着增加。***该发现补助金研究计划的目标是开发颗粒聚结过程的新知识，然后将新知识应用于提高汽车和航空航天领域使用的高强度铝铜铸造合金的质量。在接下来的5年里，我的研究团队将利用实验和模拟来量化合金含量和加工参数对晶粒聚结动态演化的影响，以及该过程对半固态本构行为和热撕裂的影响。通过这项研究，7 个 HQP 将接受开发加工/结构/缺陷唯象模型，以及将这些关系应用到铝合金铸造工艺数学模型中的培训。***研究方法：第一步，将开发一个数值模型来预测颗粒聚结。同时，将创建具有已知微观结构的模型材料，然后将其用作聚结转变的原位（约束铸造测试）和异位（半固态拉伸测试）表征的一部分。然后，实验和数值结果将用于提出基于聚结动态演化的半固体力学行为本构定律。最后，新工具将应用于砂型铸造工艺的有限元模拟，该工艺用于生产潮汐涡轮机应用中由 B206（一种工业铝铜铸造合金）制成的部件，以展示预测热撕裂的能力得到提高。工业过程。***工业影响：热撕裂是降低铝合金铸造和锻造产品生产率的主要缺陷。在许多情况下（例如高可靠性复杂形状铸件），由于这种缺陷的发生率很高，甚至无法制造产品。这项研究的结果预计将大大提高对聚结和热撕裂的理解，并将为工业界在铝铜铸件的宏观工艺模拟中使用提供预测工具。 **