Collaborative Research: Enhancing the Fracture Strength of Advanced Ceramics by Process Control at the Microstructural Scale

先进结构陶瓷的广泛应用受到所有脆性材料普遍存在的结构可靠性问题的限制。陶瓷在断裂前不会屈服，因此断裂强度通常是预测设计极限的基础。不幸的是，陶瓷的强度被发现对工艺变量非常敏感，这种方式很难预测。近年来，人们在增韧陶瓷方面做了大量的工作，使用的机制导致了对复杂微结构的需求。这类工作通常是从工艺控制的研究中分离出来的，因此对工艺条件和断裂行为之间的关系的系统和定量评估相对较少。该项目使用一种紧密结合的跨学科方法来确定陶瓷工艺变量与氧化铝断裂行为之间的关系。在第一年的工作中，将通过有限数量的断裂强度测量，在微观结构异质性的分布和抛光表面和研磨表面的结构之间建立关联。陶瓷加工、显微组织演变、硬度分布和断裂行为将通过基于定量神经网络的微观结构层次的过程模型与图像分析相结合而联系在一起。最终的结果将是对工艺变量对先进陶瓷断裂的影响的更好的定量理解--这是一种基于对微结构演变的合理统计分析的理解，并且可以用来适当地制造这些陶瓷。该项目将是图像分析和计算智能在先进材料处理中的及时应用，并将有助于将材料作为复杂系统研究的新兴范例。此外，高强度陶瓷工艺建模的成功演示将促使陶瓷加工的重点转向对微观结构控制的定量理解。最后，教育成果将提供资源，将材料加工作为一门跨学科的学科教授给大学生，并让年轻的中学生参与材料科学和图像处理领域。

The wide application of advanced structural ceramics is limited by the problems related to structural reliability that are common to all brittle materials. Ceramics do not yield before fracture and therefore fracture strength is often the basis for the prediction of a design limit. Unfortunately, the strength of ceramics is found to be very sensitive to processing variables in ways that are difficult to predict. In recent years much effort has gone into toughening ceramics using mechanisms that result in the need for complex microstructures. Such work is usually isolated from studies in process control resulting in relatively few systematic and quantitative evaluations of the relationships between processing conditions and fracture behavior. This project uses a closely integrated interdisciplinary approach to determine the relationships between ceramics processing variables and fracture behavior of alumina. For this first year effort, correlations will be made between the distribution of microstructural heterogeneities and the structure of both polished and ground surfaces along with a limited number of fracture strength measurements. Ceramics processing, microstructural evolution, hardness distributions and fracture behavior will be linked through quantitative neural network based process models at the microstructural level combined with image analysis. The end result will be an improved quantitative understanding of the effects of processing variables on the fracture of advanced ceramics -- an understanding based on sound statistical analysis of microstructure evolution, and one which can be used to properly manufacture these ceramics. This project will be a timely application of image analysis and computational intelligence in the processing of advanced materials and will contribute to the emerging paradigm of studying materials as complex systems. Furthermore, the successful demonstration of process modeling of high strength ceramics will motivate a shift of the emphasis in ceramics processing to a quantitative understanding of microstructural control. Finally, the educational outcomes will provide resources to teach materials processing as an interdisciplinary subject to university students, and engage young middle school students in the realms of material science and image processing.