Prof Ian Nettleship

Associate Professor

Dept. Mechanical Engineering & Materials Science

University of Pittsburgh
Pittsburgh
PA
15261
United States
PH: +1 (412) 6249735
Fax: +1 (412) 6248069
Email: [email protected]

Background

Dr Nettleship’s teaching interests include materials processing and mechanical properties. He teaches introductory materials science and engineering as well as higher level undergraduate courses in ceramics, materials processing and mechanical properties of materials. He is particularly interested in the undergraduate laboratory experience and its role in teaching fundamental concepts and tools. At the graduate level he teaches ceramics processing and mechanical behavior of ceramic materials.

Dr. Nettleship has two main areas of research. The first is the processing of macroporous ceramics for biomedical and environmental applications. The second, termed “Microstructure Mining”, involves creating and using microstructural information to support decision making for processing high reliability materials. This is described below.

Dr. Nettleship’s research interests are rooted in the concept of “microstructure mining” which he has developed over the last decade. Until now materials research has tended to focus on new materials, often emphasizing underlying mechanisms and new physical understanding. While this results in an appreciation of the “ideal microstructure”, it often fails to provide information on the microstructural phenomena that control reliability, a topic that is of primary concern to those interested in manufacturing. Microstructure mining has been developed as a response to this circumstance. It is an interdisciplinary approach which considers material structures to be complex systems and combines new methods in quantitative microstructural analysis and materials informatics to address problems in processing for high reliability. In this method, digitized microstructural images are processed and assembled into databases that can be searched using existing database mining methods. The resulting correlations can be used for: (i) empirical process modeling, (ii) exploring new physical understanding of materials processing and (iii) developing and testing of phenomenological models of microstructure evolution.

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