Automated Fractographic Analysis of Brittle Components

Date: 2020/10/30 - 2020/10/30

Academic Seminar: Automated Fractographic Analysis of Brittle Components

Speaker: Roberto Dugnani, Associate Professor, UM-SJTU Joint Institute, Shanghai Jiao Tong University

Time: 10:00 -11:30, October 30, 2020, (Beijing Time)

Location: CIMC Auditorium (Room 300), JI Long Bin Building

via Zoom (Meeting ID: 67867585307 Password: 4394)


The National Institute of Standards and Technology (NIST) estimated that the failure of mechanical components –without including wear and corrosion– cost yearly to the US economy the equivalent of 4% of its gross national product (GNP). NIST’s study further found that one-third of the cost could be eliminated through better designs and better use of current technologies. Among the technological improvements that would greatly benefit the manufacturing industry is the development of modern, automated root cause analysis tools to help develop safer and more reliable products.

In this presentation, a novel approach to objectively determine the fracture origin and the strength of brittle components is described. The method relies on a baseline set of fracture surfaces gathered on components of known strengths and geometries. Computer Vision-based algorithms were developed and used to automatically compare relevant, topographic features extracted from the baseline set to the corresponding features on the component investigated. An empirical relationship based on a large database of relevant fractured components was used to accurately and objectively estimate the strength of the samples analyzed. Preliminary results indicated that the developed Computer Vision-based algorithms could estimate the strength of brittle components more efficiently than the state-of-the-art, industrial standards. Representative failure examples conducted on chemically strengthened glasses (extensively used in electronic devices), alumina and silicon nitride components (used in biomedical implants and high-temperature applications respectively), incomplete/damaged brittle fracture surfaces etc. are presented and discussed to further elaborate on the power and simplicity of the proposed methodology. Examples of how Computer Vision-based fractography could also be exploited to precisely measure residual stresses in ceramics and metals is finally touched upon.


Professor Dugnani graduated from Stanford University’s Aerospace Engineering Ph.D. program in 2004.  Since 2012, Dr. Dugnani is an invited Foreign Expert and Associate Professor in at the University of Michigan – Shanghai Jiao Tong University Joint Institute (UM-SJTU JI). Prior to this appointment, Dr. Dugnani held a position at Exponent Failure Analysis Associates, a leading international scientific and engineering consulting firm. Between 2009 and 2012 Dr. Dugnani held an Adjunct Faculty position at Santa Clara University, CA in both the Civil and the Mechanical Engineering Departments.