{"id":55612,"date":"2026-05-29T07:00:07","date_gmt":"2026-05-29T11:00:07","guid":{"rendered":"https:\/\/engineering.jhu.edu\/case\/?post_type=news&p=55612"},"modified":"2026-05-27T12:20:56","modified_gmt":"2026-05-27T16:20:56","slug":"engineers-develop-a-new-predictive-tool-for-fatigue-failure-in-engineering-components","status":"publish","type":"news","link":"https:\/\/engineering.jhu.edu\/case\/news\/engineers-develop-a-new-predictive-tool-for-fatigue-failure-in-engineering-components\/","title":{"rendered":"Engineers develop a new predictive tool for fatigue failure in engineering components"},"content":{"rendered":"

In 2021, a United Airlines aircraft travelling from Denver to Honolulu experienced engine failure, culminating in a fire. Following an investigation by the National Transportation Safety Board, it was determined that a fractured fan blade in the engine had sustained damage consistent with metal fatigue, causing the engine failure.<\/span>\u00a0<\/span><\/p>\n

To\u00a0better predict\u2014and ultimately\u00a0avoid\u2014incidents of fatigue failure,\u00a0team of\u00a0engineers\u00a0from Johns Hopkins University’s Department of Civil and Systems Engineering<\/a><\/span>\u00a0and Pratt & Whitney, an aerospace manufacturer, has developed a sophisticated multiscale model integrating physics-based modeling, machine learning and uncertainty quantification to predict fatigue nucleation in metallic parts before the appearance of cracks detected by conventional non-destructive evaluation methods.<\/span>\u00a0<\/span><\/p>\n

Fatigue failure in metallic parts, the weaking and eventual breaking of the material, is one of the costliest engineering challenges in the aerospace, automotive, transportation, construction, and industrial sectors, with an estimated expense of more than 4% of the total gross domestic product of developed countries, like the U.S. and U.K.<\/span>\u00a0<\/span><\/p>\n

\u201cFatigue is a stealthy problem,\u201d says\u00a0principal\u00a0investigator,\u00a0<\/span>Somnath Ghosh<\/span><\/a>. \u201cYou typically don\u2019t know what what\u2019s going on\u00a0beyond the visible surface area,\u00a0and\u00a0you don\u2019t know when or where fatigue\u00a0cracks\u00a0will appear, which could have disastrous consequences, especially when human lives are at stake.\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

Published in\u00a0<\/span>Nature Communications<\/span><\/i><\/a>, the study presents a prognosis tool for fatigue, termed parametrically upscaled constitutive and crack nucleation models, or PUCM-PUCNM for short, that directly links extreme microstructural events with macroscopic failure and overcomes the many challenges encountered through conventional methods of fatigue prediction. By integrating physics-based modeling, machine learning, temporal acceleration and probabilistic analysis, PUCM-PUCNM can predict changes that happen over time due to repetitive use and prolonged loading, as well as the location of fatigue failure before it happens. <\/span>\u00a0<\/span><\/p>\n

The team’s study presents their successful use of the model for fatigue prediction in titanium alloys which are frequently used in aircraft engines.<\/p>\n

The team began by analyzing titanium alloys to understand the polycrystalline microstructure. They then created a 3D model of the microscopic structure and used physics-based models to simulate how the metal would deform when exposed to repeated or extended stresses. Because fatigue can take thousands or millions of cycles of loading before failure, the researchers applied temporal acceleration to speed simulations by more than three orders of magnitude to evaluate a greater number of simulations. Machine learning was applied to generate macroscopic material response models, mapping microscopic behavior over time to the moment of fatigue failure. Lastly, due to the variance in the microstructures of different titanium alloys, the model incorporates the probability of how likely a crack is to form after a certain number of cycles of loading.<\/p>\n

\u201cThis approach is the outcome of nearly 15 years of research supported by federal funding and tested in collaboration with industry partners like Pratt & Whitney, GE Aerospace and Rolls-Royce,\u201d Ghosh says. \u201cThese companies are seeing promising results and are considering the model in their engineering design. They\u2019re also sharing non-proprietary data to help us continue to advance and validate our models.\u201d<\/p>\n

He says that the models are suitable for relatively easy industrial use, which makes them particularly attractive.<\/p>\n

To determine the accuracy of PUCM-PUCNM\u2019s predictions, the team completed three case studies, and in each instance compared the model\u2019s predictions with observations collected from previous fatigue failure studies. The first two case studies showed that PUCM-PUCNM could accurately predict dwell fatigue of a notched metal specimen and cyclic loading of a compressor disk. The third case study demonstrated that when calibrated on small-scale sections, PUCM-PUCNM could be used to predict behavior in a full-size part with more accuracy than existing methods.<\/p>\n

Enhanced fatigue predictions from the new modeling tool have both economic and safety implications. With improved prognosis technologies, engineers can design components to extend their lifespan, optimize maintenance schedules, and avoid unplanned failures.<\/p>\n

\u201cImproved product life and performance could amount to a cost savings of several hundred million dollars, globally, through federal government and industry use,\u201d Ghosh says.<\/p>\n

While the model is applicable across materials and manufacturing methods, including additively manufactured alloys which are becoming more common in the aerospace and defense industries, the team is also exploring the use of their predictive model in damage sensing and non-destructive evaluation by integrating it with additional AI tools, such as neural operators.<\/p>\n

\u201cWe\u2019re advancing the frontier in this area, and the results could be significant,\u201d says Ghosh. \u201cIndustry participation is telling that this technology is directly translatable to users for substantial gains. Our study is a demo case, but it doesn\u2019t end there. We\u2019ve shown that this is a meaningful way of coupling AI with physics-based models and that holds great promise for future investments at federal and industry levels.\u201d<\/p>\n

This work was supported by the Air Force Office of Scientific Research\u2019s Structural Mechanics and Prognosis Program and the Air Force\u2019s Metals Affordability Initiative and SPARTA program. Additional study participants include Johns Hopkins University\u2019s Kishore Appunhi Nair, Tawqeer Nasir Tak and Shravan Kotha, and Pratt & Whitney\u2019s Adam Pilchak, Vasisht Venkatesh and David Furrer.<\/p>\n","protected":false},"template":"","class_list":["post-55612","news","type-news","status-publish","hentry","news_categories-mechanics-of-materials","news_categories-research"],"acf":[],"yoast_head":"\nEngineers develop a new predictive tool for fatigue failure in engineering components - Department of Civil & Systems Engineering<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/case\/news\/engineers-develop-a-new-predictive-tool-for-fatigue-failure-in-engineering-components\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Engineers develop a new predictive tool for fatigue failure in engineering components - Department of Civil & Systems Engineering\" \/>\n<meta property=\"og:description\" content=\"In 2021, a United Airlines aircraft travelling from Denver to Honolulu experienced engine failure, culminating in a fire. 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