FEATURE MECHANICAL COMPONENTS
THE DEEP SCIENCE OF IMPERFECT SURFACES
SKF principal scientist Guillermo Morales-Espejel
has been instrumental in creating a transformative approach to bearing life prediction, which
holds significant value for design engineers, equipment manufacturers, and end users
“You can’t predict bearing life deterministically,” says Morales. “You must combine statistics and physics for meaningful predictions.” Early models integrating these factors
E
appeared in the mid-20th century, with SKF scientists leading the charge. “Those early models introduced basic concepts still relevant today, like the difference between static and dynamic bearing capacity,” explains Morales.
PREDICTION AT THE LIMIT Advances over the following 50 years improved model sophistication. By the 1980s, engineers could account for the fatigue limit – a stress threshold below which fatigue in the material barely accumulates. Morales notes that 20th-century models focused mainly on subsurface fatigue. However, advancements in manufacturing, such as clean steels, have largely resolved subsurface fatigue issues. Today, most bearing failures are triggered by a problem on the surface, such as including poor lubrication, contamination, frictional heat, or electrical damage. The surface was a topic Morales knew well. With degrees in mechanical engineering from Mexico and a Ph.D. in tribology from Cambridge, he joined SKF’s research lab in 2000 to study surface issues in bearings. One early project simulated bearing performance in mixed lubrication environments, where contamination or lack of lubricant creates areas of direct metal-to- metal contact within a bearing. Another evaluated the effect on bearing life of the small indentations that can occur if a bearing is mishandled during manufacturing, shipping, or assembly.
A BROADER BRUSH Fast forward a few years, Morales and his colleagues were successfully applying mixed lubrication and surface damage models to a wide range of problems within SKF and for its customers. In 2012, a new technical director approached Morales with a bigger challenge. “He said our bearing life models were useful, but they were too rigid,” recalls Morales. “It takes too
44 DESIGN SOLUTIONS NOVEMBER 2024
ngineers have long known the impact of fatigue on bearing life, but predictive models that account for it have developed gradually.
much effort to adapt the model to a different problem or to integrate new knowledge.” The technical directors request was simple, but daunting. Could Morales and his team take what they had learned about the effect of surface conditions on bearing life and build a general-purpose model that would better predict bearing life in the real world? Their answer to this challenge was two
years in the making. “We already had some of the key ingredients,” explains Morales. “To build a general-purpose bearing life prediction model, you have to simulate the operation of different bearings, under different conditions, over millions of cycles.” Other parts of the model required the team to
break new ground. In particular, Morales says, they had to develop an approach that combined their new surface damage models with traditional methods for estimating subsurface fatigue.
ONE MODEL, MANY SOLUTIONS The first iteration of the SKF Generalized Bearing Life Model (GBLM) for conventional steel bearings was introduced as a concept at the 2015 Hannover
Messe. It offered the promise of an immediate solution to many challenges faced by design engineers every day. “With a better life prediction model, you can design better machines,” says Morales. “Our model helps designers select the optimal size and type of bearing for their application, and allows companies to provide more reliable advice on maintenance and replacement intervals.” The result is more efficient use of resources, with fewer breakdowns and less premature replacement of parts that still have life left in them. Over the past decade, Morales and his
colleagues have expanded the GBLM to include new types of bearings, notably adding models for the hybrid bearings now used in demanding applications ranging from turbomachinery to electric vehicle transmissions. They have updated the approach to reflect ongoing improvements in conventional bearing technology, including new steels, and better heat treatment techniques. The GBLM is also helping users make informed decisions about bearing remanufacturing intervals, based on the likely rate at which surface damage will accumulate in their applications. Is there more to come? As principal scientist,
Morales now has much more on his plate than bearing life models, but he maintains a strong interest in the development of the GBLM. “We have developed a flexible and extensible way to model different bearings, operating conditions and failure modes, but all such models need to be validated with data from experiments and tests,” states Morales. “Advanced sensors are now giving us better insight into the conditions inside our bearings, and these insights will help us extend and improve our modeling approach.”
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