When machines operate under extreme conditions, they often need to perform to maximum capacity. The high demands cause the amount of wear to increase relative to ‘the normal’ situation. Moreover, the extreme conditions are typically variable, making it impossible to define fixed maintenance intervals. When failure of such machines has to be prevented and maintenance is to be performed efficiently, this means that the amount of wear somehow needs to be calculated based on the use and operating conditions of the machines. Certainly in the case of machines that work in sandy environments, sand particles entering in between the machines’ components in sliding contact cause increased amounts of wear. The wear caused by this sliding movement of hard particles through a softer surface is called abrasion and is a prominent wear mechanism decreasing the life time of e.g. gears and bearings. Traditionally, the amount of wear due to abrasion is estimated with a simple model based on Archard’s wear law. Such models, however, are considered too simple and not adequate to calculate abrasive wear rates and predict maintenance intervals. The work presented in this thesis investigates the abrasion mechanism and improves the model for third-body abrasion. One of the main factors influencing third-body abrasion is formed by the properties of the particles causing the wear. According to the literature, the most important particle properties related to abrasion are the size, shape and hardness. Because the exact influence of particle size and shape on abrasion is not yet fully understood, the relations between these properties and wear have been verified both experimentally and numerically. New parameters were derived to describe the shape of particles in general. A numerical finite element model in Abaqus for a tip with a predefined shape sliding through a surface was set up to simulate the wear types associated to abrasion and further establish the relation between abrasive wear and particle shape and size. The results were implemented in a predictive maintenance setup for vehicles operating in sandy environments.
|Award date||23 Apr 2014|
|Place of Publication||Enschede|
|Publication status||Published - 23 Apr 2014|