Machinery Lubrication magazine published by Noria Corporation
Issue link: https://www.e-digitaleditions.com/i/1433576
12 | November - December 2021 | www . machinerylubrication.com Visualization and Classification of Wear Particles — AI and machine learning techniques are particularly powerful for image identification and classification. A number of researchers have used such techniques on wear particles. Size, texture, shape and color are all parameters that contain information about the wear mechanism and where it occurs. Neural network algorithms have been used to identify if the wear particle is metallic or an oxide; and whether the wear is due to fatigue or severe sliding. Design of New Materials — Machine learning has been used by a number of researchers to optimize the properties of metal alloys and vapor-deposited hard coatings. For coatings, the thickness, composition, hard- ness and Young's modulus all contribute to the tribological performance. Hard coatings are usually used to reduce wear. Similarly, for metallic alloys, properties such as density, hardness, Young's modulus and fatigue life are all properties that researchers are attempting to optimize. P r e d i c t i o n o f Lubricant Properties — Lubricants are often a complex mixture of two or three different base oils, an additive package (which could comprise from 1% to 15% of the final lubrica nt), a nd in some appl ic at ion s (such as engine oil), large polymers known as viscosity modifiers. Predicting the various v isc osit ie s of t he lubricant for different temperatures and shear rates is not straight- forward; historically, such properties have been measured experi- mentally on a number of slightly different blends to f ind the optimum. T h i s t y p e of problem is amenable to AI and machine learning since there are only a limited set of base oils, additive packages and viscosity modifiers in widespread use, and such tech- niques would be useful to lubricant and additive companies to improve the efficiency of their lubricant design process. Such companies are also likely to have good viscometric data on a wide range of lubricant formulations to test their AI models on. Unfortunately, since the composition of lubricants is usually a closely guarded secret or can change without warning, these techniques are likely to be kept "in-house" and used by lubricant suppliers for their own products. Prediction of Lubrication Regime — Recently, researchers have used machine learning to predict the lubrication regime of a journal bearing. Key parameters such as speed, load, oil and surface temperatures, contact conditions and friction coefficient were recorded. Characteristic frequencies were found for the different lubrication regimes, and the model developed was able to distinguish between journals in the hydrodynamic, mixed or boundary lubrication regimes. Numerous researchers have attempted to use artificial intelligence and machine learning to predict friction and wear. is is a much more complex problem since friction and wear are properties of the complete system rather than its individual components. e figure below shows a schematic of the complicated nature of a simple lubricated contact. Often, "running-in" takes place over a number of hours, during which contact conditions change. e simple passage of a loaded ball or cylinder over a flat piece of metal can induce hardness and other changes to the metals, which can influence friction and wear. In addition, there are complex tribo-films formed by the lubricant, whose composition and other properties are not precisely known. e contact conditions (hydrodynamic, mixed or boundary lubrication) are determined by the operating conditions (speed, load, lubricant temperature, surface roughness), and, if wear occurs, the type of wear (abrasive or adhesive) depends critically on the loads and speeds. COVER STORY Figure 2: The complex nature of a typical lubricated contact.