The problem of White Etching Cracks (WEC) leading to failures in the life cycle of industrial and automotive drive trains has existed for more than 30 years. Many parameters leading to WEC, such as the presence of electricity, unfavorable lubricants, and other factors, have been identified by the use of test rigs. However, since (a) within lifecycle operation these impacts appear incidentally, and (b) the identified impacts stimulate each other as stated in WEC research, the appearance of WEC in a drive train is impossible to predict for any given application. This difficulty causes WEC to be a serious problem. Obtaining reliable failure prediction rates using construction guidelines is still impossible, resulting in warranty claims. The aim of this paper is, first, to establish a routine to determine if and how lubricants could be described numerically with respect to their susceptibility toward electrical fields and to see how this factor could be added in a later stage to construction guidelines. Second, this paper aims to give advice to the applicant in terms of countermeasures. Currently, as predictors are missing, extensive and time-consuming testing is required. Contradictory test results in the field of application, indicate the need for fundamental parameters to determine in which circumstances the application is exposed to WEC risk and to suggest countermeasures. The current study presents a simulation method based solely on the chemical structure of lubricant components, investigating the appearance of WEC and their response to increasing electrical fields. The results show a clear pattern in WEC criticality with respect to lubricants: if two or more components present in the lubricant create clusters through their dipolar interaction, an apparent WEC risk could be accurately predicted apart from test rig results. These clusters are charged like one big particle in an early stage of electrical field exposure. As a result, the surface area increases, facilitating a higher uptake of charge. The incidental breakdown of this charge is assumed. As the charge of a cluster is higher than that of single molecules, WEC critical lubricants are subjected to this pattern. The study validates these results by comparing them using lubricants known to be critical with respect to WEC, suggesting further tests in the near future. Despite the fact that some lubricants seem more critical than others related to WEC, each lubricant clearly might lead to WEC under given conditions. Thus, constructing guidelines for a prediction routine is essential.

The knowledge of how lubricants contribute to the operational life of a drive train is unclear until now, despite the fact that plenty of literature is available. A novel concept is presented in order to estimate the wear appearing in bearings addressed to the regime of mixed friction with respect to the composition and the so-called “inner” structure of the lubricant. In doing so, the composition is turned into a set of predictors describing the dipolar and inducible dipolar properties of all components as an activity amongst them and toward the surface. The results show that the activity of the solvated specie apparent, stated as the “inner” structure of the lubricant, is closely related to the surface activity and the expected wear. The technique presented here allows a fast computational procedure such that a given lubricant, once known by its constituents, could be explored with respect to the expected wear. Reducing time-consuming tests is desirable by the fact that new materials are forthcoming as a consequence of regulations and evolving green technology.

The forthcoming revolution in mobility and the use of lubricants to ensure ecological friendliness intensifies the pressure on tribology for predictors in new life cycles, mainly addressing wear. The current paper aims to obtain such predictors by studying how the wear processes that occur in a standard FE8 bearing test rig under thin film lubrication are conducted by the properties of the lubricant rather than simple viscosity parameters. Assuming that the activity of a lubricant with respect to the temperature, surface, and chemicals is a matter of its chemical potential, the results show that the nature of the base oil is a key parameter, apart from the chemical structure of the additives. Moreover, it becomes clear that chemical predictors are changing by altering the conditions they are exposed to. As an important result, the lubricant is effective in the prevention of wear if it has the capacity to uptake and transmit electrical charges due to its polarisability during a wear process.

Novel aspects in drive train lubrication are coming into the focus of future technologies covering new materials, accompagnied by digital and ecological aspects. Cradle to cradle (C2C) and waste to cradle (W2C) are offering broad sources of chemicals derived from greens out of the bio-cycle, obtained by decentralized farming. This leads to the fact, that valuable raw materials are accessible from everywhere. Chemicals derived from the bio-life cycle are in tendence rich in oxygen (nitrogen) and as such polar with a general affinity to water with high degree of sustainability and superior CO2 footprint. Lubricants as been used today in contrast are derivatives of fossils with low oxygen content and low affinity to water, poor in sustainability and CO2 footprint. As all life cycle assessments are based on the current lubrication technology, the efforts are high (low technical readiness level) for assessing bio-life cycle based lubricants with respect to their suitability in the technical life cycle, based on test rigs. However, machine learning (ML) techniques could reveal the relevant chemical predictors that are needed to pass a life cycle test rig. Such predictors are available from numerous test rig results assessing traditional lubricants and could be taken for the prediction of the novel bio-life based chemicals. Such concepts have been published recently (Literature). Novel sensor concepts could assist by early predicting failures. As such, the use of ML is inevitable to predict their usefulness in drive train. Coatings may serve as adjuvants for novel C2C (W2C) based lubrication. So far, to open the gate for bio-life based C2C (W2C) chemistry brings up a stringent need for novel sensor concepts in junction with ML. The upcoming EV drive train technology is reaching out for non water based fluids with high heat capacity and thermal conductivity for the electrical segment on one side and low viscous lubricants for the mechanical part on the other. Novel, ML based technologies in C2C (W2C) chemistry could couple both parts into one, as a vision for decentralized sustainable and low CO2 footprint technologies.