Water-based lubricants (WBLs) have been at the forefront of recent research, due to the abundant availability of water at a low cost. However, in metallic tribo-systems, WBLs often exhibit poor performance compared to petroleum-based lubricants. Research and development indicate that nano-additives improve the lubrication performance of water. Some of these additives could be categorized as solid nanoparticles, ionic liquids, and bio-based oils. These additives improve the tribological properties and help to reduce friction, wear, and corrosion. This review explored different water-based lubricant additives and summarized their properties and performances. Viscosity, density, wettability, and solubility are discussed to determine the viability of using water-based nano-lubricants compared to petroleum-based lubricants for reducing friction and wear in machining. Water-based liquid lubricants also have environmental benefits over petroleum-based lubricants. Further research is needed to understand and optimize water-based lubrication for tribological systems completely.

Electroactive polymers (EAPs) are an advanced family of polymers that change their shape through electric stimulation and have been a point of interest since their inception. This unique functionality has helped EAPs to contribute to versatile fields, such as electrical, biomedical, and robotics, to name a few. Ionic EAPs have a significant advantage over electronic EAPs. For example, Ionic EAPs require a lower voltage to activate than electronic EAPs. On the other hand, electronic EAPs could generate a relatively larger actuation force. Therefore, efforts have been focused on improving both kinds to achieve superior properties. In this review, the synthesis routes of different EAP-based actuators and their properties are discussed. Moreover, their mechanical interactions have been investigated from a tribological perspective as all these EAPs undergo surface interactions. Such interactions could reduce their useful life and need significant research attention for enhancing their life. Recent advancements and numerous applications of EAPs in various sectors are also discussed in this review.

Machine learning (ML) algorithms have brought about a revolution in many industries where otherwise operation time, cost, and safety would have been compromised. Likewise, in lubrication research, ML has been utilized on many occasions. This review provides an in-depth understanding of seven ML algorithms from a tribological perspective. More specifically, it presents a comprehensive overview of recent advancements in ML applied to lubrication research, organized into four distinct categories. The first category, experimental parameter prediction, highlights the significant contributions of artificial neural networks (ANNs) in accurately forecasting operating conditions related to friction and wear. These predictions offer valuable insights that aid in forensic preparation. Discriminant analysis, Bayesian modeling, and transfer learning approaches have also been used to predict experimental parameters. Second, to predict the lubrication film thickness and identify the lubrication regime, algorithms such as logistic regression and ANN were useful. Such predictions provide up to 99.25% accuracy. Third, to predict the friction and wear for a given experimental condition, support vector machine (SVM), polynomial regression, and ANN offered an accuracy above 93%. Finally, for condition monitoring for bearings, gearboxes, gear trains, and similar critical situations where regular in-person inspection is difficult, Naïve Bayes, SVM, decision trees, and ANN were utilized to predict the safe life of lubricants. This review highlighted these four aspects with state-of-the-art examples and discussed the current situation and projected future possibilities of lubricant design facilitated by ML techniques.