Surface texture of the die plays an important role on friction during metal forming. In the present study, unidirectional and random surface finishes were produced on hardened steel plate surfaces. To understand the influence of surface texture on the friction, experiments were conducted using Al-Mg alloy pins that slid against steel plates of different surface texture. In the sliding experiments, a high value of coefficient of friction was observed when the pins slid perpendicular to the sliding direction and a low value of friction occurred when the pins slid on the random surfaces. FE simulations were performed using the measured friction values to understand the stress and strain evolutions of various surface textures. The numerical results showed that the states of stress and strain rates are strongly influenced by friction at the interface, and hence would influence the final material microstructure. To substantiate the numerical results, laboratory compression tests were conducted. Surface textures on the die were attained so as to experience different friction values at different locations. A large variation in the microstructure at these locations was observed during experiments, verifying the hypothesis that surface texture and friction influence fundamental material behavior.
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.
Abstract We report a novel laser-based surface processing process, laser shock surface patterning (LSSP) integrating both surface strengthening and patterning effects might lead to broader impacts in tribology research and applications. This process utilizes the laser-induced shockwave loadings to introduce the surface strengthening and patterning effects simultaneously, leading to the fabrication of arrays of micro-indentations or protrusions for the enhanced wear resistance and manipulated friction values. Two process designs, direct-LSSP and indirect-LSSP were proposed and carried out on AZ31B Mg alloys and AISI 1045 steels, respectively. The 3D surface profiles of the samples after LSSP were characterized. The hardness of surface patterns prepared by laser processing was measured. The friction values as affected by laser processing parameters were measured by sliding tests. The relationships among laser processing parameters, micro-feature characteristics, and COF were discussed.
