New publications on Triboinformatics

Our research in Triboinformatics, both at UWM and at ITMO, has covered two main directions:

1. Using machine-learing methods to establish correlations between tribological properties (friction and wear) and composition of composite materials.

* MS Hasan, A Kordijazi, PK Rohatgi, M Nosonovsky, 2022, Machine learning models of the transition from solid to liquid lubricated friction and wear in aluminum-graphite composites, Tribology International, Volume 165, January 2022, 107326,

* MS Hasan, A Kordijazi, PK Rohatgi, M Nosonovsky, 2021, Triboinformatic modeling of dry friction and wear of aluminum base alloys using machine learning algorithms, Tribology International 161, 107065

* MS Hasan, A Kordijazi, PK Rohatgi, M Nosonovsky, 2021, Triboinformatics approach for friction and wear prediction of Al-Graphite composites using machine learning methods, Journal of Tribology 144 (1), 011701.

* A Kordijazi, S Behera, D Patel, P Rohatgi, M Nosonovsky, 2021, Predictive Analysis of Wettability of Al–Si Based Multiphase Alloys and Aluminum Matrix Composites by Machine Learning and Physical Modeling,
Langmuir 37 (12), 3766–3777.

* A Kordijazi, HM Roshan, A Dhingra, M Povolo, PK Rohatgi, M. Nosonovsky, 2021, Machine-learning methods to predict the wetting properties of iron-based composites, Surface Innovations 9 (2-3), 111-119

2. Exploring new methods of surface roughness analysis such as the Data Topology methods:

* MS Hasan, M Nosonovsky, 2021, Topological data analysis for friction modeling, EPL (Europhysics Letters), 56001

Fig.2. (a) Confocal microscopy image of the roughness profile of a ceramic tile surface (b) schematic diagram of the correlation length in the roughness profile, (c) autocorrelation function for surface roughness along x- and y-direction, and (d) isotropic and anisotropic distribution of surface roughness (from Hasan and Nosonovsky, 2021, EPL,

We have more publications in preparation and under review.