Wetting
of multiphase alloys and their composites depends on multiple
parameters, and these relationships are difficult to predict from
first principles only. We study correlations between the composition,
surface finish, and microstructure of Al–Si alloys (Si content
7–50%) and Al metal matrix composites (MMCs) with graphite
(Gr), NiAl3, and SiC and the water contact angle (CA) experimentally,
theoretically, and with machine learning (ML) techniques. Their surface
properties were modified by mechanical abrasion, etching, and addition
of alloying elements. An ML approach was developed to investigate
correlations between the predictor variables (properties of the materials)
and the CA. Theoretical models of wetting of rough surfaces (Wenzel,
Cassie–Baxter, and their modifications) do not fully capture
the CA, while ML models follow the experimental values. A full factorial
design is utilized with combinations of all levels of the predictor
factors (grit size, silicon percentage, droplet size, elapsed time,
etching, reinforcing particles). To map the predictor variables to
the response variables, 409 experimental data points were applied
to train and test various supervised ML models, namely, regression,
artificial neural network (ANN), chi-square automatic interaction
detection (CHAID), extreme gradient boosting (XGBoost), and random
forest. The correlations between the most significant factors and
CA are explored through visualization techniques. The most accurately
trained model shows a strong positive linear correlation (r > 0.9) between predicted and observed CA values in
the
test set, indicating the robustness of the model. The experimental
measurements and artificial intelligence results demonstrate that
CA increases following mechanically abrading the surface, etching,
and adding Gr to the surface. The ML methods are promising to predict
wetting properties and to provide a deeper understanding of the physical
phenomena associated with the wettability of metallic alloys and their
metal matrix composites.
