Synthesis and Characterization of Composite Textured Surfaces with Enhanced Tribological Responses on Icy Surfaces

Abstract

Slips and falls on icy surfaces remain a major safety concern, highlighting the need for elastomeric materials that combine ice traction with abrasion durability. This dissertation investigates the development, characterization, and prediction of textured polymer composites through experimental tribology, interfacial engineering, and machine learning. First, a novel laboratory-scale high-velocity ice tribotesting method was developed using a modified Bruker UMT Tribolab to evaluate small rubber composite specimens under walking-relevant conditions. Rotational sliding provided more reliable friction measurements than reciprocating motion, establishing a practical approach for high-throughput materials screening. Second, thermoplastic polyurethane (TPU) nanocomposites reinforced with pristine and acid-functionalized graphene nanoplatelets (GNPs) were developed to improve ice traction and abrasion resistance. GNP functionalization enhanced filler dispersion, interfacial adhesion, and mechanical performance. Bioinspired surface texturing and abrasion-induced surface evolution generated functional micro-asperities and channels that supported ice traction after wear. TPU containing 20 phr functionalized GNPs demonstrated the best balance between abrasion resistance and retained ice traction. Finally, machine learning models were developed to predict TPU abrasion resistance from mechanical and viscoelastic properties, with decision tree and gradient boosting regressors demonstrating promising predictive performance. An additional industrial study extended data-driven modeling to automotive polymer composites, including predictive model development and graphical user interface implementation. Overall, this research establishes an integrated experimental and computational approach for developing durable, wear-adaptive elastomeric composites with enhanced ice traction and predictive material design capabilities.