ICMCTF 2026 Session PP4-ThA: Greybox Models for Wear Prediction

A concise analytical and numerical study of the one-dimensional Cahn–Hilliard equation under Dirichlet boundary conditions on concentration and temperature is presented. A bi-harmonic free-energy potential consistent with CALPHAD thermodynamics is employed. Matched asymptotic expansions are used to derive outer (bulk), inner (interfacial), and transition solutions in the small interface-thickness limit. The Homotopy Analysis Method (HAM) is applied to obtain globally valid corrections without reliance on a small parameter. Applications to Cu–Ni and Pd–Si alloys demonstrate the influence of thermodynamic nonlinearity on interface structure. Recent literature highlights continued relevance of matched asymptotics in sharp-interface limits for modified models (e.g., reaction terms in protocell growth) and exact solutions via elliptic functions in convective variants. Numerical simulations confirm the validity of the analytical results.

Accurate prediction of tool wear in milling remains a major challenge due to the complex interplay between process parameters, coating properties, and local tribological phenomena at the cutting interface. Conventional approaches, whether based on extensive machining experiments or purely data-driven algorithms, often struggle to generalise across coatings and lack physical interpretability. This work includes a descriptor from tribological tests that connects generalised wear characteristics obtained through streamlined, indicative testing protocols, yielding a concise, physics-oriented model that flexibly adjusts to changes in operational parameters and contextual influences.

Building on this foundation, a hybrid model is introduced that merges the descriptor with process variables and perspectives derived from numerical simulations, thereby creating a more comprehensive depiction of wear evolution that aligns empirical patterns with conceptual frameworks. Predictions involving the range of coating variants (pulsed DC to HiPIMS) confirm the descriptor’s ability to mirror the wear results from the real-world milling experiments. In essence, this approach establishes a flexible, understandable platform for wear forecasting applicable across diverse tool setups and everyday workpieces, dramatically reducing reliance on resource-intensive testing and characterisation.

Hard, electrically conductive films with low friction and high wear resistance are relevant to electrical contact applications. Here we augment traditional process-structure-property investigations with an accelerated workflow to detect material structure/composition, prognose associated properties, and adapt the associated process to achieve improved product outcomes.This accelerated detect-prognose-adapt cycle is aided by four key elements: (1) automated combinatorial synthesis to enable rapid parameter sweeps, (2) high-throughput evaluation of both conventional and surrogate indicators of material chemistry, structure, and properties, (3) machine learning algorithms to unravel correlations in high-dimensional spaces beyond expert cognition, and (4) batchwise Bayesian optimization strategies to balance efficient exploration and exploitation.Unlike other ML-driven materials exploration campaigns that focus on variations in the composition of the material, here our primary emphasis is on variations in deposition conditions.We identify particular deposition conditions that produce metallic thin films with exceptional hardness (>9 GPa), low friction (m<0.1), and low electrical resistivity on par with commercial electrical contact alloys. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.