CAREER: Surface Engineering by Predictive Laser Deposition of Multi-Principal Element Alloys

Lehigh University, Bethlehem PA

Investigators

Abstract

This Faculty Early Career Development Program (CAREER) award supports a transformative, experimentally-validated predictive framework to manufacture multi-principal element alloys (MPEAs). These alloys represent a new class of materials that, unlike conventional alloys such as steels, generally consist of five or more principal elements in significant proportions. Promising structural properties, such as superior mechanical strength and hardness, encourage their use as coatings for additively engineered surfaces. The processing of these materials presents some challenges, however, including uneven mixing and microstructure changes during rapid cooling that contribute to formation of cracks when these materials are deposited as coatings. This research project will build an understanding of the correlation of atomistic properties to system-scale processing parameters through the synergistic use of computational predictions, quantification of uncertainties in the processing conditions and material properties, and experimental characterization. The outcomes of the project will advance the manufacturability of these alloys and their surface coatings, which will have significant impact on many technological areas including propulsion, machinery, transportation, and medical devices. The predictive processing paradigm developed through this project will be widely applicable to an array of materials systems, and can bolster additive manufacturing processes with optimization capabilities. The tightly integrated educational and outreach activities are targeted to encourage students from underrepresented minorities to pursue opportunities in STEM fields, and simultaneously contribute towards gender equality and economic opportunities for impoverished communities. The objective of this CAREER project is to generate new knowledge on how the diffusion of multiple principal elements in an alloy melt under rapid cooling affects the microstructure and properties of their laser deposited clads. To realize this objective, an integrated computational framework will be established that (1) marries together structure and property predictions from molecular dynamics simulations of the alloy melt with processing conditions, (2) provides recommendations for optimizing the manufacturing parameters for producing clads of desired composition and quality, and (3) imparts robustness to the correlations by electron microscopy and X-ray spectroscopy characterizations, and uncertainty quantification of the high-dimensional parameter space of compositions, impurities, and manufacturing environment variables. The direct and multiscale correlation of the simulation predictions to the system scale processing parameters, will facilitate the mapping of the parameters to targeted criteria space via a Pareto front. The interrelationship between the processing conditions (system scale) and the alloy melt dynamics (atomic scale) will enable intelligent parameter selection for the laser cladding and aid in creating surface coatings that are homogenous in composition and crack resistant. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.