Optimizing Functionally Graded Metal Fabrication with AI for High-Performance Use

Abstract

Functionally graded metals (FGMs) represent a paradigm shift in advanced materials engineering,
offering spatially varying properties that enable unprecedented performance in high-demand
applications. This research investigates the integration of artificial intelligence (AI) optimization
techniques in functionally graded metal fabrication processes to enhance mechanical properties
and manufacturing efficiency. The study employed machine learning algorithms including neural
networks, genetic algorithms, and deep learning models to optimize process parameters in laserdirected
energy deposition and wire arc additive manufacturing. A comprehensive experimental
design was implemented using titanium-steel, aluminum-copper, and nickel-based superalloy
systems, analyzing microstructural evolution, mechanical properties, and thermal behavior
through advanced characterization techniques. Hypothesis testing confirmed that AI-optimized
FGM fabrication achieves 35% improvement in tensile strength, 42% enhancement in wear
resistance, and 28% reduction in manufacturing defects compared to conventional methods.
Results demonstrate successful optimization of gradient composition, thermal cycling parameters,
and layer thickness control through AI-driven process monitoring. Statistical analysis revealed
significant correlations between AI-predicted parameters and experimental outcomes with R²
values exceeding 0.92. The integration of real-time monitoring systems with machine learning
algorithms enabled adaptive process control, resulting in superior microstructural homogeneity
and enhanced functional performance. This research establishes a framework for intelligent
manufacturing of functionally graded metals, contributing to next-generation aerospace, automotive, and biomedical applications requiring exceptional material performance and
reliability.