Probabilistic design of a molybdenum-base alloy using a neural network

TL;DR

This paper presents an AI-driven approach to designing a new molybdenum-base alloy optimized for multiple properties, validated through experiments that outperform existing alloys.

Contribution

It introduces a neural network-based method for alloy design that predicts and optimizes multiple target properties simultaneously.

Findings

The AI-designed alloy meets all targeted property criteria.

Experimental results confirm the alloy's superior physical properties.

The approach accelerates alloy discovery beyond traditional methods.

Abstract

An artificial intelligence tool is exploited to discover and characterize a new molybdenum-base alloy that is the most likely to simultaneously satisfy targets of cost, phase stability, precipitate content, yield stress, and hardness. Experimental testing demonstrates that the proposed alloy fulfils the computational predictions, and furthermore the physical properties exceed those of other commercially available Mo-base alloys for forging-die applications.