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

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. Experi- mental testing demonstrates that the proposed alloy fulfills the computational predictions, and furthermore the physical properties exceed those of other com- mercially available Mo-base alloys for forging-die applications.

Keywords: modeling, refractory metals, forging, mechanical properties, neural network

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