Probabilistic design of a molybdenum-base alloy using a neural network
This work addresses the need for improved molybdenum-base alloys in forging-die applications, representing an incremental advance through AI-driven optimization.
The researchers tackled the problem of designing a molybdenum-base alloy by using an AI tool to optimize for multiple targets like cost and mechanical properties, resulting in a new alloy that experimentally exceeded the performance of commercial alternatives in forging-die applications.
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.