A Computational Framework for Material Design

Abstract

A computational framework is proposed that enables the integration of experimental and computational data, a variety of user-selected models, and a computer algorithm to direct a design optimization. To demonstrate this framework, a sample design of a ternary Ni-Al-Cr alloy with a high work-to-necking ratio is presented. This design example illustrates how CALPHAD phase-based, composition and temperature-dependent phase equilibria calculations and precipitation models are coupled with models for elastic and plastic deformation to calculate the stress-strain curves. A genetic algorithm then directs the search within a specific set of composition and processing constraints for the ideal composition and processing profile to optimize the mechanical properties. The initial demonstration of the framework provides a potential solution to initiate the material design process in a large space of composition and processing conditions. This framework can also be used in similar material systems or adapted for other material classes.Keywords Ni-based superalloy · Integrated computational materials engineering · Genetic algorithm · Material design

Invited review: Machine learning for materials developments in metals additive manufacturing

“…These are the genes of the genetic algorithm. This is similar to a study completed by Li et al [97]. Once a fitness function has been identified, the next step in a genetic algorithm is to represent candidate alloys as a chromosome.…”

Computational Alloy Design for Process-Related Uncertainties in Powder Metallurgy

“…2 where l p is the lattice parameter of the matrix phase at equilibrium, which can be obtained by using its molar fraction, V m and Avogadro number, N A as l p = 4V m N A 1 3 [10]. Equation (7) shows that precipitation hardening in tool steels increases with the fraction of secondary carbides, which can be estimated by using thermodynamic calculations.…”

“…(9) The population (pop) for the next cycle will be assembled using both the parents (from previous cycle) and off-springs (from the reproduction step). (10) To diversify the pool of candidates in every reproduction step, a new set of randomly generated individuals, about 10% of the initial population, are added. This enhances the search performance by diversifying the solution space and helps to avoid convergence to local optima.…”

“…The design of alloys for multiple property requirements via a conventional prototype-and-test protocol is prohibitively time consuming and expensive. The ICME approach can significantly reduce the burden of searching multidimensional space and make the problem tractable [10,11]. We previously reported an ICME framework for designing sintered alloys [12,13].…”

Development of Single-Crystal Ni-Base Superalloys Based on Multi-criteria Numerical Optimization and Efficient Use of Refractory Elements

“…[9,12,13] Li et al proposed a computational framework combining genetic algorithms, CALPHAD computations, and mechanistic material models to optimize the alloy composition within the ternary system Ni-Al-Cr. [14] However, the coupling of a genetic algorithm to additional, computationally expensive models, prevent the optimization within a large n-dimensional design-space. Therefore, simplified surrogate models need to be defined, [15] which are easy to couple into a multi-criteria optimization problem.…”