Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies

Abstract

Data-driven models were constructed for the mechanical properties of multi-component Ni-based superalloys, based on systematically planned, limited experimental data using a number of evolutionary approaches. Novel alloy design was carried out by optimizing two conflicting requirements of maximizing tensile stress and time-to-rupture using a genetic algorithm-based multi-objective optimization method. The procedure resulted in a number of optimized alloys having superior properties. The results were corroborated by a rigorous thermodynamic analysis and the alloys found were further classified in terms of their expected levels of hardenabilty, creep, and corrosion resistances along with the two original objectives that were optimized. A number of hitherto unknown alloys with potential superior properties in terms of all the attributes ultimately emerged through these analyses. This work is focused on providing the experimentalists with linear correlations among the design variables and between the design variables and the desired properties, non-linear correlations (qualitative) between the design variables and the desired properties, and a quantitative measure of the effect of design variables on the desired properties. Pareto-optimized predictions obtained from various data-driven approaches were screened for thermodynamic equilibrium. The results were further classified for additional properties.

A Computational Framework for Material Design

“…A GA is used as it enables random but directional iterative optimization of the design parameters [35]. It has been successfully applied in many material research related problems [13,42,58,87]. Unlike the gradient-based or the other grid search algorithms, each GA process requires a significant number of iterations to converge but it is efficient for multi-objective, multi-dimensional optimizations [35,75].…”

“…A variety of efforts have been made to design efficient algorithms, including genetic algorithms (GA) coupled with CALPHAD-based tools [58,86,87], atomistic simulations [13,31,40], and data-driven approaches [42,55,78,102]. The goal of this work is to develop a platform that integrates federated experimental and computational data repositories with CALPHAD-based tools and mechanistic property models to predict materials behavior and enable materials design using a GA. A model ternary Ni-Al-Cr alloy is chosen to demonstrate this platform.…”

Algorithms for design optimization of chemistry of hard magnetic alloys using experimental data

“…Integrated Computational Materials Engineering (ICME) approach [8] and materials genome initiative highlighted the importance and growing application of computational tools in the design of new alloys. In recent years, various data-driven techniques combined with evolutionary approaches [9] have been successfully implemented in direct alloy design [9][10][11][12][13][14] and inverse alloy design [15] and in improving thermodynamic databases such as Thermocalc [16] for alloy development. Jha et al [12,13] demonstrated the scope of use of these databases for designing Ni-based superalloy and Rettig et al [14] performed a few experiments to confirm his findings.…”

“…In recent years, various data-driven techniques combined with evolutionary approaches [9] have been successfully implemented in direct alloy design [9][10][11][12][13][14] and inverse alloy design [15] and in improving thermodynamic databases such as Thermocalc [16] for alloy development. Jha et al [12,13] demonstrated the scope of use of these databases for designing Ni-based superalloy and Rettig et al [14] performed a few experiments to confirm his findings. Data mining approaches such as Principal Component Analysis (PCA) and Partial Least Square (PLS) regression have been successfully used in designing new alloys [17,18].…”

An Improved Brain Storm Optimization with Differential Evolution Strategy for Applications of ANNs

“…In recent decades, in order to deal with the complicated training problem of the ANN, many metaheuristic optimization algorithms, such as Simulated Annealing (SA) [26,27], GA [30], and PSO [31], have been utilized to optimize the weights and biases of ANN. Besides, the recently proposed EvoNN [32,33] algorithm, which utilizes the multiobjective optimization technique in the training process of a feedforward neural network, ensures correct neural training by working out a Pareto tradeoff between the accuracy of the training and the complexity of the network. In this section, we use our BSODE algorithm to adjust connection weights and biases of ANN.…”