Multi-objective constrained design of nickel-base superalloys using data mining- and thermodynamics-driven genetic algorithms

Abstract

Photonic crystal(PhC) waveguides are used for a wide range of applications with diverse performance metrics. A waveguide optimised for one application may not be suitable for others and no one-size-fits-all solution exists. Therefore each application requires a specialised waveguide design, a computationally and time intensive process. Here, we present a hybrid, multi-objective optimisation routine for PhC waveguides, to efficiently guide the device design. The algorithm can be configured to optimise for a wide range of performance metrics and applications. We demonstrate optimisations for three different applications: slow light performance, propagation loss due to fabrication disorder and delay line applications. For each optimisation target, our routine quickly finds practical waveguide designs (<48 h, on a laptop computer) that match or exceed the performance of state-of-the-art devices designed by the community over the last 10 years. This is also the first time that scattering loss from fabrication disorder has been incorporated into an optimisation algorithm, ensuring realistic predictions of a PhC waveguide design's practical performance.

Defect Prevention in Selective Laser Melting Components: Compositional and Process Effects

“…Solidification cracks can form in the melting zone due to the segregation of alloying elements. The susceptibility to solidification cracking can be quantified by defining the brittle temperature range (BTR), within which the volume fraction of the remaining liquid phase during solidification is less than 5% [31]. Figure 1 shows schematically the sequence of alloy solidification.…”

Development of Ni-free Mn-stabilised maraging steels using Fe2SiTi precipitates

“…10). A way to improve our design strategy can be to use computational methods incorporating multi-objective optimisation of alloys, such as the one introduced in [31]. This approach would allow us to simultaneously explore wide range of combinations in alloy composition and processing parameters that can tackle the constraints faced in this work; this could be done combining genetic algorithms with Thermodynamic predictions for phase constituency and stability, the phase kinetic models for processability and possible non-linear regression models for physical mechanisms not described by the models such as precipitates with bimodal populations.…”

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.…”