High-Throughput Screening of Optimal Process Parameters for PVD TiN Coatings With Best Properties Through a Combination of 3-D Quantitative Phase-Field Simulation and Hierarchical Multi-Objective Optimization Strategy

Abstract

Physical vapor deposition (PVD) is one of the most important techniques for coating fabrication. With the traditional trial-and-error approach, it is labor-intensive and challenging to determine the optimal process parameters for PVD coatings with best properties. A combination of three-dimensional (3-D) quantitative phase–field simulation and a hierarchical multi-objective optimization strategy was, therefore, developed to perform high-throughput screening of the optimal process parameters for PVD coatings and successfully applied to technically important TiN coatings. Large amounts of 3-D phase-field simulations of TiN coating growth during the PVD process were first carried out to acquire the parametric relation among the model parameters, microstructures, and various coating properties. Experimental data were then used to validate the numerical simulation results and reveal the correlation between model parameters and process parameters. After that, a hierarchical multi-objective method was proposed for the design of multiple coating properties based on the quantitative phase–field simulations and key experimental data. Marginal utility was subsequently examined based on the identification of the Pareto fronts in terms of various combinations of objectives. The windows for the best TiN coating properties were, therefore, filtered with respect to the model/process parameters in a hierarchical manner. Finally, the consistent optimal design result was found against the experimental results.

Phase field simulation of microstructure evolution and process optimization during homogenization of additively manufactured Inconel 718 alloy

“…Microstructure modeling, an essential part of integrated computational materials engineering (ICME) approach, has proven to be useful in accelerating material design and heat treatment process parameter optimization (Sahoo and Chou, 2014;Du et al, 2017;Nandy et al, 2019;Chen and Zhao, 2022;Dai et al, 2022). Among various microstructure simulation methods, phase field method (PFM) has become one of the most commonly used computational modeling techniques for studying microstructure evolution and an important component in the ICME approach to materials design.…”

Multiscale Simulation of Microstructure Evolution during Preparation and Service Processes of Physical Vapor Deposited c-TiAlN Coatings

“…Generally, multiple experimental methods are necessary to completely comprehend the evolution of the microstructure of coatings during preparation and service, as the microstructure features are dynamic and polymorphic. [7][8][9][10][11][12] on TiAlN coatings and simulation studies on TiAlN coatings during the preparation [13][14][15][16][17] and service process [18][19][20][21] Numerical simulation is advantageous for capturing dynamic evolution results and even vivid three-dimensional visualizations. Stewart et al [16] devised a phase-field model to simulate the changes in the microstructure of polycrystalline thin films throughout the preparation process.…”

“…This model was subsequently adopted by Yang et al [14] and successfully applied to establish a correlation between model parameters and deposition rate, thereby analyzing the impact of deposition rate on the surface roughness and microstructure of the metal thin films. Dai et al [13] employed a series of phasefield simulations for TiN coatings to establish relationships between process parameters and model parameters, enabling the identification of optimal processing parameter windows.…”

Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations

“…Such a strategy should be beneficial to efficiently construct the required dataset with a minimum size. As for the second challenge, the multi-objective optimization strategies, like the sequential filter strategy [ 44 ], the transformation of multi-objective into single-objective optimization methods [ 45 ], Pareto front optimization method [ 46 ], can be utilized.…”