Computational approach to grain boundary segregation engineering of nickel-base superalloys.

Grain boundary (GB) strengthening elements, such as B, C, and Zr have been added in small amounts to nickel-base superalloys. However, their strengthening effects have not been quantified and no specific design principles for GB chemistry have been reported. In this study, we propose a practical computational approach for the GB segregation engineering of nickel-base superalloys. Considering the partitioning of alloying elements into coexisting phases (strengthening phases, carbides, etc.), the equilibrium composition of a high-angle GB was computed for several nickel-base superalloys using a calculation of phase diagrams database. The computational results showed that B and Mo were enriched at the GB in most of the investigated alloys. The creep rupture strengths of the investigated alloys were predicted using the computed GB composition as a regression model feature. The regression coefficients for the features confirm that B segregation at the GB has a non-negligible strengthening effect on nickel-base superalloys.

Artificial intelligence inspired design of non-isothermal aging for γ-γ' two-phase, Ni-Al alloys.

In this paper, a state-of-the-art Artificial Intelligence (AI) technique is used for a precipitation hardening of Ni-based alloy to predict more flexible non-isothermal aging (NIA) and to examine the possible routes for the enhancement in strength that may be practically achieved. Additionally, AI is used to integrate with Materials Integration by Network Technology, which is a computational workflow utilized to model the microstructure evolution and evaluate the 0.2% proof stress for isothermal aging and NIA. As a result, it is possible to find enhanced 0.2% proof stress for NIA for a fixed time of 10 min compared to the isothermal aging benchmark. The entire search space for aging scheduling was ~ 3 billion. Out of 1620 NIA schedules, we succeeded in designing the 110 NIA schedules that outperformed the isothermal aging benchmark. Interestingly, it is found that early-stage high-temperature aging for a shorter time increases the γ' precipitate size up to the critical size and later aging at lower temperature increases the γ' fraction with no anomalous change in γ' size. Therefore, employing this essence from AI, we designed an optimum aging route in which we attained an outperformed 0.2% proof stress to AI-designed NIA routes.

A Generalized Framework of Multifidelity Max-Value Entropy Search Through Joint Entropy.

Bayesian optimization (BO) is a popular method for expensive black-box optimization problems; however, querying the objective function at every iteration can be a bottleneck that hinders efficient search capabilities. In this regard, multifidelity Bayesian optimization (MFBO) aims to accelerate BO by incorporating lower-fidelity observations available with a lower sampling cost. In our previous work, we proposed an information-theoretic approach to MFBO, referred to as multifidelity max-value entropy search (MF-MES), which inherits practical effectiveness and computational simplicity of the well-known max-value entropy search (MES) for the single-fidelity BO. However, the applicability of MF-MES is still limited to the case that a single observation is sequentially obtained. In this letter, we generalize MF-MES so that information gain can be evaluated even when multiple observations are simultaneously obtained. This generalization enables MF-MES to address two practical problem settings: synchronous parallelization and trace-aware querying. We show that the acquisition functions for these extensions inherit the simplicity of MF-MES without introducing additional assumptions. We also provide computational techniques for entropy evaluation and posterior sampling in the acquisition functions, which can be commonly used for all variants of MF-MES. The effectiveness of MF-MES is demonstrated using benchmark functions and real-world applications such as materials science data and hyperparameter tuning of machine-learning algorithms.

Development of a prototype thermodynamic database for Nd-Fe-B permanent magnets.

For the Nd-Fe-B permanent magnets, a prototype thermodynamic database of the 8-element system (Nd, Fe, B, Al, Co, Cu, Dy, Ga) was constructed based on literature data and assessed parameters in the present work. The magnetic excess Gibbs energy of the Nd2Fe14B compound was reassessed using thoroughly measured heat capacity data. The Dy-Nd binary system was reassessed based on formation energies estimated from ab initio calculations. The constructed database was applied successfully for estimations of phase equilibria during the grain boundary diffusion processes (GBDP) and the reactions in the hydrogenation decomposition desorption recombination (HDDR) processes.

Computational thermodynamics and microstructure simulations to understand the role of grain boundary phase in Nd-Fe-B hard magnets.

To control the coercivity of Nd hard magnets efficiently, the thermal stability of constituent phases and the microstructure changes observed in hard magnets during thermal processes should be understood. Recently, the CALPHAD method and phase-field method have been recognized as promising approaches to realize phase stability and microstructure developments in engineering materials. Thus, we applied these methods to understand the thermodynamic feature of the grain boundary phase and the microstructural developments in Nd-Fe-B hard magnets. The results are as follows. (1) The liquid phase is a promising phase for covering the Nd2Fe14B grains uniformly. (2) The metastable phase diagram of the Fe-Nd-B ternary system suggests that the tie line end of the liquid phase changes drastically depending on the average composition of Nd. (3) The Nd concentration in the grain boundary phase can reach 100 at% if the volume fraction of the grain boundary phase is constrained. (4) The effect of Cu addition to the Nd-Fe-B system on the microstructural morphology is reasonably modeled based on the phase-field method. (5) The morphology of the liquid phase can be controlled using phase separation in the liquid phase and the grain size of the Nd2Fe14B phase.

Estimation of material parameters based on precipitate shape: efficient identification of low-error region with Gaussian process modeling.

In this study, an efficient method for estimating material parameters based on the experimental data of precipitate shape is proposed. First, a computational model that predicts the energetically favorable shape of precipitate when a d-dimensional material parameter (x) is given is developed. Second, the discrepancy (y) between the precipitate shape obtained through the experiment and that predicted using the computational model is calculated. Third, the Gaussian process (GP) is used to model the relation between x and y. Finally, for identifying the "low-error region (LER)" in the material parameter space where y is less than a threshold, we introduce an adaptive sampling strategy, wherein the estimated GP model suggests the subsequent candidate x to be sampled/calculated. To evaluate the effectiveness of the proposed method, we apply it to the estimation of interface energy and lattice mismatch between MgZn2 ([Formula: see text]) and α-Mg phases in an Mg-based alloy. The result shows that the number of computational calculations of the precipitate shape required for the LER estimation is significantly decreased by using the proposed method.

Data assimilation for massive autonomous systems based on a second-order adjoint method.

Data assimilation (DA) is a fundamental computational technique that integrates numerical simulation models and observation data on the basis of Bayesian statistics. Originally developed for meteorology, especially weather forecasting, DA is now an accepted technique in various scientific fields. One key issue that remains controversial is the implementation of DA in massive simulation models under the constraints of limited computation time and resources. In this paper, we propose an adjoint-based DA method for massive autonomous models that produces optimum estimates and their uncertainties within reasonable computation time and resource constraints. The uncertainties are given as several diagonal elements of an inverse Hessian matrix, which is the covariance matrix of a normal distribution that approximates the target posterior probability density function in the neighborhood of the optimum. Conventional algorithms for deriving the inverse Hessian matrix require O(CN^{2}+N^{3}) computations and O(N^{2}) memory, where N is the number of degrees of freedom of a given autonomous system and C is the number of computations needed to simulate time series of suitable length. The proposed method using a second-order adjoint method allows us to directly evaluate the diagonal elements of the inverse Hessian matrix without computing all of its elements. This drastically reduces the number of computations to O(C) and the amount of memory to O(N) for each diagonal element. The proposed method is validated through numerical tests using a massive two-dimensional Kobayashi phase-field model. We confirm that the proposed method correctly reproduces the parameter and initial state assumed in advance, and successfully evaluates the uncertainty of the parameter. Such information regarding uncertainty is valuable, as it can be used to optimize the design of experiments.

Development of an environmentally acceptable volatile organic compounds recovery and reuse system based on vacuum spray flash with dibasic acid esters as absorbents.

We used dibasic acid esters as the absorbent for a volatile organic compounds (VOC) recovery and reuse system geared mainly toward VOCs from painting and presswork. The system consists of three elements: absorption using dibasic acid esters, separation by vacuum spray flash method, and recovery by condensation. The concentration of both ethyl acetate (EtAc) and isopropyl alcohol (IPA) decreased around 90% at room temperature. Vacuum spray flash was found as a useful process for cost-effective and energy-effective VOC removal system. The difference in the dissolubility parameters (Hansen constant) between dibasic acid esters and VOCs and the boiling point of VOCs was the main contributor to the separation behavior.

Phase-field modeling of microstructure evolutions in magnetic materials.

Recently, the phase-field method has been extended and utilized across many fields of materials science. Since this method can incorporate, systematically, the effect of the coherency induced by lattice mismatch and the applied stress as well as the external electrical and magnetic fields, it has been applied to many material processes including solidification, solid-state phase transformations and various types of complex microstructure changes. In this paper, we focus on the recent phase-field simulations of real magnetic materials, and the simulation method for magnetic materials is explained comprehensively. Several applications of the phase-field method to clarifying the microstructure changes in magnetic materials, such as Ni2MnGa ferromagnetic shape memory alloy, FePt nanogranular thin film, Co-Sm-Cu rare-earth magnet, Fe-Cr-Co spinodal magnet, and Fe-C steel with external magnetic field, are demonstrated. Furthermore, the general concept of the effective strategy for controlling microstructure in magnetic materials is proposed.