Review of “Integrated Computer-Aided Process Engineering Session in the 17th International Symposium on Novel and Nano Materials (ISNNM, 14–18 November 2022)”

Guzmán-Flores I., Granda-Gutiérrez E.E., Cruz-González C.E., Hernández-García H.M., Díaz-Guillén J.C., Flores-González L., Praga-Alejo R.J., Martínez-Delgado D.I.

This research investigates the heat treatment parameters of 6061-aluminum alloy to enhance its mechanical properties. The Taguchi design-of-experiments (DOE) method was employed to systematically examine the effects of solutionizing temperature, solutionizing time, aging temperature, and aging time on the tensile strength of the alloy. Mechanical testing suggested a major influence of solutionizing and aging temperatures on the ultimate tensile strength of the alloy. The samples subjected to a solutionizing temperature of 540 °C for 3 h, followed by aging at 170 °C for 18 h, exhibited the highest ultimate tensile strength (293.7 MPa). Conversely, the samples processed at the lowest levels of these parameters displayed the lowest ultimate tensile strength (193.7 MPa). Microstructural analysis confirmed the formation of equiaxed grains, strengthening precipitates, precipitate clusters, and β (Mg2Si) precipitates alongside Fe-Al-Si dispersoids. Energy-dispersive X-ray spectroscopy (EDS) analysis detected the presence of elemental precursors of β phase (Al-Mg-Si) and dispersoid-forming elements (Al-Fe-Si). X-ray diffraction spectroscopy (XRD) analysis revealed the persistence of the β phase in the alloy, indicating its contribution to the improved mechanical properties, which are mainly obtained by aging precipitation phases. Fracture analysis showed a ductile fracture mechanism, and examining fractured samples supported the findings of enhanced tensile properties resulting from the adequate selection of heat treatment parameters. We employed ANOVA (analysis of variance) to analyze the DOE results, using a multiple regression model to express the ultimate tensile strength of the alloy in terms of the variables used in the design. This yielded an adjusted coefficient of determination of 89.75%, indicating a high level of explained variability in the test data for evaluating the model’s predictive capacity.

Zhou S., Yang B., Xiao S., Yang G., Zhu T.

Interpretable machine learning (ML) has become a popular tool in the field of science and engineering. This research proposed a domain knowledge combined with ML method to increase interpretability while ensuring the accuracy of ML models and verifies the generality of the ML approach in fatigue crack growth (FCG) modelling. LZ50 steel single edge notch tension (SENT) specimens were tested for short crack (SC) growth rate and microstructure characterization under various R-controls. Based on the test results, the SC growth process was divided into 3 stages: microstructural short crack (0–145 μm), physical short crack (145–1000 μm), and long crack (1000 μm–fracture). Following the analysis of 8 semi-empirical FSCG rate equations with different driving forces, 6 impact variables that may affect the FCG rate characteristics were identified. Random forest and Pearson correlation analysis were used to investigate the influence of each feature on the FCG rate and the relationships among the features. The main influential features for the short crack symbolic regression (SCSR) model were found to be |ΔK–ΔKat|, Δγxy, |a–at|, and eα(1−R). After considering these 4 input features, the predicted FSCG rate equation generated by the SR model has a concise mathematical structure. Finally, an elastic net multiple linear regression method was proposed to determine the parameters of the predicted equation, while retaining the physical characteristics of each parameter. The SCSR model for SC demonstrated good prediction performance on various metallic materials.

Choi J.Y., Xue T., Liao S., Cao J.

Phase-field (PF) modeling is a versatile physics-based computational method that has been used to simulate the evolution of microstructures. The PF method can produce accurate microstructures but suffers from a high computational cost, limiting its use in length scales relevant to additive manufacturing. Using small-scale PF simulations as training data, we trained a surrogate machine learning (ML) model as a computationally cheaper alternative. We use a three-dimensional (3D) U-Net convolutional neural network and learn microstructure evolution in a supervised fashion. With initial microstructure and thermal history as inputs, the ML model can predict the resulting grain orientations at a high accuracy compared to the PF model. Computationally, the ML model is orders of magnitudes faster than the direct PF simulation in a GPU implementation, and scales favorably with increasing number of cells. By spatio-temporally composing multiple ML model predictions at the small-scale, we demonstrate a large-scale 64-layer simulation of a 2 mm × 2 mm × 2 mm cube for a powder bed fusion additive manufacturing process. The ML results revealed a mixture of equiaxed, columnar, and curved grains, comparable to experimental observations. Microstructures resulting from different toolpath strategies, and lack of fusion defects are also demonstrated. This approach paves the way for microstructure-driven process design.

Sánchez-Ruiz F.J., Bedolla-Hernández M., Rosano-Ortega G., Bedolla-Hernández J., Schabes-Retchkiman P.S., Vega-Lebrún C.A., Vargas-Viveros E.

This article presents a computational numerical model for the simulation and analysis of quantum chemistry and Gibbs free energy theory using static (ANNS), dynamic (DANN), and chaotic neural networks (CANN). The model calculates the physical-surface mechanics of hardness, adhesion, and strength. They resulted in nanostructured metal coatings with electrodeposited chromium nanoparticles on low-carbon steel. The ANNS, DANN, and CANN simulations showed that model-obtained values for analyzed properties presented an approximation of 99 % concerning theoretical matters taken as base. Likewise, model accuracy was verified by comparison with reference data (datasheet). The proposed model is not limited to the analyzed case and provides consistent results for predicting surface physical-mechanical properties of electrodeposited coating-substrate arrangements, with a minimum error percentage of 1–1.5 % over learning.

Liang C., Yin Y., Wang W., Yi M.

Selective laser sintering (SLS) additive manufacturing possesses the characteristics of extremely non-uniform temperature distribution and high temperature gradient, which are unmanageable by the traditional isothermal models. In this work, we propose a thermodynamically consistent non-isothermal phase-field model (PFM) to investigate the microstructure evolution during SLS process with the full consideration of temperature gradient effect. The model is derived from a thermodynamic framework which invokes the microforce theory and Coleman–Noll procedure. The temperature-dependent parameters in the free energy formulation are determined by the experimentally measurable surface energy and grain boundary energy with a given interface width. Benchmark simulations of sintering two particles under temperature gradient indicate that the grain boundary has the trend to migrate to the higher temperature region, which is further verified by molecular dynamics simulations. This phenomenon cannot be captured by the traditional isothermal PFM. Grain boundary migrations can also be driven by curvature for two unequally-sized particles. Simulations on SLS of powder bed show the dependence of microstructure evolution and final porosity on the laser power and scanning speed. The developed non-isothermal PFM could be a practicable toolkit for the microstructure simulation of SLS process.

Wiangkham A., Aengchuan P., Sudtachat K., Ariyarit A., Srisuk S., Thammachot N.

The "Big Knife" or "Eto" is the local name for one of the popular types of knives in Thailand which has many applications. These knives are typically forged from car leaf spring steel that the villagers buy wholesale into the knife forging shop at the scale of a community industry. However, occasionally the materials used in knives are insufficient to meet their needs and there may be uncertainty because of various or unidentified sources. This study investigates the replacement of car leaf spring steel used in knife forging with commercial AISI 1010 low-carbon steel in order to solve the problem mentioned above. The low-carbon steel that was used as the replacement knife forging material was processed by the pack carburizing process with several types of energizers, including calcium carbonates, egg duck shells, cow bone, river snail shells, and golden apple snail shells under different conditions of temperature and time, and the properties in terms of hardness and impact tolerance were investigated. To make it easier to implement, the pack carburizing process conditions were optimized for hardness and impact properties via the NSGA-II multi-objective optimization algorithm with the Gaussian process regression model (GPR), which is one artificial intelligence algorithm, as a surrogate model. After the experiment, results clearly indicated the effect of heat treatment conditions (energizer type, temperature, and time) on the hardness and impact of treated AISI 1010 steel; moreover, the GPR model also shows a relatively high efficiency measured in various terms of performance metrics representing the behavior of hardness and impact that arise from the pack carburized process parameters.

Kahhal P., Ghorbani-Menghari H., Kim H., Choi H., Cha P., Kim J.H.

Joo G., Song Y., Kim M., Park S., Shin J., Choi S., Choi H., Kim S.

Park M., Jeon H.Y., Han S., Lee D.H., Lee Y.

Seo H., Lee H., Park H., Park S., Sung H.

Jeon J., Sung Y., Seo N., Jung J., Son S.B., Lee S.

Jeon J., Seo N., Jung J., Son S.B., Lee S.

Cho K., Lee S.

Jung D.H., Oh W.J., Kyeong J.S., Lee S.

Wang K., Lv S., Wu H., Wu G., Wang S., Gao J., Zhu J., Yang X., Mao X.

Solidification structure is a key aspect for understanding the mechanical performance of metal alloys, wherein composition and casting parameters considerably influence solidification and determine the unique microstructure of the alloys. By following the principle of free energy minimization, the phase-field method eliminates the need for tracking the solid/liquid phase interface and has greatly accelerated the research and development efforts geared toward optimizing metal solidification microstructures. The recent progress in the application of phase-field simulation to investigate the effect of alloy composition and casting process parameters on the solidification structure of metals is summarized in this review. The effects of several typical elements and process parameters, including carbon, boron, silicon, cooling rate, pulling speed, scanning speed, anisotropy, and gravity, on the solidification structure are discussed. The present work also addresses the future prospects of phase-field simulation and aims to facilitate the widespread applications of phase-field approaches in the simulation of microstructures during solidification.