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Thermal distribution during the sapphire growth process determines to a great extent the thermal stresses and dislocation density in sapphire. In this work, thermal and defect simulations of sapphire growth in a simplified single-boule furnace are presented. The heat transfer in the furnace is modeled via ANSYS Fluent® by considering conduction, convection and radiation effects. A dislocation density-based crystal plasticity model is applied for the numerical simulation of dislocation evolution during the crystal growth of sapphire. The physical models are validated by using a temporal series of measurements in the real furnace geometry, which capture the crystal–melt interface position during the technological growth process. The growth rate and the shape of the crystal growth front are analyzed for different side and top heater powers which result in different thermal distributions in the furnace. It is found that the cooling flux at the crucible bottom wall determines to a great extent the growth profile in the first half of the growth stage. Only toward the end of the growth stage, different top and side power distributions induce different growth front shapes. The effect of the convexity of the growth surface on the generation of dislocation defects is investigated by the crystal plasticity model. The results of simulations show that the convexity of the growth surface has a significant effect on the generation of dislocations. 

Thermoset polymer composite structures are heavily used in the aerospace, defense, transport, and energy sectors due to their lightweight and high-performance behavior. Thermoset polymer resins require external heat for manufacturing/curing. The behavior of these polymer composite materials is highly dependent on curing process as it affects evolution of material properties as well as residual stresses and deformation. Various cure process parameters, mainly related to cure thermal cycle, need to be optimized to get the desired properties of these structures. In this paper, the polymer cure process is explicitly modeled through finite element method. Its effects at the structural level are captured by modeling thermo-chemical-mechanical analysis through multiple length scales. The multi-scale analysis is carried out by surrogate models to reduce run time. In this study, non-dominated sorting genetic algorithm II is used for multi-objective cure process optimization. The objectives are to minimize the spring-in angle and minimize the process time with achieving degree of cure above given requirement. Insights from such optimization can be utilized by product designers as well as manufacturers to take timely decisions to improve the performance of these composite structures. 

To optimize and extend the service life of polymeric materials in outdoor environments, a domain knowledge-based and data-driven approach was utilized to quantitatively investigate the temporal evolution of degradation modes, mechanisms, and rates under various stepwise accelerated exposure conditions. Six formulations of poly(methyl methacrylate) (PMMA) with different combinations of stabilizing additives, including one unstabilized formulation, were exposed in three accelerated weathering conditions. Degradation was dependent on wavelength as samples in UV light at 340 nm (UVA) exposure showed the most yellowing. The unstabilized PMMA formulation showed much higher yellowness index values (59.5) than stabilized PMMA formulations (2–12). Urbach edge analysis shows a shift toward longer wavelength from 285 to 500 nm with increasing exposure time and an increased absorbance around 400 nm of visible region as the unstabilized samples increase in yellowing. The degradation mechanisms of PMMA were tracked using induced absorbance to dose at specific wavelengths that correspond to known degradation mechanisms. The degradation pathway of PMMA was modeled in a framework using network structural equation modeling (netSEM). netSEM showed changes in degradation pathway as PMMA transition stages of degradation 

High-throughput screening (HTS) can significantly accelerate the design of new materials, allowing for automatic testing of a large number of material compositions and process parameters. Using HTS in Integrated Computational Materials Engineering (ICME), the computational evaluation of multiple combinations can be performed before empirical testing, thus reducing the use of material and resources. Conducting computational HTS involves the application of high-throughput computing (HTC) and developing suitable tools to handle such calculations. Among multiple ICME methods compatible with HTS and HTC, the calculation of phase diagrams known as the CALPHAD method has gained prominence. When combining thermodynamic modeling with kinetic simulations, predicting the entire history of precipitation behavior is possible. However, most reported CALPHAD-based HTS frameworks are restricted to thermodynamic modeling or not accessible. The present work introduces CAROUSEL—an open-sourCe frAmewoRk fOr high-throUghput microStructurE simuLations. It is designed to explore various alloy compositions, processing parameters, and CALPHAD implementations. CAROUSEL offers a graphical interface for easy interaction, scripting workflow for advanced simulations, the calculation distribution system, and simulation data management. Additionally, CAROUSEL incorporates visual tools for exploring the generated data and integrates through-process modeling, accounting for the interplay between solidification and solid-state precipitation. The application area is various metal manufacturing processes where the precipitation behavior is crucial. The results of simulations can be used in upscale material models, thus covering different microstructural phenomena. The present work demonstrates how CAROUSEL can be used for additive manufacturing (AM), particularly for investigating different chemical compositions and heat treatment parameters (e.g., temperature, duration 

A simple Gaussian process regressor (GPR) model is employed to predict steel hardness and toughness response for tempered martensitic steels. A dataset of over 2000 hardness values from over 250 distinct alloys was compiled, with the aim of incorporating a diverse set of quenched and tempered martensitic steels. The Izod impact toughness was included for over 450 of these alloy/temper conditions. The GPR exhibited an increase in accuracy for both the predicted hardness and Izod impact toughness over linear regression trained on the same dataset. Shapley additive explanations (SHAP) were used to assess the importance of the input features of tempering temperature, tempering time, and 15 elements. Tempering temperature and carbon content were the most important input features in all models. The relative importance of the other 14 alloying elements varied depending on the target property. The SHAP analysis highlighted the complex relationships between composition and mechanical properties that are able to be captured by machine learning approaches. 

Developing accurate process–structure–property models for metal additive manufacturing is crucial due to the numerous process parameters, extended build times, and high material costs which make it impractical to rely solely on an experimental trial and error approach when optimizing the process. In this work, a multiscale digital approach to estimate tensile anisotropy along selective laser melted titanium meta-stable alloys is presented. The approach uses a component scale thermal FEA model of the process to calculate temperature, a meso-scale phase field model to calculate microstructure evolution, and a microscale crystal plasticity model to calculate the effect of texture on the tensile properties in different directions. The model has predicted isotropic yield strength for this material, which could guide designers to choose orientations freely. However, anisotropy in hardening behavior could be expected but is caused by porosity and cracking, which are not considered in the presented models. We believe the presented approach, which relies solely on easy to use commercial simulation tools, lays a good foundation for the development of process–structure–property models to optimize process parameters. The modeling approach should be applicable to other mechanical properties and materials with appropriate considerations. 

Phase transformations in materials systems can be tracked using atomic force microscopy (AFM), enabling the examination of surface properties and macroscale morphologies. In situ measurements investigating phase transformations generate large datasets of time-lapse image sequences. The interpretation of the resulting image sequences, guided by domain-knowledge, requires manual image processing using handcrafted masks. This approach is time-consuming and restricts the number of images that can be processed. In this study, we developed an automated image processing pipeline which integrates image detection and segmentation methods. We examine five time-series AFM videos of various fluoroelastomer phase transformations. The number of image sequences per video ranges from a hundred to a thousand image sequences. The resulting image processing pipeline aims to automatically classify and analyze images to enable batch processing. Using this pipeline, the growth of each individual fluoroelastomer crystallite can be tracked through time. We incorporated statistical analysis into the pipeline to investigate trends in phase transformations between different fluoroelastomer batches. Understanding these phase transformations is crucial, as it can provide valuable insights into manufacturing processes, improve product quality, and possibly lead to the development of more advanced fluoroelastomer formulations.