Integrated Computational Materials Engineering (ICME) is a set of methodologies utilized by researchers and engineers assisting the study of material behaviour during production processes and/or service. ICME aligns with societal efforts for the twin green and digital transitions while improving the sustainability and cost efficiency of relevant products/processes. A significant link of the ICME chain, especially for metallic materials, is the crystal plasticity (CP) formulation. This review examines firstly the progress CP has made since its conceptualization and secondly the relevant thematic areas of its utilization and portraits them in a concise and condensed manner. CP is a proven tool able to capture complex phenomena and to provide realistic results, while elucidating on the material behaviour under complex loading conditions. To this end, a significant number of formulations falling under CP, each with their unique strengths and weaknesses, is offered. It is a developing field and there are still efforts to improve the models in various terms. One of the biggest struggles in setting up a CP simulation, especially a physics-based one, is the definition of the proper values for the relevant parameters. This review provides valuable data tables with indicative values.

The research and development of high-entropy alloys (HEAs) and complex-concentrated alloys (CCAs) are growing rapidly, focusing on the enhanced properties of these alloys. However, so far, their manufacturing has not exceeded the laboratory scale. To meet this challenge, a combination of the processing characteristics and methods along with their sustainable production must be ensured. Moving towards a circular economy, this includes the utilization of low-cost, widely available scrap for the manufacturing of CCAs. Changing the raw materials, can ensure a cost-efficient production and paves the way to surpass major limitations in the industrial manufacturing of CCAs. Examples of a novel lightweight CCA design approach will be presented in this work.

Optimum mechanical behavior is achieved by means of controlling microstructural anisotropy. The latter is directly related to the crystallographic texture and is considerably affected by thermal and mechanical processes. Therefore, understanding the underlying mechanisms relating to its evolution during thermomechanical processing is of major importance. Towards that direction, an attempt to identify possible correlations among significant microstructural parameters relating to texture response during deformation was made. For this purpose, a 3104 aluminum alloy sheet sample (0.5 mm) was examined in the following states: (a) cold rolled (with 90% reduction), (b) recovered and (c) fully recrystallized. Texture, anisotropy as well as the mechanical properties of the samples from each condition were examined. Afterwards, samples were subjected to uniaxial loading (tensile testing) while the most deformed yet representative areas near the fractured surfaces were selected for further texture analysis. Electron backscatter diffraction (EBSD) scans and respective measurements were conducted in all three tensile test directions (0°, 45° and 90° towards rolling direction (RD)) by means of which the evolution of the texture components, their correlation with the three selected directions as well as the resulting anisotropy were highlighted. In the case of the cold-rolled and the recovered sample, the total count of S2 and S3 components did not change prior to and after tensile testing at 0° towards RD; however, the S2 and S3 sum mostly consisted of S3 components after tensile testing whereas it mostly consisted of S2 components prior to tensile testing. In addition, the aforementioned state was accompanied by a strong brass component. The preservation of an increased amount of S components, and the presence of strain-free elongated grains along with the coexistence of a complex and resistant-to-crack-propagation substructure consisting of both high-angle grain boundaries (HAGBs) and subgrain boundaries (SGBs) led into an optimal combination of Δr and r_m parameters.

: Predicting the mechanical response of industrial alloys is crucial for optimizing manufacturing processes and improving material performance. Traditional, solely experimental approaches, though effective, are inefficient as they are resource-intensive, requiring extensive laboratory testing and the iterative calibration of processing conditions. These costs can be avoided through computational/virtual experiments based on a multiscale hierarchical framework that integrates macroscopic approaches, mesoscale modelling as well as atomic level and advanced thermodynamical simulations to study and predict the mechanical response of metallic systems. In the context of this work, a framework for studying the effect of forming on metallic materials is proposed, applied, and validated on the hot extrusion of AA6063. Coupling thermodynamic simulations (including Phase Field) results with literature data establishes a microstructurally accurate representative volume element (RVE) design. This way, the phase fraction and the grain size of the RVE are determined by thermodynamic simulations (ThermoCalc, MICRESS), which can be validated via microstructure characterization. It is known that the mechanical properties of the individual phases affect the macroscopical properties of the material. Using atomic level simulations (i.e., molecular dynamics), the dislocation density of the material is calculated and utilized as an input for a Crystal Plasticity Fast Fourier Transformation simulation. This iterative process can be applied to match all stages of manufacturing processes. The hierarchical and systematic integration of these computational methodologies enables a rigorous analysis of the effect that processing parameters have on the microstructure. This work contributes to the broader effort of creating experiment-free workflows for designing materials and processes by leveraging a multiscale modeling approach. Coupled with experimental data, the predictive accuracy of the mechanical behavior can be further enhanced.

The rapid penetration of Artificial Intelligence (AI) and all related developments of the fourth industrial revolution is paving the way for a more sophisticated production sequence that strives for higher quality, lower emissions and lower cost production. This work reviews and discusses these developments and correlates them with state-of-the-art changes in materials engineering. We highlight penetration paradigms of modern computation tools. These technologies sound very promising in terms of maximizing the production efficiency of modern industries and, thus, minimizing the required energy input, greenhouse gas emissions and leading the way to a more ecofriendly economy.

Brass alloys are critical for numerous modern applications. However, a significant knowledge gap exists regarding the impact of impurities on their processability, particularly regarding hot cracking susceptibility. This issue is exacerbated by increasing recycling rates, leading to a higher concentration of impurities in the alloy pool. This study employed computational thermodynamics to assess the impact of common impurities in brasses. Based on the results, the combined impact of impurities and alloying content significantly shifted the α and β solvus to the left, the α solvus indicatively is shifted by approximately 10% at low temperatures, and introduced multiple solvus and liquidus lines, crucial for the hot formability of the alloy. These preliminary results provide insight on the need for process optimization, considering the possibly deleterious effects of some elements to minimize the risk of hot cracking, a step towards enhancing the reliability and sustainability of these alloys.