Bohuslav Mašek

The concept of digital twinning is essential for smart manufacturing and cyber-physical systems to be connected to the Metaverse. These digital representations of physical objects can be used for real-time analysis, simulations, and predictive maintenance. A combination of smart manufacturing, Industry 4.0, and the Metaverse can lead to sustainable productivity in industries. This paper presents a practical approach to implementing digital twins of a magnetic forging holder that was designed and manufactured in this project. Thus, this paper makes two important contributions: the first contribution is the manufacturing of the holder, and the second significant contribution is the creation of its digital twin. The holder benefits from a special design and implementation, making it a user-friendly and powerful tool in materials research. More specifically, it can be employed for the thermomechanical influencing of the structure and, hence, the final properties of the materials under development. In addition, this mechanism allows us to produce a new type of creep-resistant composite material based on Fe, Al, and Y. The magnetic forging holder consolidates the powder material to form a solid state after mechanical alloying. We produce bars from the powder components using a suitable forging process in which extreme grain coarsening occurs after the final heat treatment. This is one of the conditions for achieving very high resistance to creep at high temperatures.

Digital twins are the most important parts of Cyber-Physical Systems (CPSs), and play a crucial role in the realization of the Metaverse. Therefore, two important factors: flexibility and adaptability, need to be focused on digital twinning systems. From a virtual perspective, constructing buildings, structures, and mechanisms in the Metaverse requires digital materials and components. Hence, accurate and reliable digital models can guarantee the success of implementation, particularly when it comes to completing physical twins in the real world. Accordingly, four Machine Learning (ML) methods to make digital twins of an advanced 42SiCr alloy considering all of its uncertainties and non-linearities have been employed in this paper. These ML methods accelerate the digitalization of the proposed alloy and allow users to employ them for a wide range of similar metals. Based on this technique, producers can borrow these virtual materials and build their structures in the Metaverse. This way, if the properties of the materials were satisfactory, they might buy them and start manufacturing their products. As a case study, we focus on digital twining of an 42SiCr steel with some influential factors in its mechanical properties, making the nature of the alloy complex. Processes, including heat treatment, may restore the material’s deformability; however, Quenching and Partitioning (Q&P) not only eliminates the impact of cold forming but also provides advanced high-strength steel (AHSS) properties. In this research, the combined impacts of different Q&P treatments were investigated on the mechanical properties of 42SiCr steel alloy. The results have shown the acceptability and accuracy of the proposed ML methods in realizing the digital twins of this complex alloy.

A series of experiments was conducted by quenching and partitioning (Q-P) heat-treated alloys to investigate the effect of cooling intensity on the mechanical properties of low alloy steel 42SiCr. By applying a conventional heat treatment, reasonable high strength can be achieved; however, the alloys become more brittle. To obtain an optimal balance, advanced heat treatment methods like the Q-P process can be used. It consists of quenching to temperatures between martensite start and martensite finish temperatures and holding, which leads to the stabilization of untransformed austenite by carbon partitioning. The martensitic microstructure is then formed with a small volume fraction of retained austenite embedded on a microscopic scale. The material’s deformability can be significantly improved by using such heat treatment processes. Moreover, to improve advanced high strength properties (AHSS), an additional Q-P process can be applied, which leads to erasing the influence of cold forming as well as enhancement of the mechanical properties. Several combinations of the Q-P process with/without partitioning were performed with various cooling rates for both heat treatment methods. Ultimate Tensile Strength (UTS), Ductility and Hardness (HV10), as well as the microstructure of the alloys, are compared to evaluate the cooling intensity effects. The cooling rate is found not to be a significant factor influencing mechanical properties, which is a crucial point for practical material heat treatment.

The use of asphaltic concrete cores for sealing embankments and rockfill dams is very important. The self-healing properties of bitumen, simple construction in cold and rainy conditions compared to clay cores, good flexibility and connection with embankment materials are the essential characteristics of asphaltic concrete. The main concern regarding the use of asphaltic concrete cores in Iran is mainly the performance of these dams under seismic loads. The evaluations of the performance of these types of dams in other countries show that asphaltic concrete cores perform satisfactorily in the static state, but in earthquake conditions, the situation may be different. In this paper, the static and seismic behavior of the Meijaran dam in Iran, Mazandaran, is evaluated for three core angles of 90°, 60° and 45°. This evaluation was conducted at the end of the impounding stage and after applying seismic loads using FLAC 2D software and Mohr–Coulomb consitutive models. The results were matched with the ICOLD recommendation to use angled cores in dams with asphaltic cores and showed that the dam performs better with angled cores. Finally, for the Meijaran dam, the results from the dynamic analysis are compared with the results from the centrifuge test.

Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets.