Optimization of cooling rate of Q-P treated 42SiCr steel using AI digital twinning.

In the continuously advancing field of mechanical engineering, digitalization is bringing a major transformation, specifically with the concept of digital twins. Digital twins are dynamic digital models of real-world systems and processes, crucial for Industry 4.0 and the emerging Industry 5.0, which are changing how humans and machines work together in manufacturing. This paper explores the combination of physics-based and data-driven modeling using advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques. This approach provides a comprehensive understanding of mechanical systems, improving materials design and manufacturing processes. The focus is on the advanced 42SiCr alloy, where AI-driven digital twinning is used to optimize cooling rates during Quenching and Partitioning (Q-P) treatments. This leads to significant improvements in the mechanical properties of 42SiCr steel. Given its complex properties influenced by various factors, this alloy is perfect for digital twinning. The Q-P heat treatment process not only restores the material's deformability but also gives it advanced high-strength steel (AHSS) properties. The findings show how AI and ML can effectively guide the development of high-strength steels and enhance their treatment processes. Additionally, integrating digital twins with new technologies like the Metaverse offers exciting possibilities for simulated production, remote monitoring, and collaborative design. By establishing a clear workflow from physical to digital twins and presenting empirical results, this paper connects theoretical modeling with practical applications, paving the way for smarter manufacturing solutions in mechanical engineering. Furthermore, this paper analyzes how digital twins can be integrated into advanced technologies like the Metaverse, opening up new possibilities for simulated production, remote monitoring, design collaboration, training simulations, analytics, and complete supply chain visibility. This integration is a crucial step toward realizing the full potential of digitalization in mechanical engineering.

Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys.

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.

The Effect of Heat Treatment on the Tribological Properties and Room Temperature Corrosion Behavior of Fe-Cr-Al-Based OPH Alloy.

The microstructure, mechanical, tribological, and corrosion properties of Fe-Cr-Al-Y-based oxide-precipitation-hardened (OPH) alloy at room temperature are presented. Two OPH alloys with a composition of 0.72Fe-0.15Cr-0.06Al-0.03Mo-0.01Ta-0.02Y2O3 and 0.03Y2O3 (wt.%) were prepared by mechanical alloying with different milling times. After consolidation by hot rolling, the alloys presented a very fine microstructure with a grain size of approximately 180 nm. Such a structure is relatively brittle, and its mechanical properties are enhanced by heat treatment. Annealing was performed at three temperatures (1000 °C, 1100 °C, and 1200 °C), with a holding time from 1 to 20 h. Tensile testing, wear testing, and corrosion testing were performed to evaluate the effect of heat treatment on the behavior and microstructural properties. The grain size increased almost 10 times by heat treatment, which influenced the mechanical properties. The ultimate tensile strength increased up to 300% more compared to the initial state. On the other hand, heat treatment has a negative effect on corrosion and wear resistance.