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

Effects of Shot Peening and Cavitation Peening on Properties of Surface Layer of Metallic Materials-A Short Review

Shot peening is a dynamically developing surface treatment used to improve the surface properties modified by tool, impact, microblasting, or shot action. This paper reviews the basic information regarding shot peening methods. The peening processes and effects of the shot peening and cavitation peening treatments on the surface layer properties of metallic components are analysed. Moreover, the effects of peening on the operational performance of metallic materials are summarized. Shot peening is generally applied to reduce the surface roughness, increase the hardness, and densify the surface layer microstructure, which leads to work hardening effects. In addition, the residual compressive stresses introduced into the material have a beneficial effect on the performance of the surface layer. Therefore, peening can be beneficial for metallic structures prone to fatigue, corrosion, and wear. Recently, cavitation peening has been increasingly developed. This review paper suggests that most research on cavitation peening omits the treatment of additively manufactured metallic materials. Furthermore, no published studies combine shot peening and cavitation peening in one hybrid process, which could synthesize the benefits of both peening processes. Moreover, there is a need to investigate the effects of peening, especially cavitation peening and hybrid peening, on the anti-wear and corrosion performance of additively manufactured metallic materials. Therefore, the literature gap leading to the scope of future work is also included.

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.