Al 7068-T651 alloy is one of the recently developed materials used mostly in the defense industry due to its high strength, toughness and low weight compared steels. The aim of this study is to identify the Johnson-Cook (J-C) material model parameters, the accurate Johnson-Cook (J-C) damage parameters, D₁, D₂ and D₃ of the Al 7068-T651 alloy for Finite Elemental Analysis (FEA) based simulation techniques, together with other damage parameters, D₄ and D₅. In order to determine D₁, D₂ and D₃ tensile tests were conducted on notched and smooth specimens at medium strain rate, s⁻¹, and tests were repeated 7 times to ensure the consistency of the results both in the rolling direction and perpendicular to the rolling direction. To determine D₄ and D₅, further tensile tests were conducted on specimens at high strain rate (10² s⁻¹) and temperature (300 °C) by means of Gleeble Thermal-mechanical Physical Simulation system. The final areas of fractured specimens were calculated through optical microscopy. The effects of stress triaxiality factor, rolling direction, strain rate and temperature on the mechanical properties of Al 7068-T651 alloy were also investigated. Damage parameters were calculated via Levenberg-Marquardt optimization method. From all the aforementioned experimental work, J-C material model parameters were also determined. In this article, J-C damage model constants, based on maximum and minimum equivalent strain values, were also reported which can be utilized for the simulation of different applications.

The mechanical properties of AlSi10Mg alloy fabricated by laser powder bed fusion (LPBF) were investigated under different strain rates and post-processing conditions, including shot peening (SP) and stress relief (SR). Tensile tests were performed at quasistatic (0.1 s ⁻¹ ) and dynamic (0.015 s ⁻¹ ) strain rates on as-built and post-processed specimens. The results revealed that SP significantly increased the yield strength by 7.10 %, reaching 249.59 MPa, due to the induced compressive residual stresses. However, while SP slightly improved the ultimate tensile strength (UTS) by 0.25 %, it also reduced elongation at break by 18.06 %, indicating a trade-off between strength and ductility. Conversely, SR improved ductility by reducing internal stresses, leading to an elongation at break increase of 574.01 %, with a slight reduction in yield strength. The combination of SP and SR exhibited a synergistic effect, achieving a balance between strength and ductility. Strain rate sensitivity (SRS) analysis indicated that stress-relieved specimens performed better under dynamic loading conditions. These findings highlight the potential of post-processing techniques in tailoring the mechanical behavior of LPBF-produced AlSi10Mg alloys. The balanced properties achieved through combined treatments make this material particularly suitable for high-performance aerospace and automotive applications, where strength and ductility are critical under varying operational conditions.

Hydrogen embrittlement (HE) poses a critical safety risk in the aviation industry, causing unexpected failures in high-strength metals and alloys used in essential components. This study focuses on detailed analyses of multiple aviation incidents and accidents where HE played a significant role, particularly concerning failures in helicopter drive systems and fixed-wing aircraft crankshaft bolts. Investigations revealed that these failures were primarily caused by hydrogen introduced during manufacturing processes, such as electroplating and surface treatments, which were insufficiently mitigated by post-processing techniques like dehydrogenation. These incidents led to Airworthiness Directives (ADs), which mandated corrective actions to prevent similar occurrences, such as improved manufacturing protocols and enhanced material testing. By presenting concrete evidence from metallurgical analyses and documented aviation failures, this paper highlights the critical need for robust manufacturing controls, stringent post-treatment procedures, and continuous monitoring to address hydrogen embrittlement risks effectively and enhance aviation safety.

Hydrogen embrittlement (HE) poses a significant risk in the aviation industry, leading to unexpected failures in high-strength metals and alloys used in critical components. This paper investigates multiple aviation incidents where hydrogen embrittlement contributed, notably in helicopter drive systems and fixed-wing aircraft crankshaft bolts. The failures were attributed mainly to hydrogen introduced during manufacturing processes, particularly electroplating and surface treatments, which left residual hydrogen despite dehydrogenation procedures. These incidents led to the issuance of Airworthiness Directives (ADs), which mandated stricter manufacturing controls and post-processing treatments, such as baking, to eliminate residual hydrogen and prevent future embrittlement. While the paper briefly covers traditional mitigation strategies like cathodic protection and surface coatings, its primary focus is on the need for improved quality control in manufacturing processes to reduce hydrogen-related failures, thereby enhancing the safety and reliability of aviation systems.

Shot peening is a crucial surface treatment technique that significantly improves the mechanical properties of metallic components, particularly their fatigue resistance and ability to withstand corrosion cracking. This study aims to optimize the shot peening process for aviation applications by evaluating and comparing various mathematical modeling and optimization techniques. Seven mathematical models were analyzed using a neuro-regression method (NRM), among which the second-order trigonometric non-linear (SOTN) model exhibited the highest reliability, achieving R² values of 0.93 and 0.90 for training and testing datasets, respectively. To improve the model’s robustness, four optimization algorithms – differential evolution (DE), simulated annealing (SA), Nelder–Mead (NM), and random search (RS) – were applied to the SOTN model. Although each technique offered valuable insights, performance fluctuations across different intensity ranges necessitated the development of a hybrid optimization model that combines the strengths of all four methods. The hybrid model achieved a mean error of approximately 2.69 %, outperforming individual approaches and demonstrating strong potential for reliable shot peening optimization across a wide range of target intensities. These findings provide a comprehensive methodology for AI-based optimization of surface treatment processes in engineering applications.