Key Engineering Materials Vol. 1033

Abstract: As aerospace, nuclear engineering, and other high-technology industries continue to advance, the demands for material properties have grown more stringent. Strengthened via the intrinsic thermally stable element tungsten (W) with the ductile γ phase, tungsten heavy alloys (WHAs) have emerged as critical materials for extreme service conditions in multiple fields due to their high density, outstanding strength, radiation resistance, and excellent high-temperature performance. However, their application is limited by certain drawbacks such as high temperature/irradiation conditions, interfacial weakening and room-temperature brittleness. This study investigates the effect of annealing on the microstructural evolution and hardness behaviour of W-Ni-Fe and W-Ni-Cu alloys through the Vickers hardness tests, crystal structure, morphology and elemental distribution analysis. The W-Ni-Fe and W-Ni-Cu alloys exhibit remarkable hardness stability during annealing, with less affected by temperature, confirming the excellent thermal stability. W-Ni-Fe alloys exhibit softening phenomena during high-temperature annealing, while the W-Ni-Cu alloys demonstrate enhanced hardness through annealing. This discrepancy is originally caused by the differences in the diffusion behaviour of the two binder phases (NiFe/NiCu) at elevated temperatures and their distinct interfacial interaction mechanisms with the W matrix.

Abstract: This study investigates how heat treatment affects the mechanical properties and microstructure of extruded AA2017 aluminum alloy. Quenching (icy water vs. liquid nitrogen) and tempering (T6: 120–160°C; T7: 240°C) significantly alter hardness, tensile strength, and fatigue life. T6 promotes fine, coherent precipitates, enhancing strength and fatigue resistance, while T7 leads to over-aging and property degradation [X]. Icy water quenching improves fatigue life over liquid nitrogen by refining precipitates [Y]. Microstructural analysis reveals elastic adaptation (T6) and plastic shakedown (T7) as fatigue stabilization mechanisms, with fracture modes shifting from ductile (T6) to mixed ductile-brittle (T7) [Z]. These results optimize heat treatment for AA2017 in high-strength, fatigue-critical applications.

Abstract: In machining process, surface roughness and material removal rate have a vital importance since they affect mass production, consumption of energy, force, and tool life and product quality. In this study, Taguchi-Grey Relation Method (TGRM) is applied to AISI 1040 mild steels in the hardened form when machined with ceramic inserts using response surface methodology for multi-objective optimization. Grey-Relation Method and Pareto chart reveal that feed rate, depth of cut, speed besides square effect of speed/feed rate are effective parameters on the response. Among all eighteen experiments, trial twelfth provides the best multi-performance characteristics while the first experiment shows the worst performance. Optimal levels are determined at higher speed, higher feed rate associated with higher depth of cut. It is concluded that quadratic regression model and reduced quadratic regression model are developed. The correlation coefficients range from 98.3% to 96.89%, respectively. As a result, TGRM has an efficient to provide a good modelling in combination of surface roughness and metal removal rate.

Abstract: In this study, Taguchi-L18 design is applied to cut AISI 304 stainless steels based on surface roughness under the effects of main control factors through un-coated carbide (K10 grade) and TiAlN coated carbide. The orthogonal array and analysis of variance are utilized to examine the performance characteristic when turning steel bars. A linear regression analysis is carried out to find out the relationship between input parameters and output. In addition, the chips are collected by both cutting inserts to see the morphology. The experimental results indicated that optimal levels were determined at 190 m/min speed, 0.076 m/rev. feed rate, 1.4 mm depth of cut when used TiAlN coating insert for surface roughness. Pareto chart and analysis of variance results revealed that feed rate was dominant, followed by coated tool and cutting speed in analyzing the surface roughness, but the coating was more effective than that of the speed. Further, it was concluded that correlation coefficients were around 93.8% for output. Confirmation tests were provided by Taguchi method and regression analysis. Moreover, the chips collected by TiAlN carbide inserts showed long narrow chips, leading to lower surface roughness because of obtaining the lowest feed rate/moderate speed and insert hardness in addition to providing the larger chip radius and chip length.

Abstract: The structural integrity of Pelton runners, crucial in hydroelectric power generation, is severely compromised by suboptimal repair practices, leading to premature failures and significant operational losses. This study presents a comprehensive analysis of five Latin American case studies (Colombia, Perú and Guatemala), revealing a direct correlation between uncontrolled residual stress and accelerated component degradation. This study demonstrates the effectiveness of integrating XRD with other Non-Destructive Testing (NDT) techniques, enabling the development of predictive models for crack propagation and the optimization of repair protocols. In addition, the results provide evidence that Pelton wheels repair transcends conventional welding, necessitating a profound understanding of residual stress distribution. Precise XRD-driven residual stress determination, pre-and post-intervention, is pivotal for implementing corrective thermal treatments and extending component lifespan. This study demonstrates that integrating XRD into maintenance protocols for Pelton runners constitutes a paradigm shift in structural integrity management. This innovative approach, by enabling precise residual stress analysis, minimizes catastrophic failures and maximizes operational efficiency within hydroelectric power generation. The findings validate the hypothesis that XRD-driven maintenance strategies significantly enhance component longevity and reliability, thereby revolutionizing industry standards.

Abstract: Gears in service conditions experience inevitable tooth damage. These damages can cause changes in the time-varying mesh stiffness depending on the mode of damage. The time-varying mesh stiffness of gears is an essential input in calculating gear dynamic responses. Several researchers have evaluated the mesh stiffness of gear teeth with single-mode damage like pitting, spalling, root cracks and wear using either or any combination of analytical, numeric and experimental models. However, limited research has been done on investigating the mesh stiffness of gear teeth undergoing multi-mode damage. In this work, an analytical model is proposed to evaluate the mesh stiffness of a tooth on the pinion with single-mode damage, including pitting, spalling, and surface crack, separately. In addition, a gear tooth with a combination of pits, spalling and surface cracks is also evaluated. The volume of damage on the tooth is kept constant to provide a basis for comparison. The comparison highlights the possible effects of the combined damage modes, which is a more realistic occurrence in gears in service.

Abstract: Due to the identical operator form of general integral equations (GIE) proposed for both local micromechanics (LM) and a wide class of nonlocally elastic [weakly nonlocal (strain-gradient, stress-gradient) and strongly nonlocal (strain type and displacement type, peridynamics). The modified computational analytical micromechanics (CAM) approach is built on an exact Additive General Integral Equation (AGIE), applicable to CMs with a wide class of structural configuration and phase behavior. A unified iterative solution to the static AGIE is developed, using a compactly supported body force as a fundamentally new training parameter. The method introduces an extended Representative Volume Element concept that generalizes Hill's classical framework. The AGIE-CAM framework enables seamless integration with machine learning (ML) and neural network (NN) methods for constructing any unpredefined surrogate nonlocal operators. The methodology is designed as a modular, block-based system, supporting flexible development and refinement of computational tools.

Abstract: The multiple linear regression analysis (MLR) is one of the most mathematical tools that widely used in recent decades by several investigations research for the prediction of the mechanical properties of energy-efficient building materials. For this purpose, the present study aims to predict and to estimate the compressive strength properties of the compressive stabilized earth blocks (CSEB) using the multiple linear regression analysis (MLR). The statistical modeling is carried out in this work based to the experimental results obtained using different independent variables of cement content, compaction pressures, lime and resin concentrations. The MLR model performed during this investigation is statistically significant with a good correlation between the experiment and the calculated data. The performance evaluation of the developed model was tested by the analysis of the statistical parameters of the coefficient of determination (R²), significance level (Sig.), standardized coefficient (β), t-test value and the variation inflation factor (VIF). Based on the obtained results, the MLR model gives high correlation for compressive strength prediction of CSEBs (R² of 0.82). Moreover, the findings conclude that the cement content has significantly impacts compressive strength values of CSEBs, followed by compaction pressures, lime and resin concentrations. Standardized coefficients of 0.74, 0.44, 0.22, and -0.03 are obtained respectively for each independent variable.

Abstract: A special localization technique is presented for solving steady heat transfer problems, in which the thermal conductivity may depend on space variables. The original problem is split into several subproblems defined on much smaller subdomains. The subproblems are solved using the Method of Fundamental Solutions, which is a truly meshless method. This leads to a Seidel-like iterative technique, which mimics the classical Schwarz overlapping method. The problems associated with large, dense and ill-conditioned matrices are avoided. The method is embedded into a multi-level context, which significantly reduces the computational complexity.