Fracture toughness is a key engineering design parameter. Fracture of engineering materials and components may occur under 3 basic deformations or modes namely pure mode I (opening), pure mode II (shearing) and pure mode III (tearing). However, in practice the possibility of fracturing under mixed mode I/II, I/III and general mixed mode I/II/III case are more than the pure modes. Several experimental methods and testing specimens have been employed by the fracture mechanics researcher to determine the fracture toughness of engineering materials under different mode mixities. Among them, a recently designed and proposed test configuration named Edge-Notched-Disc-Bend (ENDB) is a suitable and favorite testing method for conducting general mixed-mode I/II/III fracture toughness experiments. In this research following a brief description of the ENDB specimen, a review of some recently published papers for investigating the mixed mode fracture problem is presented. According to such a review, it can be concluded that the ENDB method is a suitable candidate specimen for studying general mixed-mode I/II/III fracture problems in materials with brittle or quasi-brittle nature.

Aluminum 1100 is a commercially pure aluminum alloy with properties suitable for applications requiring ductility and workability. It is soft, weldable, and corrosion-resistant. This study attempts to determine the influence of machining on high-speed turning operations. The experiment is designed using the Design of Experiments of Response Surface Methodology, using input parameters such as cutting speed, feed rate, and cutting depth, to estimate surface roughness, temperature, and machining time of aluminum1100 as the workpiece material, with a carbide tool used for operation. The Analysis of Variance technique has been used to test the material's performance. In contrast, the Design Expert software has been used to study the impact of cutting parameters on the workpiece. A Backpropagation ANN model is developed in MATLAB to optimize cutting parameters and reduce Ra, T, and Tm values. The ANN indicates that the lowest expected value is in this case. The Multi-Objective Genetic Algorithms are employed to forecast turning parameters, and it is observed that, for an input parameter grouping of 16 Pareto-optimal solution sets, the ideal Ra ranges from 1.37 to 1.62 µm, and the temperature ranges from 34.10 to 34.08 °C. The machining time ranges from 1.27 to 1.34 min. Among all, cutting speed has the greatest influence on the parameter. The confirmatory analysis shows that the experimental and predicted values differ by less than ±2% and agree admirably with the experimental values.

This study investigates the ballistic response of multilayer jute/epoxy laminates subjected to 9 mm projectile impact through a combined experimental and numerical approach. Experimental results show that, at an impact velocity of 356 m/s, the projectile penetrates only 7-8 layers of a 39-layer laminate and rebounds without back-face deformation. A finite element model is developed and validated against experimental observations, showing good agreement in terms of penetration depth and damage mechanisms. A parametric analysis is conducted by varying the laminate thickness (number of layers) to determine the ballistic limit and the minimum thickness required for projectile arrest. The results identify a transition region between 23 and 26 layers, where the response changes from complete perforation to full projectile arrest, highlighting the strong influence of laminate thickness on ballistic performance.

Rapid advances in 3D printing enable the automotive sector to shorten the design cycles, improve flexibility and support customised manufacturing, overcoming the limitations of conventional methods. However, direct comparisons of impact performance particularly those employing instrumented techniques to analyse strain–time signals between SLM-produced SS 316L specimens of varying thicknesses and these specific conventional reference materials remain limited, leading to uncertainty regarding the use of Additive Manufacturing (AM) parts in safety-critical applications. This study aims to assess the strength of additive manufacturing Stainless Steel 316L (SS 316L) powder at various specimen thicknesses and to compare it against the conventional SS 304 and Al 6061-T6. To capture the strain signal, a Charpy machine, a data acquisition system and strain gauges were used in the experiment. Specimen preparation followed the ASTM E8 for tensile test and ASTM E23 for the Charpy impact. Charpy specimens with thicknesses 5, 7.5 and 10 mm were fabricated using an Ermaksan Enavision 120 Selective Laser Melting (SLM) 3D printer under controlled parameters. Results show that increase in specimen thicknesses proportionally increase the absorbed energy and the area under the curve. Compared to reference material, SS 304 exhibited highest impact resistance, followed by AM SS 316L and Al 6061-T6. These findings demonstrate that AM materials can closely match the performance of conventional material. Furthermore, optimising SLM parameters and applying post-processing can improve impact toughness.

Engineering and economics have played a significant role in today’s world. Together, these two disciplines have contributed to the construction of more sustainable, safer and more durable structures. With technological advancements, structural analyses have become more accessible, allowing for rapid architectural design and implementation of desired geometries. Within the scope of this study, appropriate cross-sections and displacement values of cantilever beams under four different loadings are investigated for both rectangular and circular cross-sectional shapes. Analytical and finite element-based simulation solutions are obtained, taking into account the constant strength criterion throughout the analyses. Displacement values are calculated for prismatic beams and variable-section beams as width or height changed along the beam length. The exact displacements are analytically evaluated; three-dimensional cross-section models and simulation analyses are created using SolidWorks. The three-dimensional cross-section models and the total volume reductions resulting from cross-section changes and the resulting material savings are also investigated.

Natural fiber reinforced composites (NFRCs) have gained recognition as sustainable and environmentally friendly alternatives to synthetic composites. However, estimating their fatigue durability remains a major challenge due to their complex and heterogeneous behaviour. This study develops a machine learning framework to predict the fatigue life of hemp fiber-reinforced HDPE composites from experimental data, addressing the specific limitations of conventional empirical models. The dataset is divided into training and testing subsets following a preprocessing phase that includes logarithmic transformation, normalization, and outlier removal. Four regression models are compared: Multiple Linear Regression, Random Forest, Gradient Boosting Regressor, and Support Vector Regression. Grid Search with 5-fold cross-validation is used to optimize hyperparameters and improve predictive accuracy. Model performance is evaluated using the coefficient of determination (R²), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). Results show that ensemble methods, particularly Random Forest (R² = 99.91%, MAPE = 1.11%) and Gradient Boosting (R² = 99.87%, MAPE = 1.52%), substantially outperform linear models and traditional S-N curve fitting. Feature importance analysis reveals that maximum stress accounts for the majority of prediction variance (approximately 68%–81% depending on the dataset), offering actionable insights for material design. The proposed framework demonstrates strong generalization potential and provides a reproducible template for data-driven fatigue modeling in sustainable composite materials.

This research studied the effect of adding dioctyl phthalate (DOP) on the low- velocity impact properties of unsaturated polyester composites (UPR) reinforced with hybrid fibers (glass-hemp fibers). To achieve this objective, DOP was added to unsaturated polyester at percentages of 1%, 3%, 5%, and 7%. Samples were prepared reinforced with five layers of random glass fibers and two hemp layers (top and bottom). These samples were then subjected to repeated impact testing three times using a drop ball apparatus. The initial impact tests showed that adding DOP up to 5% improved impact resistance, while increasing the added percentage led to an increase in deformation values. Repeated impact tests (first, second, and third) revealed that all samples remained intact but exhibited varying deformation values. The sample containing 5% DOP showed the lowest deformation percentage at 25.17%. A strong correlation was also observed between the rebound coefficient and the added DOP percentage.