In the past few years, a new passion for the growth of biodegradable polymers based on elements derived from natural sources has been getting much attention. Natural fiber-based polymer matrix composites offer weight loss, reduction in cost and carbon dioxide emission, and recyclability. In addition, natural fiber composites have a minimal impact on the environment in regards to global warming, health, and pollution. Polylactic acid (PLA) is one of the best natural resource polymers available among biodegradable polymers. Natural fiber–reinforced PLA polymer composites have been extensively researched by polymer researchers to compete with conventional polymers. The type of fiber used plays a massive part in fiber and matrix bonds and, thereby, influences the composite’s mechanical properties and thermal properties. Among the various natural fibers, low density, high strength bamboo fibers (BF) have attracted attention. PLA and bamboo fiber composites play a vital character in an extensive range of structural and non-structural applications. This review briefly discussed on currently developed PLA-based natural bamboo fiber–reinforced polymer composites concentrating on the property affiliation of fibers. PLA polymer–reinforced natural bamboo fiber used to establish composite materials, various composite fabrication methods, various pretreatment methods on fibers, their effect on mechanical properties, as well as thermal properties and applications on different fields of such composites are discussed in this study. This review also presents a summary of the issues in the fabrication of natural fiber composites.

In a number of circumstances, the Kachanov–Rabotnov isotropic creep damage constitutive model has been utilized to assess the creep deformation of high-temperature components. Secondary creep behavior is usually studied using analytical methods, whereas tertiary creep damage constants are determined by the combination of experiments and numerical optimization. To obtain the tertiary creep damage constants, these methods necessitate extensive computational effort and time to determine the tertiary creep damage constants. In this study, a curve-fitting technique was proposed for applying the Kachanov–Rabotnov model into the built-in Norton–Bailey model in Abaqus. It extrapolates the creep behaviour by fitting the Kachanov–Rabotnov model to the limited creep data obtained from the Omega-Norton–Bailey regression model and then simulates beyond the available data points. Through the Omega creep model, several creep strain rates for SS-316 were calculated using API-579/ASME FFS-1 standards. These are dependent on the type of the material, the flow stress, and the temperature. In the present work, FEA creep assessment was carried out on the SS-316 dog bone specimen, which was used as a material coupon to forecast time-dependent permanent plastic deformation as well as creep behavior at elevated temperatures and under uniform stress. The model was validated with the help of published experimental creep test data, and data optimization for sensitivity study was conducted by applying response surface methodology (RSM) and ANOVA techniques. The results showed that the specimen underwent secondary creep deformation for most of the analysis period. Hence, the method is useful in predicting the complete creep behavior of the material and in generating a creep curve.

The wear loss and frictional characteristics of magnesium-based hybrid composites reinforced with boron carbide (B₄C) particles and graphite filler were the main subjects of the investigation. Key parameters, including reinforcement content (0–10 wt%), applied load (5–30 N), sliding speed (0.5–3 m/s), and sliding distance (500–3000 m), were varied. Data-driven machine learning (ML) algorithms were utilized to identify complex patterns and predict relationships between input variables and output responses. Five distinct machine learning algorithms, Artificial Neural Network (ANN), Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting Machine (GBM), and Support Vector Machine (SVM), were employed to analyze experimental tribological data for predicting wear loss and coefficients of friction (COFs). The performance evaluation showed that ML models effectively predicted friction behavior and wear behavior of magnesium-based hybrid composites using tribological test data. A comparison of model performances revealed that the Gradient Boosting Machine (GBM) provided superior accuracy compared to other machine learning models in predicting both wear loss and the coefficient of friction. Additionally, feature importance analysis indicated that the graphite weight percentage was the most significant influence in predicting the coefficient of friction and wear loss characteristics.

This study aims to optimize the turning parameters for EN-GJL-250 grey cast iron using hybrid machine learning techniques integrated with multi-objective optimization algorithms. The experimental design focused on evaluating the impact of cutting tool type, testing three tools: uncoated and coated silicon nitride (Si₃N₄) ceramic inserts and coated cubic boron nitride (CBN). Key cutting parameters such as depth of cut (ap), feed rate (f), and cutting speed (Vc) were varied to examine their effects on surface roughness (Ra), cutting force (Fr), and power consumption (Pc). The results showed that the coated Si₃N₄ tool achieved the best surface finish, with minimal cutting force and power consumption, while the uncoated Si₃N₄ and CBN tools performed slightly worse. Advanced optimization models including improved grey wolf optimizer–deep neural networks (DNN-IGWOs), genetic algorithm–deep neural networks (DNN-GAs), and deep neural network–extended Kalman filters (DNN-EKF) were compared with traditional methods like Support Vector Machines (SVMs), Decision Trees (DTs), and Levenberg–Marquardt (LM). The DNN-EKF model demonstrated exceptional predictive accuracy with an R² value of 0.99. The desirability function (DF) method identified the optimal machining parameters for the coated Si₃N₄ tool: ap = 0.25 mm, f = 0.08 mm/rev, and Vc = 437.76 m/min. At these settings, Fr ranged between 46.424 and 47.405 N, Ra remained around 0.520 µm, and Pc varied between 386.518 W and 392.412 W. The multi-objective grey wolf optimization (MOGWO) further refined these parameters to minimize Fr, Ra, and Pc. This study demonstrates the potential of integrating machine learning and optimization techniques to significantly enhance manufacturing efficiency.