Magnesium matrix composites have attracted significant attention due to their lightweight nature and impressive mechanical properties. However, the fabrication process for these alloy composites is often time-consuming, expensive, and labor-intensive. To overcome these challenges, this study introduces a novel use of machine learning (ML) techniques to predict the mechanical properties of magnesium matrix composites, providing an innovative and cost-effective alternative to conventional methods. Various regression models, including decision tree regression, random forest regression, extra tree regression, and XGBoost regression, were employed to forecast the yield strength of magnesium alloy composites reinforced with diverse materials. This approach leverages existing research data on matrix type, reinforcement type, heat treatment, and mechanical working. The XGBoost Regression model outperformed the others, exhibiting an R² value of 0.94 and the lowest error rate. Feature importance analysis from the best model indicated that the reinforcement particle form had the most significant influence on the mechanical properties. Our research also identified the optimized parameters for achieving the highest yield strength at 186.99 MPa. This study successfully demonstrated the effectiveness of ML as a valuable, novel tool for optimizing the production parameters of magnesium matrix composites.

Machine learning (ML), a prominent branch of artificial intelligence, is increasingly applied in material design, particularly for magnesium composites. In this study, random forest models were used to predict mechanical properties and fractographic behavior using regression classification, respectively. Both the regression and classification models of the random forest demonstrated high accuracy in predicting new optimal mechanical properties for a composite containing 0.16 wt% graphene, which was enhanced through T6 heat treatment and equal channel angular pressing (ECAP). The predictions were further validated through laboratory experiments. Although not all predicted mechanical property values exceeded the optimal values obtained from the experiments, the strain-hardening capacity of the ML-recommended samples was higher than that of the experimental samples. In addition, the predicted surface features using fractography closely matched the experimental validation, indicating consistent ductile behavior.

Gas, ion, and biological sensors have been widely utilized to detect analytes of great significance to the environment, food, and health. Paper-based sensors, which can be constructed on a low-cost paper substrate through a simple and cost-effective fabrication process, have attracted much interests for development. Moreover, many materials can be employed in designing sensors, such as metal oxides and/or inorganic materials, carbon-based nanomaterials, conductive polymers, and composite materials. Most of these provide a large surface area and pitted structure, along with extraordinary electrical and thermal conductivities, which are capable of improving sensor performance regarding sensitivity and limit of detection. In this review, we surveyed recent advances in different types of paper-based gas, ion, and biological sensors, focusing on how these materials’ physical and chemical properties influence the sensor’s response. Challenges and future perspectives for paper-based sensors are also discussed below.

The light weight and high strength of magnesium alloys have garnered significant attention, rendering them suitable for various applications across industries. Nevertheless, to meet industrial requirements, the mechanical properties must be improved. This investigation explores the potential of graphene addition to enhance the mechanical properties of AM60B magnesium alloy. Tests were conducted on samples with different weight percentages (wt.%) of graphene (0 wt.%, 0.1 wt.%, and 0.2 wt.%) using stir casting. The elongation and tensile strength of the composite materials were also assessed. The phase composition, particle size, and agglomeration phenomena were analyzed using characterization techniques such as X-ray diffraction, optical microscopy, and SEM-EDS. The yield strength of the magnesium alloy was enhanced by approximately 13.4% with the incorporation of 0.1 wt.% graphene compared to the alloy without graphene. Additionally, an 8.8% increase in elongation was observed. However, the alloy tensile properties were reduced by adding 0.2 wt.% graphene. The tensile fractography results indicated a higher probability of brittle fracture with 0.2 wt.% graphene. Furthermore, regression analysis employing machine learning techniques revealed the potential of predicting the stress–strain curve of composite materials.

Magnesium matrix composites have attracted significant attention due to their lightweight nature and impressive mechanical properties. However, the fabrication process for these alloy compo-sites is often time-consuming, expensive, and labor-intensive. To overcome these challenges, this study employed machine learning (ML) techniques to predict the mechanical properties of magnesium matrix composites. Regression models were utilized to forecast the yield strength of magnesium alloy composites reinforced with various materials. The study incorporated previous research on matrix type, reinforcement type, heat treatment, and mechanical working. The re-gression models employed in this study included decision tree regression, random forest re-gression, extra tree regression, and XGBoost regression. Model performance was assessed using metrics such as RMSE and R2. The XGBoost Regression model out-performed others, exhibiting an R2 value of 0.94 and the lowest error rate. Feature importance analysis indicated that the rein-forcement particle form had the greatest influence on the mechanical properties. The study iden-tified the optimized parameters for achieving the highest yield strength, which was 186.99 MPa. Overall, this study successfully demonstrates the effectiveness of ML as a valuable tool for opti-mizing the production parameters of magnesium matrix composites.