University of Military Science and Technology

Additive Manufacturing (AM) is transforming the manufacturing industry, with Fused Deposition Modeling (FDM) being one of the most widely used techniques due to its affordability and ease of use. However, the mechanical performance of FDM-printed parts is highly dependent on process parameters, making it essential to understand and optimize their influence. This study investigates the impact of six critical FDM parameters including extruder temperature, printing speed, infill density, infill pattern, layer height, and the number of wall layers, on the tensile strength of 3D-printed Polylactic Acid (PLA) parts. Using the dataset from Qian et al. (2024), which includes tensile strength values of 92 PLA samples fabricated under varied parameter settings, we extend their work by applying several machine learning (ML) models to predict and classify mechanical outcomes. Regression algorithms used include Polynomial Regression (PR), Gradient Boosting (GB), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB), while classification tasks employed Support Vector Classifier (SVC) and XGBoost (XGB). The XGBoost regressor outperformed other models with a test R² of 0.76 (RMSE:5.19, MAE:3.09), followed by Polynomial Regression (R²:0.71, RMSE:5.60, MAE:3.17), Gradient Boosting (R²:0.70, RMSE:5.78, MAE:2.83), and SVM (R²:0.65, RMSE:6.18, MAE:3.19). In classification, the XGB classifier achieved perfect results on the test set with 100% accuracy, precision, recall, and F1-score, whereas the SVC model attained an accuracy of 84.21% and an F1-score of 0.8491. Pearson correlation coefficient analysis revealed infill density, printing speed and wall layers as the most influential parameters (p

Global energy consumption will double by 2050, increasing our dependence on fossil fuels in the process. Fossil fuel combustion is predicted to generate 500 tons of CO2 by 2060. Researchers have been working for years to reduce CO2 emissions by converting it into value-added products, like chemicals and fuels. CO2 is an inert gas with a low electron affinity and a high bandgap (13.6 eV). The dissociation of the C=O bond requires a large energy input (750 kJ mol−1), which is only possible under pressure and temperature conditions or using highly efficient catalysts. After discovering graphene in 2004, research on catalysts for CO2 conversion has become a hot topic. Nanomaterials with a large surface area to volume ratio act as catalysts more effectively than their bulk counterparts. The extremely thin thickness of 2D nanomaterials also results in extraordinary electrical and optical properties, which facilitate the process of harvesting energy. In addition, a high density of crystal imperfections like dislocations and point defects can easily be incorporated into 2D materials, which can act as active sites for catalytic reactions. Graphene oxides, graphitic carbon nitrides, 2D metal oxides, MXenes, transition metal dichalcogenides, metal complexes, etc., exhibit promising potential for catalytic CO2 reduction. Chemical conjugates of inorganic and organic compounds are the most effective catalysts in the CO2 reduction reaction. They minimize the cost of using noble elements without compromising efficiency. This chapter addresses 2D hybrid nanomaterials used to reduce CO2 to value-added chemicals and fuels, focusing on their synthesis, properties, applications, and challenges.