Government Engineering College, Madhubani is a government engineering college in Madhubani district of Bihar Near Pandaul On the Madhubani-Sakri Highway. It was established in the year 2019 under Department of Science and Technology, Bihar. It is affiliated with Aryabhatta Knowledge University,Patna and approved by All India Council for Technical Education.
In this work, the application of machine learning (ML) models for the prediction of titanium alloy for electrical discharge machining (EDM) was studied. The machining performances (MRR, TWR, Ra , and recast layer thickness) were predicted by SVM, XG Boost and KNN machine learning models. The heatmap highlighted a strong correlation between parameters and a positive effect was observed for MRR (0.96) and Ra-recast layer thickness (0.82). The performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) were also utilized to compare the models. SVM achieved the best prediction results for TWR and recast layer thickness, while KNN produced the lowest MAE on MRR. The XG Boost model had slightly compromised prediction accuracy than the support vector machine for TWR. These results suggested that a hybridized approach could improve prediction accuracy for complex machining parameters, especially in high-precision machining applications such as biomedical and aerospace industries.
Hydrothermal and plant extracted green synthesis method was employed to synthesize MoS2 and CuO nanoparticles respectively. Subsequently, CuO/MoS2 nanocomposites were synthesized by incorporation of CuO into MoS2. The FESEM, XRD, UV-DRS and TGA techniques were used to examine the properties of materials.The FESEM showed a layered MoS2 nanosheets were decorated with CuO nanoparticles, which confirms an effective interfacial interaction and uniform dispersion in the hybrid structure. The UV analysis revealed an improvement in visible light absorption of the composites over pure MoS2. Optical bandgap values were also determined as 1.75 eV for bare MoS2 and 1.66 eV for CuO and the composites showed slightly lower bandgap of 1.70 to 1.67 eV. TGA analysis revealed enhanced thermal stability of the composites whereby the MC (1:2) sample exhibited the superior thermal stability.
This study presents a hybrid machine learning framework for stability assessment of anchored sheet pile walls under uncertainty of soil. Monte Carlo Simulation was employed to generate 1,000 stochastic realizations by incorporating variability in the angle of internal friction, unit weight, and submerged unit weight of sandy soil. Factors of safety against sliding and overturning were computed for each realization to establish a comprehensive dataset. Extreme Gradient Boosting models were optimized using Particle Swarm Optimization, Tiki-Taka Algorithm, Whale Optimization Algorithm, and Reptile Search Algorithm to predict stability responses. Model performance was assessed using statistical error metrics and comparative analyses. The model optimized with Reptile Search Algorithm achieved the highest predictive accuracy for both sliding and overturning stability, surpassing the models optimized with the other metaheuristic algorithms. Explainable artificial intelligence analysis based on SHapley Additive exPlanations was performed to quantify the contribution of input variables and improve model interpretability. The analysis identified the angle of internal friction as the most influential parameter, followed by soil unit weight. The developed framework provided accurate, computationally efficient, and interpretable predictions, demonstrating its applicability for reliability-based assessment and design of anchored sheet pile wall systems under uncertain geotechnical conditions.
