Rock fragmentation is a critical process in mining operations, with blasting being one of the most common and effective methods employed to achieve the desired results. The primary goal of blasting operations is to deliver optimum rock fragmentation while avoiding adverse environmental effects like back-breaks, fly rock, and ground vibration. The process involves the controlled detonation of explosives within a rock mass, resulting in the generation of smaller rock fragments. This fragmentation is essential for facilitating the extraction, transportation, and processing of valuable minerals, and it plays a pivotal role in optimizing the overall productivity and cost-effectiveness of mining operations. The current work attempted to optimize the ANN utilizing four innovative MOAs: PSO, ICA, TLBO, and ALO, to predict rock fragmentation in mine blasting. A dataset consisting of 219 blasting events with 10 influencing parameters was considered from a limestone mine blasting site located in India. All optimized models were evaluated using the following performance indices: R2, WMAPE, NS, RMSE, VAF, PI, WI, and MAE. The top models were chosen based on the performance indices results, accuracy matrix, ranking analysis, scatter plot, and convergence curve of the optimized models. It was found that the ANN-ICA model outperforms other optimized models for predicting rock fragmentation. This is because, during the training process, the ICA algorithm updates the weights and biases of the ANN better than the PSO, TLBO, and ALO. From the sensitivity analysis of all influencing parameters, it was found that CS emerged as a highly influential factor and MCPD as the second most influential parameter.

Shear strength of soil (SSS) is crucial in civil engineering for foundations, highways, earth fill dams, slope stability, airfields, and coastal structure design. Measuring SSS at a field scale is difficult, time-consuming, and costly. Geotechnical engineers need to predict SSS without complex laboratory testing, addressing practical needs. The prediction of this parameter using hybrid models may assist in saving time and money on construction initiatives. For this purpose, the weight and bias of the artificial neural network (ANN) were optimized by grey wolf optimization (GWO), augmented grey wolf optimization (AGWO), and Harris hawks optimization (HHO), forming hybrid models (ANN-GWO, ANN-AGWO, and ANN-HHO) to predict SSS. The most effective models were chosen after all models had been developed and tested. The validation of the developed hybrid models was implemented with the help of various performance parameters. After the validation process, it was found that the ANN-AGWO hybrid model gives better outcomes in both training and testing phases in predicting SSS. Based on the rank analysis of each model, the rank value in total attained by ANN-AGWO is much higher than that of other developed hybrid models. The hybrid model's performance parameter and rank analysis revealed AGWO as the most reliable ANN, while ANN-GWO emerged as the second-most accurate model.

In this study we focus on the mechanical properties of alkali treated and raw agave Americana fiber. Mechanical extraction and water retting were used to obtain this fibre from the agave Americana tree. In hilly locations, agave americana plants are most common. We get it from the Indian states of Himachal Pradesh and Uttarakhand. Agave Americana fibres were put through mechanical, thermal, SEM, and chemical tests to compare their mechanical properties. The data obtained indicate that treated agave Americana fibre has superior qualities than raw fibre. Thermal properties and mechanical properties improve after NaOH treatment as compare to untreated fibre. The alkali treatment is a fairly simple procedure that is advised for use on plant fibres similar to the fibre from the agave americana.

The most common material utilized in mechanical applications, particularly in structural applications, is aluminium alloys. The study of aluminium alloys utilized in the aircraft sector, automotive industry, energy production management and many other industrial applications has potential. Due to the benefits of high specific strength, low weight, primarily anti-erosion, increased conductivity, eco-friendly nature, and recoverability, aluminium alloys are widely used in the fields of electric module packaging, electronic technology, automotive body structure, and wind and solar energy management. Reviewing earlier studies of the applications, workability, and usage of aluminium is the study's main goal. According to the study's findings, there are many different types of aluminium alloys that have been used by earlier researchers; nevertheless, only AA6262 T6 has received the least amount of attention. The study's future objectives include examining the effects of machining parameters on the turning process and find out the optimized technique for the process parameters.

This research paper is based on the optimization for machining parameter of turning process of CNC M/C tool on aluminium alloy with the help of grey relation analysis. The parameters is found out the experiments perform on aluminium alloy AA6262T6. Machining operation is performed under dry cutting condition by uncoated carbide insert tool. In this research work operation parameters like feed, speed and depth of cut is optimized with output of material removal rate and surface roughness. In the present work, grey relation grade is used as a tool for optimization technique. The result is obtained by grey relation grade find out through grey relation analysis. The experiment outcomes are clear that the response in turning process can be increase efficiency from this fresh approach. To validate the test result, the confirmation test is performed. Prediction of GRA shows the noteworthy increment of 16.629 (14.55 %) in Material Removal Rate (MRR) and a noteworthy reduction of 0.148 (8.9%) in Surface Roughness.