Prediction of groundwater contamination in an open landfill area using a novel hybrid clustering-based ai model

The current research explores the optimization of Silicon Carbide particle-reinforced aluminum metal matrix composites to improve mechanical properties. An integrated method based on Taguchi Design of Experiment and Artificial Neural Network has been adopted, with the novel approach to explore the optimal combination of parameters. The obtained best set includes the minimum load of 30 N, the minimum speed of 100 rpm, and the larger composition of 9% SiC particle. The designed L9 orthogonal experimental plan was used to conduct the experiments, and the findings explicitly indicated the significant impacts on the reduction of specific wear rate and friction force . Furthermore, the Artificial Neural Network trained through the backpropagation algorithm estimated all the percentages correctly to the ideal combination, equivalent to 100% in predicting the target responses. Moreover, the confirmation experience has validated the optimal combination, as it approaches specific wear rate of 0.0019, and friction force was 10.5. These results highlight the role of the integrated research approach for assessing the optimal parameters of aluminum MMCs to the required mechanical properties. Consequently, the current study highlights the importance of experimental plan integration and predictive modeling for optimizing materials, and it applies to various engineering fields where wear resistance and friction performance are critical.

Optimizing Aluminum Metal Matrix Composites with SiC Nanoparticles using Taguchi-ANN Approach for Enhanced Mechanical Performance

Al Ansari,

Karthikeyan,

Kaliappan

et al. 2024

Optimizing Surface Roughness in Turning of Al7072 with nano particles of Carbon Metal Matrix Composite using Taguchi Analysis and ANN Prediction

Al Ansari,

Kaliappan,

Bhargavi

et al. 2024

This research centers on optimizing the machining process of Al7072 alloy reinforced with carbon nanoparticles. While surface roughness is the primary research focus, it is one of the most critical parameters in the manufacturing of aerospace components. According to the Taguchi design of experiments tool, the structured experimental framework has been used to learn the precise consequences of Cutting speed (Cs) , Feed rate (Fr), and Depth of Cut (DoC) on surface roughness outcomes. Using cutting-edge algorithms, particularly the Artificial Neural Network, significantly increases these predictive abilities. It hence forecasts the surface roughness achieved with various machining outcomes. According to the initial results, the surface roughness response is extremely dependent on the machining outcomes. The signal-to-noise ratio conducted the statistical analysis to discover the best parameter equation that would allow for the best surface quality and machining economy. Furthermore, the ANN-based model has been created, demonstrating a high level of accuracy in providing feed response. This might be used to optimize the machining process. The results recommend improving the accessibility of machining and increasing aerospace equipment’s quality of service. Thus, the process presented in this research might improve the public’s communication with respect to machining and machining economics.

Estimation of Machining Performance in Wire EDM of Aluminum Silicon Nitride Composite an Experimental Analysis and ANN Modeling

Al Ansari,

Kaliappan,

Reddy

et al. 2024

The primary objective of the current research is to optimize machining performance in Al 7010 alloyreinforced with silicon nitride nanoparticles. This has been accomplished through a combination ofexperimental analysis and predictive modeling methodologies. Initially, composite materials were createdusing stir casting, and varied percentages of silicon nitride were incorporated into the material to supplementits mechanical properties. Wire Electrical Discharge Machining was performed using different parameters suchas Pulse On Time , Pulse Off Time , and Current , and a range of these parameters was defined according tolevels . Material Removal Rate and Surface Roughness were chosen as the machining responses and indicatedhigh sensitivity to variations in chosen parameters. Each response was thoroughly investigated and detectedusing these responses before establishing the optimized levels. Taguchi design of experiments and signal-tonoiseratio were two common techniques used to investigate parameter interactions, and they were also used todetermine the optimum combinations for both the parameters for optimizing MRR and minimizing SR.Moreover, an Artificial Neural Network (ANN) model was also established to foresee the response readingswith great precision and predict the parameter effect to enhance further predictive modeling capabilities inmachining. The present research optimization results indicated that the maximum MRR is obtained at Pulse OnTime , Pulse Off Time , and Current levels, while the minimum SR is obtained at Pulse On Time , Pulse OffTime , and Current levels. These findings provide promising avenues of research in the field of aerospace,indicating the possibility of machining components with superior machinability and mechanical strength.Furthermore, the predicting ability of an ANN model helps in obtaining the insights to engineers to optimizetheir process by gaining information about performance and material response.