Experimental Insights and ANN-Based Surface Roughness Prediction through analysis of Machined Surface Quality of Al2024/SiCp Composites

Autor: Al Ansari Mohammed Saleh, Krishnakumari A., Saravanan M., Kiran Chappeli Sai, Kaliappan Seeniappan, Maranan Ramya
Jazyk: English<br />French
Rok vydání: 2024
Předmět:
Zdroj: E3S Web of Conferences, Vol 556, p 01023 (2024)
Druh dokumentu: article
ISSN: 2267-1242
DOI: 10.1051/e3sconf/202455601023
Popis: This present research deals with optimizing machining parameters and surface quality improvement of Al2024/SiCp composites which are important materials used in the aerospace industry. The optimal quartet of factors was investigated to achieve the best outcomes using Taguchi design approach and includes cutting speed of 105 m/min, feed rate of 0.15 mm/rev, and depth of cut of 0.35 mm with a minimal level of roughness of 0.9 μm. An ANN model has been trained and validated, and a high level of predictive accuracy with an overall accuracy of 100% after 195 epochs has been achieved. The results indicated that systematic experimentation and the application of advanced modeling approaches, including the beneficial configuration of parameters and validated ANN model, can help to achieve a superior surface quality meeting the requirements of the aerospace industry. As a result, manufacturers can benefit from the proposed solutions to optimize their production practices, enhance the performance of components, and contribute to the field of aerospace engineering.
Databáze: Directory of Open Access Journals