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

¹ College of Engineering, Department of Chemical Engineering, University of Bahrain, Bahrain
² Department of Mechatronics Engineering, KCG College of Technology, Chennai 600097, Tamil Nadu, India.
³ Department of Mechanical Engineering, CVR College of Engineering, Vastunagar, Mangalpalli, Ibrahimpatnam, Telangana 501510
⁴ Chemical Engineering, Department of Technology, Shivaji University, Kolhapur, Maharashtra , India 416004
⁵ Department of Automobile Engineering, KCG College of Technology, Chennai 600097, Tamil Nadu, India.
⁶ Department of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu 602105, India.

^* correspondence Author:malansari.uob@gmail.com

Abstract

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.