• AMEER PATEL
• WAJID
• MD ASHFAQ HUSSAIN

The purpose of this study is to find out the most influencing parameters and its optimum range in CNC turning using various statistical tools such as Taguchi’s Grey relational analysis and Analysis of Variance (ANOVA). Surface roughness and material remova1 rate are predicted using the predictor’s Cutting speed, feed rate and depth of cut. For investigation ANOVA and grey relational analysis are used to find the most significant factor among spind1e speed, feed rate and depth of cut that influences Surface roughness and Material removal rate. Artificial Neural Network model based on Levenberg-Marquardt back-propagation algorithm is proposed to use as a prediction tool as an expert system. With the optimum combination levels from ANOVA and grey relationa1 analysis, confirmation test is proposed to be conducted. The experiments are planned to be conducted XL TURN CNC Turning Machine and responses will be measured by using Mitutoyo SURFTEST SJ-210 apparatus and digital weighing Machine and MINITAB14 software will be used for ANOVA.

Turning operation, Analysis of Variance (ANOVA), Signal to Noise Ratio, (SN), Taguchi’s Method, Grey Relational Grade, Artificial Neural Network (ANN)

"Multi Objective Optimization in CNC Turning Using Taguchi Concept Coupled with Artificial Neural Network ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.a52-a63, March-2025, Available :http://www.jetir.org/papers/JETIR2503007.pdf

"Multi Objective Optimization in CNC Turning Using Taguchi Concept Coupled with Artificial Neural Network ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppa52-a63, March-2025, Available at : http://www.jetir.org/papers/JETIR2503007.pdf