OPTIMIZATION PROCESS PARAMETERS IN CNC TURNING USING ANOVA AND GREY RELATIONAL ANALYSIS

Abstract

Turning operations play a vital role in modern manufacturing by shaping components through controlled material removal. The quality of machined parts is primarily influenced by surface roughness, MRR, and dimensional accuracy. Improper selection of machining parameters can lead to poor surface finish, reduced productivity, increased tool wear, and higher production costs. Therefore, optimizing these parameters is essential for achieving high-quality and cost-effective manufacturing. This study is undertaken to optimize CNC turning parameters to improve machining performance. The objective is to identify the most significant factors affecting surface roughness, accuracy, and MRR, and to establish the optimal combination of spindle speed, feed rate, and depth of cut. A structured methodology is adopted, incorporating literature review, experimental design, statistical analysis, and validation. A Taguchi-based experimental is used for systematically plan experiments and evaluate the influence of process parameters. The Signal-to-Noise (S/N) ratio is employed to assess process robustness, while Analysis of Variance (ANOVA) is applied to determine the statistical importance and part of each factor. To address the multi-response optimization problem, Grey Relational Analysis (GRA) is utilized to convert multiple performance characteristics into a single relational grade, enabling the identification of optimal machining conditions. Confirmation experiments are conducted to verify the effectiveness of the optimized parameters. The results are expected to identify key influencing factors, determine optimal cutting conditions, and enhance surface quality, dimensional accuracy, and productivity. This study provides practical insights for improving CNC turning operations and contributes to the advancement of machining optimization techniques.