ANOVA Based Optimization of Machining Parameters – Review Paper

Abstract

Machining operations such as turning, milling, and drilling are widely used in manufacturing industries to produce components with required dimensional accuracy and surface quality. The selection of optimal machining parameters plays a crucial role in improving productivity and reducing manufacturing cost. Analysis of Variance (ANOVA) is a powerful statistical tool used to determine the significance of machining parameters and their contribution toward performance characteristics such as surface roughness, tool wear, cutting force, and material removal rate. This review paper summarizes recent research work on ANOVA-based optimization of machining parameters in various machining processes. The study focuses on different optimization techniques such as Taguchi method, Response Surface Methodology (RSM), Grey Relational Analysis (GRA), and hybrid machine learning approaches combined with ANOVA. The review highlights that feed rate and cutting speed are generally the most influential parameters affecting surface quality and machining performance. Furthermore, this paper discusses the major research gaps and future research opportunities in the field of machining parameter optimization.

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