Evaluating Cutting Tools for Turning Applications Through Statistical Analysis

Abstract

This study investigates the performance and
effectiveness of cutting tools used in turning
operations through a comprehensive statistical
analysis. Turning is a fundamental machining process
widely employed in manufacturing, where the choice
of cutting tool significantly impacts machining
efficiency, surface finish, and tool longevity. The
research utilizes various statistical techniques to
analyze data collected from multiple turning trials,
focusing on parameters such as tool wear, cutting
speed, feed rate, and material properties. By
employing statistical methods such as regression
analysis, ANOVA, and control charts, the study aims
to identify key factors that influence tool
performance and to establish correlations between
tool geometry and operational efficiency. The
findings reveal critical insights into optimal cutting
conditions and highlight the importance of selecting
appropriate cutting tools for specific materials and
applications. Ultimately, this research provides
valuable guidelines for manufacturing engineers and
practitioners to enhance productivity and quality in
turning operations, contributing to the overall
advancement of machining technology.