Optimization of specific energy consumption in CNC turning of a hardened alloy steel roll at low cutting speeds

Abstract

Purpose

This study was conducted to analyze the effects of machining parameters on the specific energy consumption in the computerized numerical control lathe turning operation of a hardened alloy steel roll at low cutting speeds. The aim was to minimize its consumption.

Design/methodology/approach

The design matrix was based on three variable factors at three levels. Response surface methodology was used for the analysis of experimental results. Optimization was carried out by using the desirability function and genetic algorithm. A multiple regression model was used for relationship build-up.

Findings

According to desirability function, genetic algorithm and multiple regression analysis, optimal machining parameters were cutting speed 40 m/min, feed 0.2 mm/rev and depth of cut 0.50 mm, which resulted in minimal specific energy consumption of 0.78, 0.772 and 0.78 kJ/mm³, respectively. Correlation analysis and multiple regression model found a quadratic relationship between specific energy consumption with power consumption and material removal rate.

Originality/value

In the past, many researchers have developed mathematical models for specific energy consumption, but these models were developed at high cutting speed, and a majority of the models were based on the material removal rate as the independent variable. This research work developed a mathematical model based on the machining parameters as an independent variable at low cutting speeds, for a new type of large-sized hardened alloy steel roll. A multiple regression model was developed to build a quadratic relationship of specific energy consumption with power consumption and material removal rate. This work has a practical application in hot rolling industry.

Citation

Noor, K., Siddiqui, M.A. and Syed, A.I. (2023), "Optimization of specific energy consumption in CNC turning of a hardened alloy steel roll at low cutting speeds", Journal of Engineering, Design and Technology, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JEDT-03-2023-0088