Optimization of Machining Parameters of FG300 Flange Using ANOVA Technique under MQL

Pravin K. Paramkar; Ajit. R. Balwan

doi:10.55524/ijirem.2025.12.3.4

Abstract

The machining of FG300 gray cast iron flanges is critical in various industrial applications, requiring optimized parameters to enhance surface quality, reduce tool wear, and improve efficiency. The goal of this research is to maximise cutting depth, feed rate, and speed using a Minimum Quantity Lubrication (MQL) system, which is a sustainable substitute for traditional cooling techniques. MQL minimizes lubricant consumption while ensuring effective cooling and lubrication, contributing to sustainable manufacturing. Turning experiments were conducted using carbide cutting tools under controlled MQL conditions. Tool wear and surface roughness (Ra) were examined as important performance metrics. The statistical significance of the machining parameters and their impact on the responses were evaluated using the Analysis of Variance (ANOVA) approach. The findings show that cutting speed has the greatest impact, followed by feed rate and cut depth. The optimized parameters led to improved surface finish, reduced tool wear, and enhanced machining efficiency. This study provides a systematic approach for optimizing machining parameters under MQL conditions, offering valuable insights for industries seeking to enhance productivity while reducing costs and environmental impact. The findings demonstrate that ANOVA-based optimization under MQL can significantly improve the machining of FG300 flanges, making it a promising approach for sustainable and high-precision manufacturing.

Keywords

Minimum Quantity Lubrication (MQL), Surface roughness, Analysis of Variance

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Cites this article as

P. K. Paramkar, A. R. Balwan, "Optimization of Machining Parameters of FG300 Flange Using ANOVA Technique under MQL", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-12, Issue-3, Page No-32-40, 2025. Available from: https://doi.org/10.55524/ijirem.2025.12.3.4