Optimization of CNC milling parameters for YXR-7 tool steel using fuzzy MARCOS: A multi-response approach to improve machining productivity

Abstract

This study presents an integrated optimisation method for CNC milling of heat-treated YXR7 tool steel using carbide cutting inserts under varying lubrication and process parameters. A full factorial experimental design comprising 27 runs was employed to assess the influence of depth of cut ( d c ), feed per tooth ( f t ), cutting speed ( C s ), and nano-cutting fluid ( C f ) on critical performance responses such as surface roughness ( Ra ), material removal rate ( MRR ), and tool wear rate ( TWR ). An advanced modelling through regression and ANOVA showed complex interactive and non-linear effects among process parameters. To effectively navigate these interdependencies, a novel hybrid decision-making model combining the Full Consistency Method (FUCOM) and fuzzy-MARCOS was employed. This multi-criteria decision-making (MCDM) method was described for uncertainties in machining performance and successfully ranked experimental alternatives based on their proximity to ideal performance. The optimal configuration (Experiment 21) accomplished a superior balance across all criteria, notably achieving a low surface roughness (Ra ≈ 0.42 µm) and TWR (~0.148 mm³/min) while maintaining a high MRR (~109.4 mm³/min). The proposed fuzzy-FUCOM-MARCOS method reveals high robustness, adaptability, and decision reliability, contributing a valuable strategy for precision machining of hard-to-cut steels. This work bridges experimental understandings with intelligent optimisation, fostering sustainable and high-performance manufacturing practices in the tooling industry.