AUTHOR=Donenbayev Bakhytzhan , Sherov Karibek , Mardonov Bakhtiyor , Makhmudov Lutfiddin , Magavin Sabit , Rakishev Asset , Sherov Aibek 

TITLE=Research and modelling of the high-speed milling process of heat-resistant high-alloy steel

JOURNAL=Frontiers in Mechanical Engineering

VOLUME=Volume 11 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/mechanical-engineering/articles/10.3389/fmech.2025.1680007

DOI=10.3389/fmech.2025.1680007

ISSN=2297-3079

ABSTRACT=This study investigates the high‐speed milling behaviour of heat‐resistant martensitic–ferritic steel 15Kh12VMF, widely used in energy and power engineering components but difficult to machine due to its high hardness, strength and low thermal conductivity. An integrated approach combining experimental trials and finite element modelling was applied to assess the influence of cutting parameters on surface quality, tool wear and thermo-mechanical responses. Experiments were conducted on a vertical machining centre under dry cutting conditions using TiAlSiN‐coated carbide tools. Milling parameters were varied within spindle speeds of 2000–12,000 revolutions per minute, feed rates of 500–4500 mm/min and cutting depths of 1–5 mm. Surface roughness was measured according to ISO 4287 standards. Finite element simulations were performed in ANSYS Workbench using the Johnson–Cook constitutive and damage models to reproduce chip formation, temperature distribution and cutting forces. Results indicated that increasing spindle speed from 3000 to 6000 revolutions per minute reduced surface roughness by up to 18%, whereas higher feed rates and depths of cut increased it by 25% and 32%, respectively. Optimal parameters were identified as 6000 revolutions per minute, 1500 mm/min and 2 mm. Tool wear accelerated beyond 6000–7000 revolutions per minute due to elevated cutting temperatures. Simulations predicted a peak temperature of 291.47 °C and cutting forces between –2500 N and +7500 N, consistent with experiments. This study provides validated reference data and modelling insights to support parameter optimisation and improve high-speed milling performance of martensitic–ferritic steels.