APPLYING TAGUCHI METHOD AND FUZZY GREY RELATIONSHIP ANALYSIS (FGRA) TO ANALYZE THE INFLUENCE OF CUTTING PARAMETERS ON TOOL WEAR WHEN HIGH-SPEED MILLING OF HARDENED STEEL

Abstract

Analysis of the influence of cutting parameters on tool wear during high-speed milling is a
highly effective method to improve tool life and thereby reduce machining costs. To study the
influence of cutting parameters on tool wear during high-speed milling, experimental models
according to Taguchi L9 were established in combination with the fuzzy grey relationship analysis
(FGRA) algorithm presented in this study. The results of ANOVA analysis show that there is a
significant difference between the dry compared to the wet milling process, when milling with a
cooling fluid, the option for the smallest flank wear corresponding to depth of cut t = 0.1mm, feed
rate S = 955 (mm/min), cutting speed V = 300 (m/min), while dry milling corresponds with
parameters depth of cut t = 0.3mm, feed rate S = 955 (mm/min), cutting speed V = 300 (m/min).
The results of the FGRA analysis show that when high-speed milling of hardened steel in wet
milling and dry milling processes both show the depth of cut has the most influence on tool wear,
followed by cutting speed and lastly the feed rate has the smallest effect on tool wear.