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Assessment of Machinability of Ti6Al4V Alloy Under Dry Conditions
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Titanium alloys pose significant challenges during machining, necessitates a thorough investigation into the impact of process parameters on machining Ti6Al4V alloy. This study investigates into the machinability of Ti6Al4V alloy under dry machining conditions using uncoated tools. The experimental design studiedthe effect of process parameters on the cutting power, surface roughness and material removal rate during dry machining of Ti6Al4V alloy. Utilizing Taguchi design of experiments, unified cutting experiments were conducted under dry conditions employing uncoated tools. The study findings revealed that optimal cutting parameters lead to improved cutting energy consumption, reduced tool wear, and enhanced surface finish and material rates. Analysis of Variance (ANOVA) and regression modelling were employed to obtain an objective function capable of predicting the best cutting conditions. ANOVA showed that cutting speed is the main factor for an increase in the cutting power with major contribution of 73 % whereas feed rate is the key factor for surface roughness with the contribution of 80 %. These results help to gain the advantages of dry machining under optimal conditions, facilitating clean production with minimal energy consumption and improved surface quality.
Title: Assessment of Machinability of Ti6Al4V Alloy Under Dry Conditions
Description:
Titanium alloys pose significant challenges during machining, necessitates a thorough investigation into the impact of process parameters on machining Ti6Al4V alloy.
This study investigates into the machinability of Ti6Al4V alloy under dry machining conditions using uncoated tools.
The experimental design studiedthe effect of process parameters on the cutting power, surface roughness and material removal rate during dry machining of Ti6Al4V alloy.
Utilizing Taguchi design of experiments, unified cutting experiments were conducted under dry conditions employing uncoated tools.
The study findings revealed that optimal cutting parameters lead to improved cutting energy consumption, reduced tool wear, and enhanced surface finish and material rates.
Analysis of Variance (ANOVA) and regression modelling were employed to obtain an objective function capable of predicting the best cutting conditions.
ANOVA showed that cutting speed is the main factor for an increase in the cutting power with major contribution of 73 % whereas feed rate is the key factor for surface roughness with the contribution of 80 %.
These results help to gain the advantages of dry machining under optimal conditions, facilitating clean production with minimal energy consumption and improved surface quality.
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