Titanium turning data

Furthermore, the theoretical tool life and the relationship between parameters have been analysed. The methodology involved 45 physical experimental analyses conducted on a Ti-6Al-4V bar using a Harrison M400 conventional lathe and coated carbide inserts with nose radii of 0.40 mm, 0.80 mm, and 1.20 mm. The parameter ranges investigated were cutting speeds between 50-160 m/min, feed rates between 0.05-0.18 mm/rev, and depths of cut between 0.1-1 mm.

Surface roughness was the key dependent/response parameter, measured using a Taylor Hobson Surtronic 3+ instrument. The gathered data have been analysed using Design Expert software (Stat-Ease 360) to create 3D graphs and validate the statistical significance of the models. The results demonstrated that the nose radius and feed rate were the most statistically significant parameters influencing surface roughness, as confirmed by ANOVA analysis.

Both the 3D surface plots and the theoretical relationship confirmed that is inversely proportional to the nose radius and rises nonlinearly and quickly with increasing feed rate. The 1.20 mm nose radius insert consistently produced a smoother surface finish compared to the smaller radii. Cutting speed and depth of cut had less significant effects on surface roughness within the optimum range tested. i Theoretical tool life analysis confirmed an inverse relationship where tool life decreases as cutting speed, feed rate, or depth of cut increase.

The chips produced were characteristic of difficult-to-machine materials like Ti-6Al-4V, identified as serrated chips.Serrated chips produced characterised that Ti-6Al-4V is a difficult to machine material. Chip entanglement, which impairs machining efficiency, was observed at higher cutting speeds and depths of cut. This research aligns with the United Nations Sustainable Development Goal 12, promoting sustainable industrial practices by focusing on efficient machining processes.The significance of this research is to provide the manufacturing industry and Researchers with optimal process data.

Secondly, to showcase the importance of physical machining compared to current mathematical models, simulation techniques and cooling methods.<p></p>