Affiliation:
1. Faculty of Mechanical Engineering and Design, Kaunas University of Technology, Studentų Str. 56, LT-51424 Kaunas, Lithuania
Abstract
Duplex stainless steels (DSSs) are used in many applications due to their properties, such as high mechanical strength, good corrosion resistance, and relatively low cost. Nevertheless, DSS belongs to the materials group that is difficult to machine. The demand for a total increase in the production requires the optimization of cutting conditions. This paper examines the influence of cutting parameters, namely cutting velocity, feed, and the depth of cut on the surface roughness and chip compression ratio (CCR) after the DSS wet turning process. The study employed Taguchi optimization to determine the ideal cutting parameters for wet turning finishing operations on steel 1.4462. Using the Taguchi design, experiments focused on surface roughness (Ra) and CCR. Utilizing a TiAlN/TiN-PVD coating insert with a 0.4 mm nose radius, cutting velocity of 200 m/min, feed rates of 0.05 mm/rev, and cutting depths of 1 mm yielded the lowest Ra at 0.433 μm. Meanwhile, a cutting velocity of 200 m/min, feed rate of 0.15 mm/rev, and cutting depth of 0.5 mm resulted in the smallest CCR at 1.39, indicating minimal plastic deformation. The inclusion of additional cooling proved beneficial for surface roughness compared to dry and wet turning methods. The experimental data holds value for training and validating artificial intelligence models, preventing overfitting by ensuring sufficient data collection.
Reference39 articles.
1. Modeling and optimization of turning duplex stainless steels;Koyee;J. Manuf. Process.,2014
2. Environmentally conscious machining of difficult-to-machine materials with regard to cutting fluids;Shokrani;Int. J. Mach. Tools Manuf.,2012
3. Machinability Study of Duplex Stainless Steel 2205 during Dry Turning;Sonawane;Int. J. Precis. Eng. Manuf.,2020
4. Fountas, N.A., Papantoniou, I., Manolakos, D.E., and Vaxevanidis, N.M. (2024). Implementation of Grey Wolf, Multi-Verse and Ant Lion Metaheuristic Algorithms for Optimizing Machinability of Dry CNC Turning of Annealed and Hardened UNIMAX® Tool Steel. Machines, 12.
5. Turning processes investigation of materials austenitic, martensitic and duplex stainless steels and prediction of cutting forces using artificial neural network (ANN) techniques;Ulas;Indian J. Eng. Mat. Sci.,2019