Forecasting the output using ANN models and effect of input factors on machinability of Duplex Steel 2205 in dry-turning operation for high strength and anti-corrosive applications

ABSTRACT

The products made of advanced materials are the buzz amid material scientists as they possess enhanced mechanical properties, leading to development of material such as Duplex Steel 2205. The material being hard and having high strength, machinability along with stringent environmental regulations is a challenge keeping in view the wide range of applications that the material caters like pressure vessels, marine equipment and heat exchanger. The current experimental investigation of flank wear and surface roughness was predicted through ANN model and experimentally evaluated under dry-turning conditions using carbide-tipped inserts. Dry turning was performed by selecting the speed levels of 103, 134, and 174 m/min; feed rate (FR) levels of 0.12, 0.15, and 0.18 mm/rev; and depth of cut (DOC) levels of 0.5, 1.0, and 1.5 mm. Experiments were designed using Taguchi-L9 model, optimum cutting speed (134 m/min), feed (0.12 mm/rev), and DOC of (1.0 mm) resulted in least Ra values. The regression model created on the experimental data is in 95% conformance for surface roughness and flank wear. Optimal machining parameters indicate that cutting speed have greatest significance on machining trailed by feed and DOC.

KEYWORDS:

• ANN modelling
• Duplex Steel 2205
• dry turning
• orthogonal array

Disclosure statement

No potential conflict of interest was reported by the author(s).