INVESTIGATION OF MACHINABILITY PERFORMANCE IN TURNING OF Ti–6Al–4V ELI ALLOY USING FIREFLY ALGORITHM AND GRNN APPROACHES

Ramanuj Kumar, Anish Pandey, Ashok Kumar Sahoo and Mohammad Rafighi
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Ramanuj Kumar: School of Mechanical Engineering, KIIT University, Patia, Bhubaneswar 751024, Odisha, India
Anish Pandey: School of Mechanical Engineering, KIIT University, Patia, Bhubaneswar 751024, Odisha, India
Ashok Kumar Sahoo: School of Mechanical Engineering, KIIT University, Patia, Bhubaneswar 751024, Odisha, India
Mohammad Rafighi: ��Department of Mechanical Engineering, University of Turkish Aeronautical Association, 06790 Etimesgut, Ankara, Turkey

Surface Review and Letters (SRL), 2022, vol. 29, issue 06, 1-17

Abstract: Ti–6Al–4V ELI alloy is one of the most familiar materials for orthopedic implants, aeronautical parts, marine components, oil and gas production equipment, and cryogenic vessel applications. Therefore, its appropriate quality of finishing is highly essential for these applications. But the characteristics like lower modulus of elasticity, lesser thermal conductivity, and high chemical sensitivity placed it in the categories of difficult-to-cut metal alloys. Also, tooling cost is one of the prime issues in the machining of this alloy. Therefore, this research is more inclined to use a low-budget uncoated carbide tool in turning the Ti–6Al–4V ELI alloy. Also, the selection of suitable levels of machining parameters is highly indispensable to get the appropriate surface finish with a low tooling cost. So, the L16 experimental design is utilized to check the performances of the uncoated carbide tool in the turning tests. The performance indexes like surface roughness (Ra), flank wear of tool (VBc), and material removal rate (MRR) are measured and studied with the help of surface plots and interaction plots. Further, the Firefly Algorithm optimization is employed to find the optimal cutting parameters and cutting response values. The local optimal values of the input parameters a, f, and Vc are estimated as 0.3241mm, 0.0893mm/rev, and 82.41m/min, respectively. Similarly, the global optimal values for the responses Ra, VBc, and MRR are reported as 0.6321μm, 0.09253mm, and 24.61g/min, individually. Additionally, to predict the responses, Generalized Regression Neural Network (GRNN) modeling is employed and the average absolute error for each response is noticed to be less than 1%. Therefore, the GRNN modeling tool is strongly recommended for various machining applications.

Keywords: Ti–6Al–4V ELI; surface roughness; flank wear; Firefly Algorithm; GRNN (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218625X22500755

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