Evaluating and optimizing surface roughness using genetic algorithm and artificial neural networks during turning of AISI 52100 steel

The major activity in research of metal cutting is to derive the mathematical models for surface roughness in turning. The present work aims at experimental investigation of hard turning of AISI 52100 bearing steel in order to determine the combined effects of tool geometry (nose radius and rake ang...

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Bibliographic Details
Published in:International journal on interactive design and manufacturing 2024-10, Vol.18 (8), p.6151-6160
Main Authors: Rao, G. Srinivasa, Mukkamala, Usha, Hanumanthappa, Harish, Prasad, C. Durga, Vasudev, Hitesh, Shanmugam, Bharath, KishoreKumar, K. Ch
Format: Article
Language:English
Subjects:
Artificial neural networks
Bearing steels
CAE) and Design
Chromium steels
Computer-Aided Engineering (CAD
Cutting tools
Design analysis
Design factors
Design of experiments
Design optimization
Electronics and Microelectronics
Engineering
Engineering Design
Genetic algorithms
Industrial Design
Instrumentation
Mathematical analysis
Mechanical Engineering
Metal cutting
Neural networks
Optimization
Original Paper
Orthogonal arrays
Prediction models
Process parameters
Productivity
Rake angle
Surface roughness
Surface roughness effects
Turning (machining)
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Summary:The major activity in research of metal cutting is to derive the mathematical models for surface roughness in turning. The present work aims at experimental investigation of hard turning of AISI 52100 bearing steel in order to determine the combined effects of tool geometry (nose radius and rake angle) and process parameters (speed, feed and depth of cut) on the performance characteristic surface roughness. Experiments were conducted using orthogonal array and central composite face centered (CCF) design. A mathematical prediction model for SR has been obtained in terms of factors mentioned. 3D response graphs were drawn to study the interaction effects of surface roughness. Prediction for orthogonal array is done through artificial neural network. Optimization of surface roughness was also carried out for CCF design using genetic algorithm. The efficacy of the CCF design over orthogonal array design is evaluated. Minimum surface roughness of 0.985 µm is obtained when A = 90m/min, B = 0.052mm/rev, C = 0.6mm, D = 4mm and E = − 12 O after application of genetic algorithm.
ISSN:1955-2513
1955-2505
DOI:10.1007/s12008-023-01549-5