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A platform for research: civil engineering, architecture and urbanism
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 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 = − 12O after application of genetic algorithm.
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 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 = − 12O after application of genetic algorithm.
Evaluating and optimizing surface roughness using genetic algorithm and artificial neural networks during turning of AISI 52100 steel
Int J Interact Des Manuf
Rao, G. Srinivasa (author) / Mukkamala, Usha (author) / Hanumanthappa, Harish (author) / Prasad, C. Durga (author) / Vasudev, Hitesh (author) / Shanmugam, Bharath (author) / KishoreKumar, K. Ch. (author)
2024-10-01
10 pages
Article (Journal)
Electronic Resource
English
Analysis of white layers formed in hard turning of AISI 52100 steel
| British Library Online Contents | 2005
| British Library Online Contents | 2017