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Estimation of Roughness of Machined Surface Using Artificial Neural Networks


Affiliations
1 Department of Mechanical Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia, India
2 Department of Computer Science & Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia, India
     

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Setting appropriate machining parameters can give desired finish of a job. Selection
of such parameters needs time consuming and costly experimentation. In this work,
artificial neural networks (ANN) is used to predict roughness parameters of machined
surface to reduce time and cost involved for the experiments. Surface roughness
parameters assessed through ANN are compared with the observed data and an
accuracy of 95.5% is reported.

Keywords

Machining; shaping; surface roughness; estimation; ANN; Neural Networks.
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  • Estimation of Roughness of Machined Surface Using Artificial Neural Networks

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Authors

Firdous Ali Khan
Department of Mechanical Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia, India
Pritam Chatterjee
Department of Mechanical Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia, India
Sumit Mandi
Department of Mechanical Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia, India
Upendra Kumar Shaw
Department of Mechanical Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia, India
Santanu Das
Department of Mechanical Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia, India
Supriyo Banerjee
Department of Computer Science & Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia, India

Abstract


Setting appropriate machining parameters can give desired finish of a job. Selection
of such parameters needs time consuming and costly experimentation. In this work,
artificial neural networks (ANN) is used to predict roughness parameters of machined
surface to reduce time and cost involved for the experiments. Surface roughness
parameters assessed through ANN are compared with the observed data and an
accuracy of 95.5% is reported.

Keywords


Machining; shaping; surface roughness; estimation; ANN; Neural Networks.

References





DOI: https://doi.org/10.24906/isc%2F2022%2Fv36%2Fi3%2F213770