Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Artificial Neural Network Applied to Acoustic Emission Based Surface Roughness Monitoring in Face Milling


Affiliations
1 Dept. of Mechanical Engg., NMAM Institute of Technology, Nitte-574110, India
2 Dept. of Information Science & Engg., S.J. College of Engineering, Mysore-570006, India
     

   Subscribe/Renew Journal


This paper attempts to monitor the surface roughness caused by the increase of tool wear (flank wear), through the variations of acoustic emission in face milling operations under different cutting conditions and for one workpiece material. The analysis revealed that there is a good correlation between two important parameters of the acoustic emission signal namely ring down count and RMS voltage with the surface roughness parameter namely Ra. The results show that acoustic emission can be used as an effective signal for monitoring surface roughness in face milling and thereby can be useful for establishing the end of tool life in face milling operations. Artificial Neural Network (ANN) is a powerful tool that can be applied to many scientific and engineering problems. In this paper a simple feed forward ANN has been applied to predict surface roughness using acoustic emission parameters, flank wear and cutting conditions.
User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 245

PDF Views: 0




  • Artificial Neural Network Applied to Acoustic Emission Based Surface Roughness Monitoring in Face Milling

Abstract Views: 245  |  PDF Views: 0

Authors

P. Srinivasa Pai
Dept. of Mechanical Engg., NMAM Institute of Technology, Nitte-574110, India
T. N. Nagabhushana
Dept. of Information Science & Engg., S.J. College of Engineering, Mysore-570006, India

Abstract


This paper attempts to monitor the surface roughness caused by the increase of tool wear (flank wear), through the variations of acoustic emission in face milling operations under different cutting conditions and for one workpiece material. The analysis revealed that there is a good correlation between two important parameters of the acoustic emission signal namely ring down count and RMS voltage with the surface roughness parameter namely Ra. The results show that acoustic emission can be used as an effective signal for monitoring surface roughness in face milling and thereby can be useful for establishing the end of tool life in face milling operations. Artificial Neural Network (ANN) is a powerful tool that can be applied to many scientific and engineering problems. In this paper a simple feed forward ANN has been applied to predict surface roughness using acoustic emission parameters, flank wear and cutting conditions.