Manufacturing Technology Today

P. Srinivasa Pai ¹, T. N. Nagabhushana ²

Affiliations
1 Dept. of Mech. Engg., NMAM Institute of Technology, Nitte, IN
2 Dept. of Information Science & Engg., S. J. College of Engg., Mysore, IN

Abstract

Tool Condition Monitoring (TCM) is a very important aspect to maintain quality of products manufactured in any machining process. Tool wear is a complex phenomenon occurring in different and varied ways in metal cutting processes. There is a need to identify the tool condition well in advance and take necessary action like replacement, to prevent damaged tools from negatively affecting production in terms of excessive power takeoff, inaccurate tolerances and uneven workpiece surface finish and some times damage to the machine tools and also injury to the operator. There is a need to reliably detect the condition of the tool and take necessary steps. Artificial Neural Networks (ANN) has been widely used for sensor fusion in TCM. This paper presents a brief overview of ANN and followed by it presents an overview of the application of ANN for tool condition monitoring and briefly to other manufacturing processes. Conclusions at the end of the paper contain a brief summary of some of the observations relevant to the application of ANN to TCM.

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P. Srinivasa Pai ¹, T. N. Nagabhushana ²

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

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 R_a. 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.

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