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Prediction and Comparison of Vision Parameter of Surface Roughness in WEDM of Al-6%Si3N4 and Al-10%Si3N4 Using ANN

  • H. R. GurupavanEmail author
  • H. V. Ravindra
  • T. M. Devegowda
Conference paper
Part of the Lecture Notes on Multidisciplinary Industrial Engineering book series (LNMUINEN)

Abstract

The need for composite material has increased in various sectors due to the technological developments and requirement of complex shapes in the manufacturing sectors. Metal matrix composites are the most widely used composite materials. Due to the presence of abrasive reinforcing particles, conventional machining of these causes severe tool wear and hence reduces the life of the cutting tool. This difficulty can be overcome by using an advanced machining technique. One such advanced machining technique is Wire Electrical Discharge Machining (WEDM). WEDM is a thermal machining method used for cutting any conductive materials and capable of precisely cutting parts of hard materials with complex shapes. This paper focuses on the prediction and comparison of vision parameter of surface roughness during wire electrical discharge machining of Al-6%Si3N4 and Al-10%Si3N4 composite materials. Stylus instruments are widely used for measuring surface roughness of machined components, which have limited flexibility in handling various machined parts. Due to high resolution, reliability, and ease of automatic processing of data, vision systems are recently being exploited for various measurements. Using the machine vision system, the surface images of machined specimens were acquired. Based on the analysis of the distribution of light intensity of a rough surface, the surface roughness (Ga) of a machined component is measured. Surface roughness prediction was carried out successfully for the two composite materials using Artificial Neural Networks (ANN). From the results, it was observed that, measured and predicted vision parameters of surface roughness (Ga) values correlated well with ANN.

Keywords

WEDM Surface roughness Machine vision and ANN 

References

  1. 1.
    Lee, B.Y., Tarng, Y.S.: Surface roughness inspection by computer vision in turning operations. Int. J. Mach. Tools Manuf. 41, 1251–1263 (2001)CrossRefGoogle Scholar
  2. 2.
    Kumar, R., Kulashekar, P., Dhanasekar, B., Ramamoorthy, B.: Application of digital image magnification for surface roughness evaluation using machine vision. Int. J. Mach. Tools Manuf. 45, 228–234 (2005)CrossRefGoogle Scholar
  3. 3.
    Shivanna, D.M., Kiran, M.B., Kavitha, S.D.: Evaluation of 3D surface roughness parameters of EDM components using vision system. Procedia Mater. Sci. 5, 2132–2141 (2014)CrossRefGoogle Scholar
  4. 4.
    Songli, Y., Jiahao, J., Yuxuan, W.: Design of surface roughness measuring system based on machine vision. Int. J. Innov. Res. Eng. Manag. (IJIREM) 3(4) (2016). ISSN: 2350-0557Google Scholar
  5. 5.
    Rajaram Narayanan, M., Gowri, S., Murali Krishna, M.: On line surface roughness measurement using image processing and machine vision. In: Proceedings of the World Congress on Engineering, London, UK, vol. 1 (2007). ISBN: 978-988-98671-5-7Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • H. R. Gurupavan
    • 1
    Email author
  • H. V. Ravindra
    • 1
  • T. M. Devegowda
    • 1
  1. 1.Department of Mechanical EngineeringP.E.S. College of EngineeringMandyaIndia

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