| Issue |
MATEC Web of Conferences
Volume 172, 2018
3rd International Conference on Design, Analysis, Manufacturing and Simulation (ICDAMS 2018)
|
|
|---|---|---|
| Article Number | 04010 | |
| Number of page(s) | 7 | |
| Section | Manufacturing Engineering | |
| DOI | https://doi.org/10.1051/matecconf/201817204010 | |
| Published online | 12 June 2018 | |
Modeling and experimental investigation of process parameters in WEDM for surface roughness using regression model
Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
* Corresponding author: inspire.munish@gmail.com
The present study focused on the multiple regression modeling and predicting the surface roughness of the Aluminum hybrid composite during the WEDM process. The hybrid MMC was manufactured by process named as stir casting utilizing particulates of Silicon carbide and graphite each in Al6061 combination. The analyses were outlined with Taguchi L27 design matrix. Mathematical relationships between the surface roughness and WEDM cutting parameters (Pulse on time, Pulse off time, current, gap voltage, wire speed and wire tension) have been investigated. The results show that the multiple regression analysis is a successful method for developing a mathematical model to predict the surface roughness. The optimum value of process parameters for the predicted optimum value of surface roughness (1.285) is pulse on time 106 units (Level 1), pulse off time 60 units (Level 3), peak current 90 units (Level 2), gap set voltage 50 units (Level 3), wire speed3 units (Level 1) and wire tension 12 units (Level 3).The optimum results are adopted in validation study and the results based on WEDM process responses can be effectively improved.
Key words: WEDM / Taguchi method regression model / stir casting / hybrid composite and surface roughness
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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