Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)

Optimization of Process Parameters of Nickel – Chromium Electroplating for Thickness Variation using Genetic Algorithm

Authors
D. Khedekar, V. Gosavi, C. Gogte, P. Brahmankar
Corresponding Author
D. Khedekar
Available Online December 2016.
DOI
https://doi.org/10.2991/iccasp-16.2017.7How to use a DOI?
Keywords
Surface Engineering, Anodic, Semi Bright, Genetic Algorithm
Abstract
Surface is very critical from quality point of view which affecting the life and the performance of products. Surface properties can be improved by number of methods, but electroplating is preferred because of its enhanced results at lower cost. In this work nickel chrome is selected for experimentation as it is widely used in industry especially in automotive sectors for plating many of the automotive components. The electroplating of nickel chrome not only improves the surface properties but also aesthetics of the component which is also a vital part of automobile requirement. Experiments are planned by Taguchi L16 orthogonal array and effect of density of solutions and time for processes at two levels on thickness variation is studied. Mathematical model were developed by regression analysis for thickness variation. Genetic Algorithm is used for optimization of process parameters. It was observed that the effect of time is more dominating for the thickness variation.
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Proceedings
International Conference on Communication and Signal Processing 2016 (ICCASP 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2016
ISBN
978-94-6252-305-0
ISSN
1951-6851
DOI
https://doi.org/10.2991/iccasp-16.2017.7How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - D. Khedekar
AU  - V. Gosavi
AU  - C. Gogte
AU  - P. Brahmankar
PY  - 2016/12
DA  - 2016/12
TI  - Optimization of Process Parameters of Nickel – Chromium Electroplating for Thickness Variation using Genetic Algorithm
BT  - International Conference on Communication and Signal Processing 2016 (ICCASP 2016)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasp-16.2017.7
DO  - https://doi.org/10.2991/iccasp-16.2017.7
ID  - Khedekar2016/12
ER  -