Abstract
The present research paper deals with dry ball burnishing process undertaken to give significant improvements in both surface finish and surface hardness required for most of applications. Aluminum alloy (Al 6061) has been burnished using different burnishing parameters (number of revolution, feed, number of tool passes, and pressure force) with burnishing apparatus. A neuro-fuzzy inference model is generated from the experimental results, and genetic algorithm (GA) is employed to search the optimal solution on the response surfaces modeled by neuro-fuzzy inference system. The absolute average error between the experimental and predicted values from neuro-fuzzy inference model for surface roughness and surface hardness was calculated as 0.05 and 0.18 %. The optimum parameters found by GA in dry ball burnishing are feed 0.157 mm/rev, force 13.91 kgf, rotational speed 145.09 rpm with two tool passes having response characteristic i.e., surface roughness 0.815 μm and surface hardness 71.3 HRB.
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Singh, J., Bilga, P.S. (2014). Optimization of Dry Ball Burnishing Process Using Neuro-Fuzzy Interface System and Genetic Algorithm. In: Khangura, S., Singh, P., Singh, H., Brar, G. (eds) Proceedings of the International Conference on Research and Innovations in Mechanical Engineering. Lecture Notes in Mechanical Engineering. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1859-3_15
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DOI: https://doi.org/10.1007/978-81-322-1859-3_15
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