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Optimization of Surface Roughness: Effect of Machining Parameters on EN23

EasyChair Preprint 9158

6 pagesDate: October 26, 2022

Abstract

EN23 is one of the most commonly used material and used to fabricate die for forging purposes in Indian industries. The dies are manufactured through machining process and it was observed that the machining process parametric values effects the surface roughness of the material significantly. While forging it was found that die life vary with the change in the machining parametric value and this took my interest to investigate in the area. The machining parameters (spindle speed, feed rate and depth of cut) were selected for the investigation of the process. Design of experiment was used to know the number of specimen required for proper investigation. Specimens were prepare as per full factorial and L9 orthogonal array. Taguchi method was used to obtain the optimized machining parameters. The results were compared and it was found that the optimized surface roughness value obtained using L9 orthogonal array is very close to the value obtained through full factorial method. Further the optimization process was modelled using a mathematical tool ANFIS to predict the surface roughness values.

Keyphrases: Design of Experiments, Orthogonal array, SN ratio, Turning operation

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@booklet{EasyChair:9158,
  author    = {Kriti Barnwal and Deepika Mishra and Ravi Prasad},
  title     = {Optimization of Surface Roughness: Effect of Machining Parameters on EN23},
  howpublished = {EasyChair Preprint 9158},
  year      = {EasyChair, 2022}}
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