| Type | Conference |
|---|---|
| Title | White layer thickness prediction in WEDM-ANFIS modeling |
| Source | MITC2015-pp 240-241 |
| Author | Ibrahem Maher, Ahmed A.D. Sarhan, Houriyeh Marashi, Mohsen Marani Barzani, M. Hamdi |
Wire Electric Discharge Machining
(WEDM) is a nontraditional technique by which the
required profile is acquired using spark energy.
Regarding wire cutting, precision machining is
necessary to achieve high product quality. White Layer
Thickness (WLT) is one of the most important factors
for assessing superior surface finish. In this research,
Adaptive Neuro-fuzzy Inference System (ANFIS) was
used to predict the WLT in WEDM using coated wire
electrode. The predicted data were compared with
measured values, and the average prediction error for
WLT was 2.61 %.