English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Selection of appropriate operating conditions is an important attribute to pay attention for in electrical discharge machining (EDM) of steel parts. The achievement of EDM process is affected by many input parameters; therefore, the computational relations between the output responses and controllable input parameters must be known. However, the proper selection of these parameters is a complex task and it is generally made with the help of sophisticated numerical models. This study investigates the capacity of Adaptive Nero-Fuzzy Inference System (ANFIS), genetic expression programming (GEP) and artificial neural networks (ANN) in the prediction of EDM performance parameters. The datasets used in modelling study were taken from experimental study. According to the results of estimating the parameters of all models in the comparison in terms of statistical performance is sufficient, but observed that ANFIS model is slightly better than the other models

Comparison of Soft Computing Techniques for Modelling of the EDM Performance Parameters