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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper presents a new method of modeling the nonlinear parameters of a coating systems base on neural Networks with artificial neural network  neurons. Artificial neural networks (ANNs) are a new type of information processing system based on modeling the neural  system of human brain. The wire coating thickness and quality depend on the wire speed, polymer viscosity, polymer melt temperature and the gap between the wire and exit end of the die. In this paper, results of experimental investigation are presented by comparing the coating quality on galvanized mild steel wire using EP 58 PVC molten is used as the coating material in a wire coating extrus?on unit at different extruder temperatures and extruder speeds. The coating thickness and quality are also discussed for different wire speeds of up to 15 m/s. A three layer back propogation artificial neutral network (ANN) model was used for the description of wire coating thickness.On comparing the experimental data, the predictions the ANN model predictions, it is found that the ANN model is capable of predicting the coating thickness. The neural network model shows how the significant parameters influencing thickness can be found. Inthis studies, a back propagation neural network model is developed to map the complex non-linear wire coating thickness between process conditions .  </span

Prediction of the coating thickness of wire coating extrusion processes using artificial neural network (ANN)