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

The study focused on analysis and prediction of cutting force and surface roughness during end milling of aluminium based Metal Matrix Composite using Artificial Neural Network (ANN) and Response Surface Methodology (RSM). The chosen material, Al-SiC composites were synthesized through Powder Metallurgy route, by mixing A356 Aluminium alloy powder of 25–50 μm average size with 10 volume % SiC particles of three different sizes (1, 12.5 and 25 μm). The mixed powders were compacted using Vacuum Hot Press to achieve near theoretical densities. The influence of SiC particle size on the composites in terms of density and microstructure were investigated. The effect of cutting speed, feed, depth of cut and SiC particle size on cutting force and surface roughness were also investigated. The results revealed that the cutting force and surface finish were decreased with increasing cutting speed. On the other hand, increase in feed, depth of cut and size of SiC particle decreased both the responses. The 4-10-2 multi layer feed forward neural network model predicts both responses with high accuracy in terms of higher correlation coefficient and lower mean square error compared to the regression model.

Analysis and Prediction of End Milling Characteristics of Al-SiC p Metal Matrix Composite Using RSM and ANN