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 study investigated the predicted cutting force model of a turning operation for Al–Si–Cu cast alloy modified with modifiers based on adaptive neuro-fuzzy inference system (ANFIS) approach. Feed rate, cutting speed and Silicon spacing were considered as the input parameters. A series of turning experiments were conducted at various feed rates and cutting speeds. The prediction result showed that the ANFIS model successfully predicted the cutting force value in terms of cutting speed, feed rate and Si spacing. A mathematical model was proposed to describe the cutting force changes during the machining of Al–Si–Cu cast alloy. Moreover, the addition of Bismuth into the base alloy decreased the cutting force compared to other refinement elements.

Neuro-fuzzy based predictive model for cutting force in CNC turning process of Al–Si–Cu cast alloy using modifier elements