Predicting and optimizing control parameters of stir casting of Al alloy/MWCNT/RHA composite using artificial neural network and Taguchi-Grey relational analysis for multi-objective outcomes
Nitin Srivastava, Manoj Kumar Yadav, Selsam Ajith Arul Daniel, Alok Bhadauria, Lavish Kumar Singh
TL;DR
This study uses artificial neural networks and statistical methods to optimize the production of a composite material for better mechanical properties.
Contribution
A novel combination of ANN and Taguchi-Grey relational analysis is used to optimize multi-objective composite casting parameters.
Findings
The ANN model achieved a high R2-score of 99.65% in predicting tensile strength.
MWCNT content had the most significant impact on composite properties with 48.26% contribution.
Optimized parameters combination A2B3C3D2E2 produced the best composite properties.
Abstract
In the present investigation, the influence of various casting parameters viz. stirrer time, stirrer speed, and processing temperature and reinforcement content on the mechanical properties of AlP0507/CNT/RHA composite is assessed. The optimum parameter combination that produces greater multi-objective performance was obtained using the GRA method. The comparison of all R2-score showed that the ANN model is best fitted to predict the tensile strength of HAMMC with highest R2- score of 99.65%. GRA established that the MWCNT content has most significant influence on the response parameters followed by stirring time, RHA content, stirring speed and processing temperature; and the best properties of stir cast HAMMCs was obtained by the combination A2B3C3D2E2. ANNOVA performed on GRA indicated that MWCNT content with contribution of 48.26% exerted maximum impact on the properties of the…
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Taxonomy
TopicsAluminum Alloys Composites Properties · Scientific and Engineering Research Topics · Advanced machining processes and optimization