• Krishnan, R. Murali
• Jatti, Vijaykumar S.
• Balaji, K.
• Saiyathibrahim, A.

Abstract

<jats:p>&lt;div&gt;This research examines the impact of different amounts of copper (Cu) powder onthe wear characteristics of acrylonitrile butadiene styrene (ABS)–Cu composites.Various formulations of ABS–Cu composites have been produced using injectionmolding, with different amounts of surfactant. Wear properties were evaluated byconducting tribological testing in accordance with ASTM standards. The findingsindicated a decrease in wear loss, particularly when using a mixture consistingof 23% ABS, 70% Cu, and 7% surfactant. Machine learning regression algorithmssuccessfully forecasted wear behavior with R-squared values over 0.97. Themodels used in the analysis included linear, stepwise linear, tree, supportvector machine (SVM), efficient linear, Gaussian progression, ensemble, andneural network regression models. This research emphasizes the significance ofcomposite materials in fulfilling contemporary technical requirements. Theacquired insights enable the development of materials with customized wearcharacteristics. These findings have important consequences for a range ofindustrial applications.&lt;/div&gt;</jats:p>

Topics

• polymer
• composite
• copper
• surfactant
• machine learning
• copper powder