Modeling of the Sintered Density in Cu-Al Alloy Using Machine Learning Approaches.

ABSTRACT: In powder metallurgy materials, sintered density in Cu-Al alloy plays a critical role in detecting mechanical properties. Experimental measurement of this property is costly and time-consuming. In this study, adaptive boosting decision tree, support vector regression, k-nearest neighbors, extreme gradient boosting, and four multilayer perceptron (MLP) models tuned by resilient backpropagation, Levenberg-Marquardt (LM), scaled conjugate gradient, and Bayesian regularization were employed for predicting powder densification through sintering. Yield strength, Young's modulus, volume variation caused by the phase transformation, hardness, liquid volume, liquidus temperature, the solubility ratio among the liquid phase and the solid phase, sintered temperature, solidus temperature, sinte

SUBMITTER: Asnaashari S 

PROVIDER: S-EPMC10413372 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature