Preparation and characterization of composite WC / Co through rapid omnidirectional compaction

Asnaashari, Saleh; Ghambari, Mohammad

doi:10.1016/j.jallcom.2020.157764

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…Powder metallurgy (PM), due to its ability to remove the need for secondary operations, is one of the most effective techniques for manufacturing the near-net-shape and complex components, and in some cases, it plays an indispensable role in achieving low-cost parts. , It also has major advantages like a homogeneous microstructure, lower grain growth, and lower processing temperature . Metallurgical technology includes materials with enhanced properties of strength .…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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, sintered atmosphere, holding time, compaction pressure, particle size, and specific shape factor were regarded as the input parameters of the suggested models. The cross plot, error distribution curve, and cumulative frequency diagram as graphical tools and average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), standard deviation (SD), and coefficient of correlation (R) as the statistical evaluations were utilized to estimate the models' accuracy. All of the developed models were compared with preexisting approaches, and the results exhibited that the developed models in the present work are more precise and valid than the existing ones. The designed MLP-LM model was found to be the most precise approach with AAPRE = 1.292%, APRE = −0.032%, SD = 0.020, RMSE = 0.016, and R = 0.989. Lately, outlier detection was applied performing the leverage technique to detect the suspected data points. The outlier detection discovered that few points are located out of the applicability domain of the proposed MLP-LM model.

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