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

Saleh Asnaashari; Mohammadhadi Shateri; Abdolhossein Hemmati-Sarapardeh; Shahab S Band

doi:10.1021/acsomega.2c07278

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

ACS Omega. 2023 Jul 25;8(31):28036-28051. doi: 10.1021/acsomega.2c07278. eCollection 2023 Aug 8.

Authors

Saleh Asnaashari¹, Mohammadhadi Shateri², Abdolhossein Hemmati-Sarapardeh³, Shahab S Band⁴

Affiliations

¹ School of Metallurgy and Materials Engineering, University College of Engineering, University of Tehran, Tehran 7761968875, Iran.
² Department of System Engineering, École de Technologie Supérieur, Montreal, QC H3C 1K3, Canada.
³ Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman 7617117330, Iran.
⁴ Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, ROC.

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, 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.