Machine leaning aided study of sintered density in Cu-Al alloy
Graphical abstract
Introduction
Powder metallurgy (PM), with the descriptors of energy-efficient near-net forming and no (less) pollution, plays an irreplaceable role in the materials manufacturing [1], [2]. The sintered density is one of the key factors that determine the mechanical properties of powder metallurgy parts [3]. An increase of the porosity from 0 to 5% decreases the tensile strength by 35% [4]. Ductility and resistance to fatigue show higher sensitivity to porosity [5]. At present, most of the sintered density prediction depends on the priori knowledge and trial and error experiments, which greatly decelerate the efficiency of new materials discovery.
Therefore, a more efficient method is urgently needed to predict the sintered density. The Materials Genome Initiative (MGI) [6] and Integrated Computational Materials Engineering [7] (ICME) are deemed to accelerating the materials design process. Xue et al. [8] discovered a new NiTi-based shape memory alloy with targeted transformation temperatures by Support Vector Regression method. Raccuglia [9] used support vector machine methods to predict the viability of untested reactions with experimental failed data collected in the lab and the accuracy of prediction is nearly 90%.
Machine learning methods have been used to predict the sintered density in recent years. Jabar et al. [10] used artificial neural network (ANN) to predict the sintered density of BaTiO3 and the mean error is less than 0.06. Varol et al. [11] used ANN to predict the effect of reinforcement on density, hardness and tensile strength of B4Cp/Al2024 composites, with the mean absolute percentage error (MAPE) obtained less than 2.15%. Canakci et al. [12] used ANN to predict green density, sintered density and hardness of Al2O3/Al metal matrix composites (MMCs) successfully with MAPE less than 5.53%. Azadbeh et al. [13] used response surface methodology models to predict the density, electrical resistivity and hardness of Cr–Mo prealloyed sintered steels, the predicted values agreed well with the experimental ones. The previous investigations, however, focused studies solely on single kind of material for prediction without taking into account of the raw material and the phase transformation, and didn’t compare the prediction accuracy of different machine learning methods.
In the present work, machine learning algorithms were used to generate the models to estimate the sintered density of metal compacts by Powder metallurgy. And the obtained model is applied here to predict the sintered density Cu-9Al alloy as well as provide guidance for selection of processing parameters to reach the expected target.
Section snippets
Procedure of modeling
The feedback mechanism of the machine learning modeling and sintering optimization process are described in Fig. 1. Machine-learning models generated from experimental data are used to guide selection of processing parameters to reach the expected sintered density.
Model construction
A comparison of the models training results with the experimental ones is shown in Fig. 3, where the x-axis is the relative sintered density from experiments and the y-axis from models prediction. For an ideal model, the data points are more close to diagonal line. In this case MLP model performs better than the others.
In this case, the coefficient of correlation (R), mean absolute error (MAE) and root mean squared error (RMSE) [35], [36] are used to evaluate the feasibility and validation of
Machine leaning model guides the selection of parameters
The obtained MLP model is applied here to predict the sintered density Cu-9Al alloy as well as provide guidance for selection of processing parameters to reach the expected target without the time consuming trial-and-error method. In this case, we focus solely on the data of Cu-Al alloys in MLP predicted values for investigating the correlations between each descriptor and the sintered density, with chemical composition and sintering atmosphere negleted. The results are shown in Fig. 6. The
Conclusions
In this work, five algorithms are used for modeling. By comparing five prediction models, the fitting of the MLP model was the best with the predicted value of MLP model was in well agreement with the experimental value, and the maximum error value does not exceed 0.028. The MLP model is applied to predict the sintered density of Cu-Al alloy as well as provide guidance for selection of processing parameters to reach the expected target. The selected Cu and Al powders for Cu-9Al alloy were die
CRediT authorship contribution statement
ZHD and HQY: conceived and designed the study. ZHD and HQY wrote the manuscript. ZHD, KQZ, TZ, BX and QJZ collected data. ZHD and XJ performed descriptor selection and model construction. CZ calculated volume change induced by the phase transformation. ZHD, KQZ, TZ performed the experiments. ZHD, HQY and XHQ reviewed and edited the manuscript. All authors read and approved the manuscript.
Acknowledgement
The National Key Research and Development Program (2016YFB0700503), the National High Technology Research and Development Program of China (2015AA03420), Beijing Science and Technology Plan (D16110300240000), National Natural Science Foundation of China (No. 51172018) and Kennametal Inc. are greatly acknowledged.
References (37)
- et al.
A neural network approach for selection of powder metallurgy materials and process parameters
Artif. Intell. Eng.
(2000) - et al.
An informatics approach to transformation temperatures of NiTi-based shape memory alloys
Acta Materialia
(2017) - et al.
Artificial neural network modeling to effect of reinforcement properties on the physical and mechanical properties of Al2024–B4C composites produced by powder metallurgy
Compos. B Eng.
(2013) - et al.
Modeling the response of physical and mechanical properties of Cr–Mo prealloyed sintered steels to key manufacturing parameters
Mater. Des.
(2014) - et al.
Compaction, sintering and mechanical properties of elemental 6061 Al powder with and without sintering aids
Mater. Des.
(2008) - et al.
Effects of mechanical milling and sintering temperature on the densification, microstructure and tensile properties of the Fe–Mn–Si powder compacts
J. Mater. Sci. Technol.
(2016) - et al.
The role of alloying elements on the sintering of Cu
J. Alloy. Compd.
(2016) - et al.
Liquid phase sintering of mechanically alloyed Mo-Cu powders
Mater. Sci. Eng., A
(2017) - et al.
The liquid phase sintering of molybdenum with Ni and Cu additions
Mater. Chem. Phys.
(2001) - et al.
Effect of iron addition on microstructure, mechanical and magnetic properties of Al-matrix composite produced by powder metallurgy route
Trans. Nonferr. Metals Soc. China
(2015)