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A Novel Approach To Predict Green Density By High-velocity Compaction Based On The Materials Informatics Method

Kai-qi Zhang, Haiqing Yin, Xue Jiang, X. Liu, F. He, ZhengHua Deng, D. F. Khan, Q. Zheng, X. Qu
Published 2019 · Materials Science

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High-velocity compaction is an advanced compaction technique to obtain high-density compacts at a compaction velocity of ≤10 m/s. It was applied to various metallic powders and was verified to achieve a density greater than 7.5 g/cm3 for the Fe-based powders. The ability to rapidly and accurately predict the green density of compacts is important, especially as an alternative to costly and time-consuming materials design by trial and error. In this paper, we propose a machine-learning approach based on materials informatics to predict the green density of compacts using relevant material descriptors, including chemical composition, powder properties, and compaction energy. We investigated four models using an experimental dataset for appropriate model selection and found the multilayer perceptron model worked well, providing distinguished prediction performance, with a high correlation coefficient and low error values. Applying this model, we predicted the green density of nine materials on the basis of specific processing parameters. The predicted green density agreed very well with the experimental results for each material, with an inaccuracy less than 2%. The prediction accuracy of the developed method was thus confirmed by comparison with experimental results.
This paper references
10.1002/9783527646098
Integrative computational materials engineering : concepts and applications of a modular simulation platform
G. J. Schmitz (2012)
10.1016/j.neucom.2017.11.077
Feature selection in machine learning: A new perspective
Jie Cai (2018)
10.1016/0955-2219(93)90053-T
Fracture mechanics of green products
D. Bortzmeyer (1993)
10.1179/imr.1991.36.1.45
Manufacture of powder compacts
A. Rosato (1991)
10.1007/978-3-662-46221-8_16
Probability Theory and Mathematical Statistics
I. Bronshteĭn (1987)
10.1007/s40195-014-0184-6
Modeling of the Prediction of Densification Behavior of Powder Metallurgy Al–Cu–Mg/B4C Composites Using Artificial Neural Networks
T. Varol (2014)
10.1016/J.POLYMER.2007.07.058
Microstructural origin of physical and mechanical properties of ultra high molecular weight polyethylene processed by high velocity compaction
D. Jauffrès (2007)
10.1515/9783111576855-015
J
Seguin Hen (1824)
The Principle of Powder Metallurgy
P. Y. Huang (1997)
10.1016/J.ACTAMAT.2016.12.009
An informatics approach to transformation temperatures of NiTi-based shape memory alloys
D. Xue (2017)
10.2307/j.ctvrnfqk1.10
? ? ? ? f ? ? ? ? ?
A. ADoefaa (2003)
10.1016/j.eswa.2018.03.053
EEG signal classification using universum support vector machine
B. Richhariya (2018)
10.1016/S1006-7191(08)60122-2
Analysis of density and mechanical properties of high velocity compacted iron powder
Jianzhong Wang (2009)
10.1179/pom.1978.21.4.179
Particle deformation and sliding during compaction of spherical powders : a study by quantitative metallography
H. Fischmeister (1978)
10.1007/978-1-84996-323-7
Finite-element-model Updating Using Computional Intelligence Techniques
T. Marwala (2010)
10.1016/S1352-2310(97)00447-0
Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences
M. W. Gardner (1998)
10.1016/J.COMMATSCI.2018.01.039
MatCloud: A high-throughput computational infrastructure for integrated management of materials simulation, data and resources
Xiaoyu Yang (2018)
10.1038/nature17439
Machine-learning-assisted materials discovery using failed experiments
Paul Raccuglia (2016)
10.1016/j.actamat.2018.03.051
Compositional optimization of hard-magnetic phases with machine-learning models
Johannes J. Moller (2018)
10.1039/c6dt01501h
Materials informatics: a journey towards material design and synthesis.
K. Takahashi (2016)
10.1007/s10489-016-0764-4
A second-order cone programming formulation for twin support vector machines
Sebastián Maldonado (2016)
Analysis of density and mechanical properties of high velocity compacted iron powder
Qu State (2009)
10.1146/ANNUREV-MATSCI-070214-021132
Materials Informatics: The Materials ``Gene'' and Big Data
K. Rajan (2015)
Materials Genome Initiative for Global Competitiveness
C. Ward (2012)
10.1016/j.eswa.2017.11.026
Empirical studies of Gaussian process based Bayesian optimization using evolutionary computation for materials informatics
H. Ohno (2018)
Powder metallurgy and particulate materials processing : the processes, materials, products, properties and applications
R. German (2005)
10.1002/anie.201208487
The catalyst genome.
J. Nørskov (2013)
High density PM parts by high velocity compaction
P. Skoglund (2001)
Mg/B 4 C composites using artificial neural networks
Al-Cu (2015)
10.1016/J.SURFCOAT.2005.08.097
Materials informatics for the design of novel coatings
L. Zhao (2005)
10.1038/ncomms11241
Accelerated search for materials with targeted properties by adaptive design
Dezhen Xue (2016)
10.5194/GMD-7-1247-2014
Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
T. Chai (2014)
10.1016/J.POWTEC.2013.05.043
Preparation and properties of Ti–4.5Al–6.8Mo–1.5Fe alloy by high-velocity compaction
Zhiqiao Yan (2013)
10.1007/S11661-007-9153-2
The Effect of Particle Shape on the Sintering of Aluminum
Z. Liu (2007)
10.1016/j.aca.2010.03.030
A tutorial on support vector machine-based methods for classification problems in chemometrics.
J. Luts (2010)
Introduction to machine learning
Ethem Alpaydin (2004)
10.1016/J.COMMATSCI.2017.09.061
An materials informatics approach to Ni-based single crystal superalloys lattice misfit prediction
Xue Jiang (2018)
Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics
T. Marwala (2010)
10.1063/1.4812323
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
A. Jain (2013)
10.1016/j.eswa.2016.10.038
Growing random forest on deep convolutional neural networks for scene categorization
Shuang Bai (2017)
10.1016/J.COMMATSCI.2017.03.027
Data mining assisted materials design of layered double hydroxide with desired specific surface area
B. Hu (2017)
10.1179/174328406X102354
High velocity compaction compared with conventional compaction
G. Sethi (2006)
A novel approach to predict green density by high-velocity compaction based on the materials … 201 chine-based methods for classification problems in chemometrics
K Q Zhang (2010)
10.1007/978-3-319-63913-0
An Introduction to Machine Learning
M. Kubat (2017)



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