Capabilities of Auto-encoders and Principal Component Analysis of the reduction of microstructural images; Application on the acceleration of Phase-Field simulations

El Fetni, Seifallah; Habraken, Anne; Duchene, Laurent

doi:10.1016/j.commatsci.2022.111820

Article (Scientific journals)

Capabilities of Auto-encoders and Principal Component Analysis of the reduction of microstructural images; Application on the acceleration of Phase-Field simulations

El Fetni, Seifallah; Habraken, Anne; Duchene, Laurent

2022 • In Computational Materials Science, 216 (January 2023)

Peer Reviewed verified by ORBi Dataset

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https://hdl.handle.net/2268/295442

DOI
10.1016/j.commatsci.2022.111820

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Keywords :

Phase field; Spinodal decomposition; LSTM; GRU; Auto-encoders; PCA; HPC

Abstract :

[en] In this work, a data-driven framework based on Phase-Field simulations data is proposed to highlight the capabilities of neural networks to ensure accurate low dimensionality reduction of simulated microstructural images and to provide time-series analysis. The dataset was indeed constructed from high-fidelity Phase-Field simulations. Analyses demonstrated that the association of auto-encoder neural networks and principal component analyses leads to ensure efficient and significant dimensionality reduction: 1/196 of reduction ratio with more than 80% of accuracy. These findings give insight to apply analyses on data from the latent dimension. Application of Long Short Term Memory (LSTM) neural networks showed the possibility of making next frame predictions; that makes possible the acceleration of Phase-Field simulation without the need of high computing resources. We discussed the application of such a framework on various areas of research. Different methods are proposed from the conducted analyses, in order to ensure dimensionality reduction (auto-encoders, principal component analysis, Artificial Neural Networks) and time-series analysis (LSTM, Gated Recurrent Unit (GRU)).

Disciplines :

Materials science & engineering

Author, co-author :

El Fetni, Seifallah ; Université de Liège - ULiège > Urban and Environmental Engineering ; Université de Liège - ULiège > Département ArGEnCo > Département Argenco : Secteur MS2F

Habraken, Anne ; Université de Liège - ULiège > Département ArGEnCo > Département Argenco : Secteur MS2F ; Université de Liège - ULiège > Département de mécanique des matériaux et structures ; Université de Liège - ULiège > Département de mécanique des matériaux et structures > Service M & S

Duchene, Laurent ; Université de Liège - ULiège > Département ArGEnCo > Analyse multi-échelles dans le domaine des matériaux et structures du génie civil ; Université de Liège - ULiège > Département ArGEnCo > Département Argenco : Secteur MS2F

Language :

English

Title :

Capabilities of Auto-encoders and Principal Component Analysis of the reduction of microstructural images; Application on the acceleration of Phase-Field simulations

Publication date :

2022

Journal title :

Computational Materials Science

ISSN :

0927-0256

eISSN :

1879-0801

Publisher :

Elsevier, Netherlands

Volume :

216

Issue :

January 2023

Peer reviewed :

Peer Reviewed verified by ORBi

Tags :

CÉCI : Consortium des Équipements de Calcul Intensif

Development Goals :

9. Industry, innovation and infrastructure

Additional URL :

https://www.sciencedirect.com/science/article/pii/S0927025622005316#GS1

Funders :

ULiège research council of Sciences and Techniques

Funding text :

As Research Director of FRS-FNRS, AM Habraken acknowledges the support of this institution. The ULiège research council of Sciences and Techniques is acknowledged for the post-doc IN IPD-STEMA 2019 grant of Seifallah Fetni.

Data Set :

https://gitlab.uliege.be/S.Fetni/datasets-for-the-auto-encoder_lstm-project

Available on ORBi :

since 06 October 2022

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Bibliography

Gu, Y., He, X., Han, D., On the phase-field modeling of rapid solidification. Comput. Mater. Sci., 199, 2021, 110812, 10.1016/j.commatsci.2021.110812.
Lindroos, M., Pinomaa, T., Ammar, K., Laukkanen, A., Provatas, N., Forest, S., Dislocation density in cellular rapid solidification using phase field modeling and crystal plasticity. Int. J. Plast., 148, 2022, 103139, 10.1016/j.ijplas.2021.103139.
Boisse, J., Lecoq, N., Patte, R., Zapolsky, H., Phase-field simulation of coarsening of precipitates in an ordered matrix. Acta Mater. 55:18 (2007), 6151–6158, 10.1016/j.actamat.2007.07.014.
Li, X., Xu, Y., Phase field modeling scheme with mesostructure for crack propagation in concrete composite. Int. J. Solids Struct., 234–235, 2022, 111259, 10.1016/j.ijsolstr.2021.111259.
Wang, F., Huang, H., Zhang, D., Zhou, M., Cracking feature and mechanical behavior of shield tunnel lining simulated by a phase-field modeling method based on spectral decomposition. Tunn. Underg. Space Technol., 119, 2022, 104246, 10.1016/j.tust.2021.104246.
Aagesen, L.K., Schwen, D., Ahmed, K., Tonks, M.R., Quantifying elastic energy effects on interfacial energy in the kim-kim-suzuki phase-field model with different interpolation schemes. Comput. Mater. Sci. 140 (2017), 10–21.
Kumbhar, A., Dhawale, P.G., Kumbhar, S., Patil, U., Magdum, P., A comprehensive review: Machine learning and its application in integrated power system. Energy Rep. 7 (2021), 5467–5474, 10.1016/j.egyr.2021.08.133.
Rabbani, N., Kim, G.Y., Suarez, C.J., Chen, J.H., Applications of machine learning in routine laboratory medicine: Current state and future directions. Clin. Biochem., 2022, 10.1016/j.clinbiochem.2022.02.011.
Pham, T.Q.D., Hoang, T.V., Tran, X.V., Pham, Q.T., Fetni, S., Duchêne, L., Tran, H.S., Habraken, A.-M., Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learnin. J. Intell. Manuf., 36, 2020, 101641, 10.1007/s10845-021-01896-8.
Fetni, S., Pham, Q.D.T., Tran, V.X., Duchêne, L., Tran, H.S., Habraken, A.M., Thermal field prediction in DED manufacturing process using artificial neural network. 24th International Conference on Material Forming, 2021, 10.25518/esaform21.2812.
Pham, T., Hoang, T., Tran, X., Fetni, S., Duchêne, L., Tran, H., Habraken, A., Characterization, propagation, and sensitivity analysis of uncertainties in the directed energy deposition process using a deep learning-based surrogate model. Probab. Eng. Mech., 69, 2022, 103297, 10.1016/j.probengmech.2022.103297.
Ibragimova, O., Brahme, A., Muhammad, W., Lévesque, J., Inal, K., A new ANN based crystal plasticity model for FCC materials and its application to non-monotonic strain paths. Int. J. Plast., 144, 2021, 103059, 10.1016/j.ijplas.2021.103059.
Bhattacharyya, T., Brat Singh, S., Sikdar (Dey), S., Bhattacharyya, S., Bleck, W., Bhattacharjee, D., Microstructural prediction through artificial neural network (ANN) for development of transformation induced plasticity (TRIP) aided steel. Mater. Sci. Eng. A 565 (2013), 148–157, 10.1016/j.msea.2012.11.110.
Gola, J., Webel, J., Britz, D., Guitar, A., Staudt, T., Winter, M., Mücklich, F., Objective microstructure classification by support vector machine (SVM) using a combination of morphological parameters and textural features for low carbon steels. Comput. Mater. Sci. 160 (2019), 186–196, 10.1016/j.commatsci.2019.01.006.
Gupta, S., Sarkar, J., Kundu, M., Bandyopadhyay, N., Ganguly, S., Automatic recognition of SEM microstructure and phases of steel using LBP and random decision forest operator. Measurement, 151, 2020, 107224, 10.1016/j.measurement.2019.107224.
Fırat, H., Asker, M.E., Hanbay, D., Classification of hyperspectral remote sensing images using different dimension reduction methods with 3D/2D CNN. Remote Sens. Appl.: Soc. Environ., 25, 2022, 100694, 10.1016/j.rsase.2022.100694.
Abueidda, D.W., Koric, S., Sobh, N.A., Sehitoglu, H., Deep learning for plasticity and thermo-viscoplasticity. Int. J. Plast., 136, 2021, 102852, 10.1016/j.ijplas.2020.102852.
Wu, L., Nguyen, V.D., Kilingar, N.G., Noels, L., A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths. Comput. Methods Appl. Mech. Engrg., 369, 2020, 113234, 10.1016/j.cma.2020.113234.
Rehmer, A., Kroll, A., On the vanishing and exploding gradient problem in gated recurrent units. IFAC-PapersOnLine 53:2 (2020), 1243–1248, 10.1016/j.ifacol.2020.12.1342 21st IFAC World Congress.
Meka, R., Alaeddini, A., Bhaganagar, K., A robust deep learning framework for short-term wind power forecast of a full-scale wind farm using atmospheric variables. Energy, 221, 2021, 119759, 10.1016/j.energy.2021.119759.
Shi, T., Huang, S., Chen, L., Heng, Y., Kuang, Z., Xu, L., Mei, H., A molecular generative model of ADAM10 inhibitors by using GRU-based deep neural network and transfer learning. Chemometr. Intell. Lab. Syst., 205, 2020, 104122, 10.1016/j.chemolab.2020.104122.
Huang, L., Hong, X., Yang, Z., Liu, Y., Zhang, B., CNN-LSTM network-based damage detection approach for copper pipeline using laser ultrasonic scanning. Ultrasonics, 121, 2022, 106685, 10.1016/j.ultras.2022.106685.
de Oca Zapiain, M., D., J., Dingreville, R., Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods. Npj Comput. Mater., 7,3, 2021, 10.1038/s41524-020-00471-8.
Xu, C., Gao, S., Li, M., A novel PCA-based microstructure descriptor for heterogeneous material design. Comput. Mater. Sci. 130 (2017), 39–49, 10.1016/j.commatsci.2016.12.031.
Latypov, M.I., Kühbach, M., Beyerlein, I.J., Stinville, J.-C., Toth, L.S., Pollock, T.M., Kalidindi, S.R., Application of chord length distributions and principal component analysis for quantification and representation of diverse polycrystalline microstructures. Mater. Charact. 145 (2018), 671–685, 10.1016/j.matchar.2018.09.020.
Hu, C., Martin, S., Dingreville, R., Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space. Comput. Methods Appl. Mech. Engrg., 397, 2022, 115128, 10.1016/j.cma.2022.115128.
Ko, J.U., Na, K., Oh, J.-S., Kim, J., Youn, B.D., A new auto-encoder-based dynamic threshold to reduce false alarm rate for anomaly detection of steam turbines. Expert Syst. Appl., 189, 2022, 116094, 10.1016/j.eswa.2021.116094.
Chevrot, A., Vernotte, A., Legeard, B., CAE: Contextual auto-encoder for multivariate time-series anomaly detection in air transportation. Comput. Secur., 116, 2022, 102652, 10.1016/j.cose.2022.102652.
Alahmadi, A., Alkhraan, N., BinSaeedan, W., Mpsautodetect: A malicious powershell script detection model based on stacked denoising auto-encoder. Comput. Secur., 116, 2022, 102658, 10.1016/j.cose.2022.102658.
Fetni, S., Delahaye, J., Duchêne, L., Mertens, A., Habraken, A.M., Adaptive time stepping approach for phase-field modeling of phase separation and precipitates coarsening in additive manufacturing alloys. XVI International Conference on Computational Plasticity: FEM and Particle-Based Methods/Discrete Element Methods, Proceedings of COMPLAS 2021, 2021, 10.23967/complas.2021.009.
Kerr, T., Duncan, K., Myers, L., Post fire materials identification by micro-Raman spectroscopy and principal components analysis. J. Anal. Appl. Pyrolysis 102 (2013), 103–113, 10.1016/j.jaap.2013.03.008.
Sun, L., Wang, K., Xu, L., Zhang, C., Balezentis, T., A time-varying distance based interval-valued functional principal component analysis method – A case study of consumer price index. Inform. Sci. 589 (2022), 94–116, 10.1016/j.ins.2021.12.113.
ArunKumar, K., Kalaga, D.V., Mohan Sai Kumar, C., Kawaji, M., Brenza, T.M., Comparative analysis of gated recurrent units (GRU), long short-term memory (LSTM) cells, autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA) for forecasting COVID-19 trends. Alex. Eng. J. 61:10 (2022), 7585–7603, 10.1016/j.aej.2022.01.011.
Gao, S., Huang, Y., Zhang, S., Han, J., Wang, G., Zhang, M., Lin, Q., Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation. J. Hydrol., 589, 2020, 125188, 10.1016/j.jhydrol.2020.125188.
Wu, J.-Y., Learning analytics on structured and unstructured heterogeneous data sources: Perspectives from procrastination, help-seeking, and machine-learning defined cognitive engagement. Comput. Educ., 163, 2021, 104066, 10.1016/j.compedu.2020.104066.
Pospelov, N., Tetereva, A., Martynova, O., Anokhin, K., The Laplacian eigenmaps dimensionality reduction of fMRI data for discovering stimulus-induced changes in the resting-state brain activity. Neuroimage: Rep., 1(3), 2021, 100035, 10.1016/j.ynirp.2021.100035.
Oune, N., Bostanabad, R., Latent map Gaussian processes for mixed variable metamodeling. Comput. Methods Appl. Mech. Engrg., 387, 2021, 114128, 10.1016/j.cma.2021.114128.
Bencheikh, F., Harkat, M., Kouadri, A., Bensmail, A., New reduced kernel PCA for fault detection and diagnosis in cement rotary kiln. Chemometr. Intell. Lab. Syst., 204, 2020, 104091, 10.1016/j.chemolab.2020.104091.
Wei, Y., Wu, C., Li, G., Shi, H., Sequential transformer via an outside-in attention for image captioning. Eng. Appl. Artif. Intell., 108, 2022, 104574, 10.1016/j.engappai.2021.104574.