Equation-based and data-driven modeling strategies for industrial coating processes

CFD modeling; Chemical Vapor Deposition; Coating; Industrial application; Production data; Supervised learning; Chemical vapour deposition; Computational fluid dynamics modeling; Data-driven model; Dynamic learning; Equation based; Fluid machines; Industrial coating process; Machine-learning; Modelling strategies; Computer Science (all); Engineering (all); General Engineering; General Computer Science

Résumé :

[en] Computational Fluid Dynamics (CFD) and Machine Learning (ML) approaches are implemented and compared in an industrial Chemical Vapor Deposition process for the production of cutting tools. In this work, the aim is to analyze the pros and cons of each method and propose a blend of the two approaches that is suitable in industrial applications, where the process is too complicated to address with first-principles models and the data do not allow the implementation of data-hungry methods. Both approaches accurately predict the coating thickness (Mean Absolute Percentage Error (MAPE) of 6.0% and 4.4% for CFD and ML respectively for the test case reactor). CFD, despite its increased computational cost, both in terms of developing and also calibrating for the application at hand, provides meaningful insight and illuminates the process. On the other hand, ML can provide predictions in a time-efficient manner, and is thus appropriate for inline and concurrent predictions. However, it is limited by the available data and has low extrapolation ability. Equation-based and data-driven methods are combined by exploiting a handful of CFD results for efficient interpolation in a reduced space defined by the principal components of the dataset, by implementing Gappy POD. This allows for the accurate reconstruction of the full state-space with limited data.

Disciplines :

Ingénierie chimique
Sciences informatiques

Auteur, co-auteur :

PAPAVASILEIOU, Paris ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) ; School of Chemical Engineering, National Technical University of Athens, Greece

KORONAKI, Eleni ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) ; School of Chemical Engineering, National Technical University of Athens, Greece

POZZETTI, Gabriele; CERATIZIT Luxembourg S.à r.l, Mamer, Luxembourg

Kathrein, Martin; CERATIZIT Luxembourg S.à r.l., Mamer, Luxembourg

Czettl, Christoph; CERATIZIT Austria GmbH, Reutte, Austria

Boudouvis, Andreas G.; School of Chemical Engineering, National Technical University of Athens, Greece

BORDAS, Stéphane ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)

Co-auteurs externes :

yes

Langue du document :

Anglais

Titre :

Equation-based and data-driven modeling strategies for industrial coating processes

Date de publication/diffusion :

août 2023

Titre du périodique :

Computers in Industry

ISSN :

0166-3615

eISSN :

1872-6194

Maison d'édition :

Elsevier B.V.

Volume/Tome :

149

Pagination :

103938

Peer reviewed :

Peer reviewed vérifié par ORBi

Focus Area :

Computational Sciences
Physics and Materials Science

URL complémentaire :

https://api.elsevier.com/content/article/PII:S016636152300088X?httpAccept=text/xml

Intitulé du projet de recherche :

U-AGR-7130 - BRIDGES/21/16758846/OptiSimCVD (01/06/2022 - 31/05/2026) - BORDAS Stéphane

Organisme subsidiant :

H2020 Marie Skłodowska-Curie Actions
European Commission
Université du Luxembourg
Fonds National de la Recherche Luxembourg
Horizon 2020
Horizon 2020 Framework Programme

Subventionnement (détails) :

S.P.A.B acknowledges financial support by the Fonds National de la Recherche (FNR) Luxembourg (BRIDGE grant OptiSimCVD) . E.D.K. is supported by the EU under a MSCA Individual Fellowship (Grant agreement: 890676 ). S.P.A.B received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 811099 TWINNING Project DRIVEN for the University of Luxembourg.S.P.A.B acknowledges financial support by the Fonds National de la Recherche (FNR) Luxembourg (BRIDGE grant OptiSimCVD). E.D.K. is supported by the EU under a MSCA Individual Fellowship (Grant agreement: 890676). S.P.A.B received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 811099 TWINNING Project DRIVEN for the University of Luxembourg.

Disponible sur ORBilu :

depuis le 22 octobre 2023

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Bibliographie

Agarwal, P., Tamer, M., Sahraei, M.H., Budman, H., Deep learning for classification of profit-based operating regions in industrial processes. Ind. Eng. Chem. Res. 59:6 (2020), 2378–2395, 10.1021/acs.iecr.9b04737.
Alonso, A.A., Frouzakis, C.E., Kevrekidis, I.G., Optimal sensor placement for state reconstruction of distributed process systems. AIChE J. 50:7 (2004), 1438–1452, 10.1002/aic.10121.
Alonso, A.A., Kevrekidis, I.G., Banga, J.R., Frouzakis, C.E., Optimal sensor location and reduced order observer design for distributed process systems. Comput. Chem. Eng. 28:1 (2004), 27–35, 10.1016/S0098-1354(03)00175-3.
Aviziotis, I.G., Duguet, T., Vahlas, C., Boudouvis, A.G., Combined macro/nanoscale investigation of the chemical vapor deposition of Fe from Fe(CO)5. Adv. Mater. Interf, 4(18), 2017, 1601185, 10.1002/admi.201601185.
Azadi, P., Winz, J., Leo, E., Klock, R., Engell, S., A hybrid dynamic model for the prediction of molten iron and slag quality indices of a large-scale blast furnace. Comput. Chem. Eng., 156, 2022, 107573, 10.1016/j.compchemeng.2021.107573.
Bar-Hen, M., Etsion, I., Experimental study of the effect of coating thickness and substrate roughness on tool wear during turning. Tribol. Int. 110 (2017), 341–347, 10.1016/j.triboint.2016.11.011.
Blakseth, S.S., Rasheed, A., Kvamsdal, T., San, O., Deep neural network enabled corrective source term approach to hybrid analysis and modeling. Neural Netw. 146 (2022), 181–199, 10.1016/j.neunet.2021.11.021.
Boyes, H., Watson, T., Digital twins: An analysis framework and open issues. Comput. Ind., 143, 2022, 103763, 10.1016/j.compind.2022.103763.
Cai, H., Feng, J., Yang, Q., Li, W., Li, X., Lee, J., A virtual metrology method with prediction uncertainty based on Gaussian process for chemical mechanical planarization. Comput. Ind., 119, 2020, 103228, 10.1016/j.compind.2020.103228.
Carvajal Soto, J.A., Tavakolizadeh, F., Gyulai, D., An online machine learning framework for early detection of product failures in an industry 4.0 context. Int. J. Comput. Integr. Manuf. 32:4–5 (2019), 452–465, 10.1080/0951192x.2019.1571238.
Cheimarios, N., Koronaki, E.D., Boudouvis, A.G., Illuminating nonlinear dependence of film deposition rate in a CVD reactor on operating conditions. Chem. Eng. J. 181–182 (2012), 516–523, 10.1016/j.cej.2011.11.008.
Cho, J., Mountziaris, T.J., Onset of flow recirculation in vertical rotating-disc chemical vapor deposition reactors. AIChE J. 59:9 (2013), 3530–3538, 10.1002/aic.14179.
Creighton, J.R., Parmeter, J.E., Metal CVD for microelectronic applications: An examination of surface chemistry and kinetics. Crit. Rev. Solid State Mater. Sci 18:2 (1993), 175–237, 10.1080/10408439308242560.
Dai, W., Mohammadi, S., Cremaschi, S., A hybrid modeling framework using dimensional analysis for erosion predictions. Comput. Chem. Eng., 156, 2022, 107577, 10.1016/j.compchemeng.2021.107577.
Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J., Barbosa, J., Machine learning and reasoning for predictive maintenance in industry 4.0: Current status and challenges. Comput. Ind., 123, 2020, 103298, 10.1016/j.compind.2020.103298.
Deng, J., Sierla, S., Sun, J., Vyatkin, V., Reinforcement learning for industrial process control: A case study in flatness control in steel industry. Comput. Ind., 143, 2022, 103748, 10.1016/j.compind.2022.103748.
Deshpande, S., Lengiewicz, J., Bordas, S.P.A., Probabilistic deep learning for real-time large deformation simulations. Comput. Methods Appl. Mech. Engrg., 398, 2022, 115307, 10.1016/j.cma.2022.115307.
Du, H., Jiang, Y., Backup or reliability improvement strategy for a manufacturer facing heterogeneous consumers in a dynamic supply chain. IEEE Access 7 (2019), 50419–50430, 10.1109/access.2019.2911620.
Endo, H., Kuwana, K., Saito, K., Qian, D., Andrews, R., Grulke, E.A., CFD prediction of carbon nanotube production rate in a CVD reactor. Chem. Phys. Lett. 387:4 (2004), 307–311, 10.1016/j.cplett.2004.01.124.
Everson, R., Sirovich, L., Karhunen–Loève procedure for Gappy data. J. Opt. Soc. Amer. A, 12(8), 1995, 1657, 10.1364/JOSAA.12.001657.
Fotiadis, D.I., Boekholt, M., Jensen, K.F., Richter, W., Flow and heat transfer in CVD reactors: Comparison of Raman temperature measurements and finite element model predictions. J. Cryst. Growth 100:3 (1990), 577–599, 10.1016/0022-0248(90)90257-L.
Gakis, G., Koronaki, E., Boudouvis, A., Numerical investigation of multiple stationary and time-periodic flow regimes in vertical rotating disc CVD reactors. J. Cryst. Growth 432 (2015), 152–159, 10.1016/j.jcrysgro.2015.09.026.
Galvis, L., Offermans, T., Bertinetto, C.G., Carnoli, A., Karamujić, E., Li, W., Szymańska, E., Buydens, L.M.C., Jansen, J.J., Retrospective quality by design r(QbD) for lactose production using historical process data and design of experiments. Comput. Ind., 141, 2022, 103696, 10.1016/j.compind.2022.103696.
Gkinis, P., Aviziotis, I., Koronaki, E., Gakis, G., Boudouvis, A., The effects of flow multiplicity on GaN deposition in a rotating disk CVD reactor. J. Cryst. Growth 458 (2017), 140–148, 10.1016/j.jcrysgro.2016.10.065.
Gkinis, P., Koronaki, E., Skouteris, A., Aviziotis, I., Boudouvis, A., Building a data-driven reduced order model of a chemical vapor deposition process from low-fidelity CFD simulations. Chem. Eng. Sci. 199 (2019), 371–380, 10.1016/j.ces.2019.01.009.
Hastie, T., Tibshirani, R., Friedman, J., Additive models, trees and related methods. Hastie, T., Tibshirani, R., Friedman, J., (eds.) The Elements of Statistical Learning: Data Mining, Inference, and Prediction Springer Series in Statistics, 2009, Springer, New York, NY, 295–336, 10.1007/978-0-387-84858-7_9.
Hastie, T., Tibshirani, R., Friedman, J., Boosting and additive trees. Hastie, T., Tibshirani, R., Friedman, J., (eds.) The Elements of Statistical Learning: Data Mining, Inference, and Prediction Springer Series in Statistics, 2009, Springer, New York, NY, 337–387, 10.1007/978-0-387-84858-7_10.
He, Z., Tran, K.-P., Thomassey, S., Zeng, X., Xu, J., Yi, C., A deep reinforcement learning based multi-criteria decision support system for optimizing textile chemical process. Comput. Ind., 125, 2021, 103373, 10.1016/j.compind.2020.103373.
Hochauer, D., Mitterer, C., Penoy, M., Puchner, S., Michotte, C., Martinz, H., Hutter, H., Kathrein, M., Carbon doped α-Al2O3 coatings grown by chemical vapor deposition. Surf. Coat. Technol. 206:23 (2012), 4771–4777, 10.1016/j.surfcoat.2012.03.059.
Hürkamp, A., Gellrich, S., Ossowski, T., Beuscher, J., Thiede, S., Herrmann, C., Dröder, K., Combining simulation and machine learning as digital twin for the manufacturing of overmolded thermoplastic composites. JMMP, 4(3), 2020, 92, 10.3390/jmmp4030092.
Iqbal, R., Maniak, T., Doctor, F., Karyotis, C., Fault detection and isolation in industrial processes using deep learning approaches. IEEE Trans. Ind. Inform. 15:5 (2019), 3077–3084, 10.1109/tii.2019.2902274.
James, G., Witten, D., Hastie, T., Tibshirani, R., Statistical learning. An Introduction To Statistical Learning: With Applications in R, 2021, Springer US, New York, NY, 15–57, 10.1007/978-1-0716-1418-1_2.
James, G., Witten, D., Hastie, T., Tibshirani, R., Tree-based methods. An Introduction To Statistical Learning: With Applications in R, 2021, Springer US, New York, NY, 327–365, 10.1007/978-1-0716-1418-1_8.
Jo, T., Koo, B., Kim, H., Lee, D., Yoon, J.Y., Effective sensor placement in a steam reformer using gappy proper orthogonal decomposition. Appl. Therm. Eng. 154 (2019), 419–432, 10.1016/j.applthermaleng.2019.03.089.
Kagermann, H., Change through digitization–value creation in the age of industry 4.0. Albach, H., Meffert, H., Pinkwart, A., Reichwald, R., (eds.) Management of Permanent Change, 2015, Springer Fachmedien, Wiesbaden, 23–45, 10.1007/978-3-658-05014-6_2.
Kalaboukas, K., Kiritsis, D., Arampatzis, G., Governance framework for autonomous and cognitive digital twins in agile supply chains. Comput. Ind., 146, 2023, 103857, 10.1016/j.compind.2023.103857.
Kathrein, M., Schintlmeister, W., Wallgram, W., Schleinkofer, U., Doped CVD Al2O3 coatings for high performance cutting tools. Surf. Coat. Technol. 163–164 (2003), 181–188, 10.1016/s0257-8972(02)00483-8.
Kerfriden, P., Goury, O., Rabczuk, T., Bordas, S.P.A., A partitioned model order reduction approach to rationalise computational expenses in nonlinear fracture mechanics. Comput. Methods Appl. Mech. Engrg. 256 (2013), 169–188, 10.1016/j.cma.2012.12.004.
Kim, D., Kang, P., Cho, S., Lee, H.-j., Doh, S., Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing. Expert Syst. Appl. 39:4 (2012), 4075–4083, 10.1016/j.eswa.2011.09.088.
Kim, A., Oh, K., Jung, J.-Y., Kim, B., Imbalanced classification of manufacturing quality conditions using cost-sensitive decision tree ensembles. Int. J. Comput. Integr. Manuf. 31:8 (2018), 701–717, 10.1080/0951192x.2017.1407447.
Kleijn, C.R., Dorsman, R., Kuijlaars, K.J., Okkerse, M., van Santen, H., Multi-scale modeling of chemical vapor deposition processes for thin film technology. J. Cryst. Growth 303:1 (2007), 362–380, 10.1016/j.jcrysgro.2006.12.062.
Kleijn, C.R., Hoogendoorn, C.J., A study of 2- and 3-D transport phenomena in horizontal chemical vapor deposition reactors. Chem. Eng. Sci. 46:1 (1991), 321–334, 10.1016/0009-2509(91)80141-K.
Koronaki, E.D., Evangelou, N., Psarellis, Y.M., Boudouvis, A.G., Kevrekidis, I.G., From partial data to out-of-sample parameter and observation estimation with diffusion maps and geometric harmonics., 2023, arXiv, 10.48550/arXiv.2301.11728 arXiv:arXiv:2301.11728.
Koronaki, E.D., Gakis, G.P., Cheimarios, N., Boudouvis, A.G., Efficient tracing and stability analysis of multiple stationary and periodic states with exploitation of commercial CFD software. Chem. Eng. Sci. 150 (2016), 26–34, 10.1016/j.ces.2016.04.043.
Koronaki, E., Gkinis, P., Beex, L., Bordas, S., Theodoropoulos, C., Boudouvis, A., Classification of states and model order reduction of large scale chemical vapor deposition processes with solution multiplicity. Comput. Chem. Eng. 121 (2019), 148–157, 10.1016/j.compchemeng.2018.08.023.
Łępicka, M., Grądzka-Dahlke, M., The initial evaluation of performance of hard anti-wear coatings deposited on metallic substrates: Thickness, mechanical properties and adhesion measurements – a brief review. Rev. Adv. Mater. Sci 58:1 (2019), 50–65, 10.1515/rams-2019-0003.
Liu, S.-S., Xiao, W.-D., CFD–PBM coupled simulation of silicon CVD growth in a fluidized bed reactor: Effect of silane pyrolysis kinetic models. Chem. Eng. Sci. 127 (2015), 84–94, 10.1016/j.ces.2015.01.026.
Ma, Y., Zhu, W., Benton, M.G., Romagnoli, J., Continuous control of a polymerization system with deep reinforcement learning. J. Process Control 75 (2019), 40–47, 10.1016/j.jprocont.2018.11.004.
Malley, S., Reina, C., Nacy, S., Gilles, J., Koohbor, B., Youssef, G., Predictability of mechanical behavior of additively manufactured particulate composites using machine learning and data-driven approaches. Comput. Ind., 142, 2022, 103739, 10.1016/j.compind.2022.103739.
McKinney, W., 2010. Data Structures for Statistical Computing in Python. In: Python in Science Conference. Austin, Texas, pp. 56–61. http://dx.doi.org/10.25080/Majora-92bf1922-00a.
Ozaydin-Ince, G., Coclite, A.M., Gleason, K.K., CVD of polymeric thin films: Applications in sensors, Biotechnology, microelectronics/organic electronics, microfluidics, MEMS, composites and membranes. Rep. Progr. Phys., 75(1), 2011, 016501, 10.1088/0034-4885/75/1/016501.
Papavasileiou, P., Koronaki, E.D., Pozzetti, G., Kathrein, M., Czettl, C., Boudouvis, A.G., Mountziaris, T.J., Bordas, S.P.A., An efficient chemistry-enhanced CFD model for the investigation of the rate-limiting mechanisms in industrial chemical vapor deposition reactors. Chem. Eng. Res. Des. 186 (2022), 314–325, 10.1016/j.cherd.2022.08.005.
Perno, M., Hvam, L., Haug, A., Implementation of digital twins in the process industry: A systematic literature review of enablers and barriers. Comput. Ind., 134, 2022, 103558, 10.1016/j.compind.2021.103558.
Potdar, K., S., T., D., C., A comparative study of categorical variable encoding techniques for neural network classifiers. IJCA 175:4 (2017), 7–9, 10.5120/ijca2017915495.
Priore, P., Ponte, B., Puente, J., Gómez, A., Learning-based scheduling of flexible manufacturing systems using ensemble methods. Comput. Ind. Eng. 126 (2018), 282–291, 10.1016/j.cie.2018.09.034.
Psarellis, G.M., Aviziotis, I.G., Duguet, T., Vahlas, C., Koronaki, E.D., Boudouvis, A.G., Investigation of reaction mechanisms in the chemical vapor deposition of al from DMEAA. Chem. Eng. Sci. 177 (2018), 464–470, 10.1016/j.ces.2017.12.006.
Raissi, M., Perdikaris, P., Karniadakis, G., Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378 (2019), 686–707, 10.1016/j.jcp.2018.10.045.
Rasheed, A., San, O., Kvamsdal, T., Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access 8 (2020), 21980–22012, 10.1109/access.2020.2970143.
Saxena, A., Saad, A., Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Appl. Soft Comput. 7:1 (2007), 441–454, 10.1016/j.asoc.2005.10.001.
Schierling, M., Zimmermann, E., Neuschütz, D., Deposition kinetics of Al2O3 from AlCl3 -CO2-H2-HCl gas mixtures by thermal CVD in a hot-wall reactor. J. Phys. IV France 09:PR8 (1999), Pr8–85–Pr8–91, 10.1051/jp4:1999811.
Spencer, R., Gkinis, P., Koronaki, E., Gerogiorgis, D., Bordas, S., Boudouvis, A., Investigation of the chemical vapor deposition of Cu from copper amidinate through data driven efficient CFD modelling. Comput. Chem. Eng., 149, 2021, 107289, 10.1016/j.compchemeng.2021.107289.
Susto, G.A., Schirru, A., Pampuri, S., McLoone, S., Beghi, A., Machine learning for predictive maintenance: a multiple classifier approach. IEEE Trans. Ind. Inform. 11:3 (2015), 812–820, 10.1109/tii.2014.2349359.
Topka, K.C., Vergnes, H., Tsiros, T., Papavasileiou, P., Decosterd, L., Diallo, B., Senocq, F., Samelor, D., Pellerin, N., Menu, M.-J., Vahlas, C., Caussat, B., An innovative kinetic model allowing insight in the moderate temperature chemical vapor deposition of silicon oxynitride films from tris(dimethylsilyl)amine. Chem. Eng. J., 431, 2022, 133350, 10.1016/j.cej.2021.133350.
Tulsyan, A., Garvin, C., Ündey, C., Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems. Biotechnol. Bioeng. 115:8 (2018), 1915–1924, 10.1002/bit.26605.
Urcun, S., Rohan, P.-Y., Skalli, W., Nassoy, P., Bordas, S.P.A., Sciumè, G., Digital twinning of cellular capsule technology: Emerging outcomes from the perspective of porous media mechanics. PLOS ONE, 16(7), 2021, e0254512, 10.1371/journal.pone.0254512.
Wang, R., Cheung, C.F., Wang, C., Cheng, M.N., Deep learning characterization of surface defects in the selective laser melting process. Comput. Ind., 140, 2022, 103662, 10.1016/j.compind.2022.103662.
Willcox, K., Unsteady flow sensing and estimation via the gappy proper orthogonal decomposition. Comput. & Fluids 35:2 (2006), 208–226, 10.1016/j.compfluid.2004.11.006.
Wu, H., Yu, Z., Wang, Y., Experimental study of the process failure diagnosis in additive manufacturing based on acoustic emission. Measurement 136 (2019), 445–453, 10.1016/j.measurement.2018.12.067.