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Book ; Online: Automated Segmentation of Large Image Datasets using Artificial Intelligence for Microstructure Characterisation, Damage Analysis and High-Throughput Modelling Input

Medghalchi, Setareh / Kortmann, Joscha / Lee, Sang-Hyeok / Karimi, Ehsan / Kerzel, Ulrich / Korte-Kerzel, Sandra

2024

Abstract: Many properties of commonly used materials are driven by their microstructure, which can be influenced by the composition and manufacturing processes. To optimise future materials, understanding the microstructure is critically important. Here, we ... ...

Abstract	Many properties of commonly used materials are driven by their microstructure, which can be influenced by the composition and manufacturing processes. To optimise future materials, understanding the microstructure is critically important. Here, we present two novel approaches based on artificial intelligence that allow the segmentation of the phases of a microstructure for which simple numerical approaches, such as thresholding, are not applicable: One is based on the nnU-Net neural network, and the other on generative adversarial networks (GAN). Using large panoramic scanning electron microscopy images of dual-phase steels as a case study, we demonstrate how both methods effectively segment intricate microstructural details, including martensite, ferrite, and damage sites, for subsequent analysis. Either method shows substantial generalizability across a range of image sizes and conditions, including heat-treated microstructures with different phase configurations. The nnU-Net excels in mapping large image areas. Conversely, the GAN-based method performs reliably on smaller images, providing greater step-by-step control and flexibility over the segmentation process. This study highlights the benefits of segmented microstructural data for various purposes, such as calculating phase fractions, modelling material behaviour through finite element simulation, and conducting geometrical analyses of damage sites and the local properties of their surrounding microstructure. Comment: 38 pages, 24 figures
Keywords	Condensed Matter - Materials Science
Subject code	669
Publishing date	2024-01-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Three-Dimensional Damage Characterisation in Dual Phase Steel using Deep Learning

Medghalchi, Setareh / Karimi, Ehsan / Lee, Sang-Hyeok / Berkels, Benjamin / Kerzel, Ulrich / Korte-Kerzel, Sandra

2023

Abstract: High performance sheet metals with a multi-phase microstructure suffer from deformation induced damage formation during forming in the constituent phases but importantly also where these intersect. To capture damage in terms of the physical processes in ... ...

Abstract	High performance sheet metals with a multi-phase microstructure suffer from deformation induced damage formation during forming in the constituent phases but importantly also where these intersect. To capture damage in terms of the physical processes in three dimensions (3D) and its stochastic nature during deformation, two challenges remain to be tackled: First, bridging high resolution analysis towards large scales to consider statistical data and, second, characterising in 3D with a resolution appropriate for sub-micron sized voids at a large scale. Here, we present how this can be achieved using panoramic scanning electron microscopy (SEM), metallographic serial sectioning, and deep-learning assisted automatic image analysis. This brings together the 3D evolution of active damage mechanisms with volumetric and environmental information for thousands of individual damage sites. We also assess potential surface preparation artefacts in 2D analyses. Overall, we find that for the material considered here, a dual phase (DP800) steel, martensite cracking is the dominant but not sole origin of deformation induced damage and that for a quantitative comparison of damage density, metallographic preparation can induce additional surface damage density far exceeding what is commonly induced between uniaxial straining steps.
Keywords	Condensed Matter - Materials Science
Subject code	669
Publishing date	2023-03-10
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Cyclic Boosting -- an explainable supervised machine learning algorithm

Wick, Felix / Kerzel, Ulrich / Feindt, Michael

2020

Abstract: Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning algorithms typically results in black box models, where ...

Abstract	Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning algorithms typically results in black box models, where the path leading to individual predictions cannot be followed in detail. In order to address this issue, we propose the novel "Cyclic Boosting" machine learning algorithm, which allows to efficiently perform accurate regression and classification tasks while at the same time allowing a detailed understanding of how each individual prediction was made. Comment: added a discussion about causality
Keywords	Computer Science - Machine Learning ; Statistics - Machine Learning
Publishing date	2020-02-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Demand Forecasting of Individual Probability Density Functions with Machine Learning

Wick, F. / Kerzel, U. / Hahn, M. / Wolf, M. / Singhal, T. / Stemmer, D. / Ernst, J. / Feindt, M.

2020

Abstract: Demand forecasting is a central component of the replenishment process for retailers, as it provides crucial input for subsequent decision making like ordering processes. In contrast to point estimates, such as the conditional mean of the underlying ... ...

Abstract	Demand forecasting is a central component of the replenishment process for retailers, as it provides crucial input for subsequent decision making like ordering processes. In contrast to point estimates, such as the conditional mean of the underlying probability distribution, or confidence intervals, forecasting complete probability density functions allows to investigate the impact on operational metrics, which are important to define the business strategy, over the full range of the expected demand. Whereas metrics evaluating point estimates are widely used, methods for assessing the accuracy of predicted distributions are rare, and this work proposes new techniques for both qualitative and quantitative evaluation methods. Using the supervised machine learning method "Cyclic Boosting", complete individual probability density functions can be predicted such that each prediction is fully explainable. This is of particular importance for practitioners, as it allows to avoid "black-box" models and understand the contributing factors for each individual prediction. Another crucial aspect in terms of both explainability and generalizability of demand forecasting methods is the limitation of the influence of temporal confounding, which is prevalent in most state of the art approaches. Comment: included new section on temporal confounding, new introduction, improved notation, corrected typos
Keywords	Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	310
Publishing date	2020-09-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning.

Kusche, Carl / Reclik, Tom / Freund, Martina / Al-Samman, Talal / Kerzel, Ulrich / Korte-Kerzel, Sandra

PloS one

2019 Volume 14, Issue 5, Page(s) e0216493

Abstract: High performance materials, from natural bone over ancient damascene steel to modern superalloys, typically possess a complex structure at the microscale. Their properties exceed those of the individual components and their knowledge-based improvement ... ...

Abstract	High performance materials, from natural bone over ancient damascene steel to modern superalloys, typically possess a complex structure at the microscale. Their properties exceed those of the individual components and their knowledge-based improvement therefore requires understanding beyond that of the components' individual behaviour. Electron microscopy has been instrumental in unravelling the most important mechanisms of co-deformation and in-situ deformation experiments have emerged as a popular and accessible technique. However, a challenge remains: to achieve high spatial resolution and statistical relevance in combination. Here, we overcome this limitation by using panoramic imaging and machine learning to study damage in a dual-phase steel. This high-throughput approach now gives us strain and microstructure dependent insights into the prevalent damage mechanisms and allows the separation of inclusions and deformation-induced damage across a large area of this heterogeneous material. Aiming for the first time at automated classification of the majority of damage sites rather than only the most distinct, the new method also encourages us to expand current research past interpretation of exemplary cases of distinct damage sites towards the less clear-cut reality.
MeSH term(s)	Deep Learning ; Materials Testing ; Steel
Chemical Substances	Steel (12597-69-2)
Language	English
Publishing date	2019-05-08
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0216493
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Thesis: CDF Grid-Computing und das X(3872) J/ Psi Pi + Pi - mit J/ Psi e + e -

Kerzel, Ulrich

2005

Title variant	CDF Grid computing and the decay X(3872) J/ Psi Pi + Pi - mit J/ Psi e + e - ; J, Psi Pi + Pi - J, Psi
Author's details	von Ulrich Kerzel
Language	English
Size	VI, 173, II S, graph. Darst
Document type	Book ; Thesis
Thesis / German Habilitation thesis	Univ., Diss.--Karlsruhe, 2005
Note	Text engl. ; Zusfassung in dt. Sprache
Database	Former special subject collection: coastal and deep sea fishing

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Book ; Thesis: CDF Grid-Computing und das X(3872) J/ Psi Pi + Pi - mit J/ Psi e + e -

Kerzel, Ulrich

2005

Title variant	CDF Grid computing and the decay X(3872) J/ Psi Pi + Pi - mit J/ Psi e + e - ; J, Psi Pi + Pi - J, Psi
Author's details	von Ulrich Kerzel
Language	English
Size	VI, 173, II S, graph. Darst
Document type	Book ; Thesis
Thesis / German Habilitation thesis	Univ., Diss.--Karlsruhe, 2005
Note	Text engl. ; Zusfassung in dt. Sprache
Database	Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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Article: Performance of the LHCb RICH detector at the LHC.

Adinolfi, M / Aglieri Rinella, G / Albrecht, E / Bellunato, T / Benson, S / Blake, T / Blanks, C / Brisbane, S / Brook, N H / Calvi, M / Cameron, B / Cardinale, R / Carson, L / Contu, A / Coombes, M / D'Ambrosio, C / Easo, S / Egede, U / Eisenhardt, S /

Fanchini, E / Fitzpatrick, C / Fontanelli, F / Forty, R / Frei, C / Gandini, P / Gao, R / Garra Tico, J / Giachero, A / Gibson, V / Gotti, C / Gregson, S / Gys, T / Haines, S C / Hampson, T / Harnew, N / Hill, D / Hunt, P / John, M / Jones, C R / Johnson, D / Kanaya, N / Katvars, S / Kerzel, U / Kim, Y M / Koblitz, S / Kucharczyk, M / Lambert, D / Main, A / Maino, M / Malde, S / Mangiafave, N / Matteuzzi, C / Mini', G / Mollen, A / Morant, J / Mountain, R / Morris, J V / Muheim, F / Muresan, R / Nardulli, J / Owen, P / Papanestis, A / Patel, M / Patrick, G N / Perego, D L / Pessina, G / Petrolini, A / Piedigrossi, D / Plackett, R / Playfer, S / Powell, A / Rademacker, J H / Ricciardi, S / Rogers, G J / Sail, P / Sannino, M / Savidge, T / Sepp, I / Sigurdsson, S / Soler, F J P / Solomin, A / Soomro, F / Sparkes, A / Spradlin, P / Storaci, B / Thomas, C / Topp-Joergensen, S / Torr, N / Ullaland, O / Vervink, K / Voong, D / Websdale, D / Wilkinson, G / Wotton, S A / Wyllie, K / Xing, F / Young, R

The European physical journal. C, Particles and fields

2013 Volume 73, Issue 5, Page(s) 2431

Abstract: The LHCb experiment has been taking data at the Large Hadron Collider (LHC) at CERN since the end of 2009. One of its key detector components is the Ring-Imaging Cherenkov (RICH) system. This provides charged particle identification over a wide momentum ... ...

Abstract	The LHCb experiment has been taking data at the Large Hadron Collider (LHC) at CERN since the end of 2009. One of its key detector components is the Ring-Imaging Cherenkov (RICH) system. This provides charged particle identification over a wide momentum range, from 2-100 GeV/
Language	English
Publishing date	2013-05-15
Publishing country	France
Document type	Journal Article
ZDB-ID	1459069-4
ISSN	1434-6052 ; 1434-6044
ISSN (online)	1434-6052
ISSN	1434-6044
DOI	10.1140/epjc/s10052-013-2431-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Measurement of

Aaij, R / Abellan Beteta, C / Adeva, B / Adinolfi, M / Adrover, C / Affolder, A / Ajaltouni, Z / Albrecht, J / Alessio, F / Alexander, M / Alkhazov, G / Alvarez Cartelle, P / Alves, A A / Amato, S / Amhis, Y / Anderson, J / Appleby, R B / Aquines Gutierrez, O / Archilli, F /

Arrabito, L / Artamonov, A / Artuso, M / Aslanides, E / Auriemma, G / Bachmann, S / Back, J J / Bailey, D S / Balagura, V / Baldini, W / Barlow, R J / Barschel, C / Barsuk, S / Barter, W / Bates, A / Bauer, C / Bauer, Th / Bay, A / Bediaga, I / Belogurov, S / Belous, K / Belyaev, I / Ben-Haim, E / Benayoun, M / Bencivenni, G / Benson, S / Benton, J / Bernet, R / Bettler, M-O / van Beuzekom, M / Bien, A / Bifani, S / Bird, T / Bizzeti, A / Bjørnstad, P M / Blake, T / Blanc, F / Blanks, C / Blouw, J / Blusk, S / Bobrov, A / Bocci, V / Bondar, A / Bondar, N / Bonivento, W / Borghi, S / Borgia, A / Bowcock, T J V / Bozzi, C / Brambach, T / van den Brand, J / Bressieux, J / Brett, D / Britsch, M / Britton, T / Brook, N H / Brown, H / de Bruyn, K / Büchler-Germann, A / Burducea, I / Bursche, A / Buytaert, J / Cadeddu, S / Callot, O / Calvi, M / Calvo Gomez, M / Camboni, A / Campana, P / Carbone, A / Carboni, G / Cardinale, R / Cardini, A / Carson, L / Carvalho Akiba, K / Casse, G / Cattaneo, M / Cauet, Ch / Charles, M / Charpentier, Ph / Chiapolini, N / Ciba, K / Cid Vidal, X / Ciezarek, G / Clarke, P E L / Clemencic, M / Cliff, H V / Closier, J / Coca, C / Coco, V / Cogan, J / Collins, P / Comerma-Montells, A / Constantin, F / Contu, A / Cook, A / Coombes, M / Corti, G / Couturier, B / Cowan, G A / Currie, R / D'Ambrosio, C / David, P / David, P N Y / De Bonis, I / De Capua, S / De Cian, M / De Lorenzi, F / De Miranda, J M / De Paula, L / De Simone, P / Decamp, D / Deckenhoff, M / Degaudenzi, H / Del Buono, L / Deplano, C / Derkach, D / Deschamps, O / Dettori, F / Dickens, J / Dijkstra, H / Diniz Batista, P / Domingo Bonal, F / Donleavy, S / Dordei, F / Dosil Suárez, A / Dossett, D / Dovbnya, A / Dupertuis, F / Dzhelyadin, R / Dziurda, A / Easo, S / Egede, U / Egorychev, V / Eidelman, S / van Eijk, D / Eisele, F / Eisenhardt, S / Ekelhof, R / Eklund, L / Elsasser, Ch / Elsby, D / Esperante Pereira, D / Falabella, A / Fanchini, E / Färber, C / Fardell, G / Farinelli, C / Farry, S / Fave, V / Fernandez Albor, V / Ferro-Luzzi, M / Filippov, S / Fitzpatrick, C / Fontana, M / Fontanelli, F / Forty, R / Francisco, O / Frank, M / Frei, C / Frosini, M / Furcas, S / Gallas Torreira, A / Galli, D / Gandelman, M / Gandini, P / Gao, Y / Garnier, J-C / Garofoli, J / Garra Tico, J / Garrido, L / Gascon, D / Gaspar, C / Gauld, R / Gauvin, N / Gersabeck, M / Gershon, T / Ghez, Ph / Gibson, V / Gligorov, V V / Göbel, C / Golubkov, D / Golutvin, A / Gomes, A / Gordon, H / Grabalosa Gándara, M / Graciani Diaz, R / Granado Cardoso, L A / Graugés, E / Graziani, G / Grecu, A / Greening, E / Gregson, S / Gui, B / Gushchin, E / Guz, Yu / Gys, T / Hadjivasiliou, C / Haefeli, G / Haen, C / Haines, S C / Hampson, T / Hansmann-Menzemer, S / Harji, R / Harnew, N / Harrison, J / Harrison, P F / Hartmann, T / He, J / Heijne, V / Hennessy, K / Henrard, P / Hernando Morata, J A / van Herwijnen, E / Hicks, E / Holubyev, K / Hopchev, P / Hulsbergen, W / Hunt, P / Huse, T / Huston, R S / Hutchcroft, D / Hynds, D / Iakovenko, V / Ilten, P / Imong, J / Jacobsson, R / Jaeger, A / Jahjah Hussein, M / Jans, E / Jansen, F / Jaton, P / Jean-Marie, B / Jing, F / John, M / Johnson, D / Jones, C R / Jost, B / Kaballo, M / Kandybei, S / Karacson, M / Karbach, T M / Keaveney, J / Kenyon, I R / Kerzel, U / Ketel, T / Keune, A / Khanji, B / Kim, Y M / Knecht, M / Koopman, R F / Koppenburg, P / Korolev, M / Kozlinskiy, A / Kravchuk, L / Kreplin, K / Kreps, M / Krocker, G / Krokovny, P / Kruse, F / Kruzelecki, K / Kucharczyk, M / Kvaratskheliya, T / La Thi, V N / Lacarrere, D / Lafferty, G / Lai, A / Lambert, D / Lambert, R W / Lanciotti, E / Lanfranchi, G / Langenbruch, C / Latham, T / Lazzeroni, C / Le Gac, R / van Leerdam, J / Lees, J-P / Lefèvre, R / Leflat, A / Lefrançois, J / Leroy, O / Lesiak, T / Li, L / Li Gioi, L / Lieng, M / Liles, M / Lindner, R / Linn, C / Liu, B / Liu, G / von Loeben, J / Lopes, J H / Lopez Asamar, E / Lopez-March, N / Lu, H / Luisier, J / Mac Raighne, A / Machefert, F / Machikhiliyan, I V / Maciuc, F / Maev, O / Magnin, J / Malde, S / Mamunur, R M D / Manca, G / Mancinelli, G / Mangiafave, N / Marconi, U / Märki, R / Marks, J / Martellotti, G / Martens, A / Martin, L / Martín Sánchez, A / Martinez Santos, D / Massafferri, A / Mathe, Z / Matteuzzi, C / Matveev, M / Maurice, E / Maynard, B / Mazurov, A / McGregor, G / McNulty, R / Meissner, M / Merk, M / Merkel, J / Messi, R / Miglioranzi, S / Milanes, D A / Minard, M-N / Molina Rodriguez, J / Monteil, S / Moran, D / Morawski, P / Mountain, R / Mous, I / Muheim, F / Müller, K / Muresan, R / Muryn, B / Muster, B / Musy, M / Mylroie-Smith, J / Naik, P / Nakada, T / Nandakumar, R / Nasteva, I / Nedos, M / Needham, M / Neufeld, N / Nguyen, A D / Nguyen-Mau, C / Nicol, M / Niess, V / Nikitin, N / Nomerotski, A / Novoselov, A / Oblakowska-Mucha, A / Obraztsov, V / Oggero, S / Ogilvy, S / Okhrimenko, O / Oldeman, R / Orlandea, M / Otalora Goicochea, J M / Owen, P / Pal, K / Palacios, J / Palano, A / Palutan, M / Panman, J / Papanestis, A / Pappagallo, M / Parkes, C / Parkinson, C J / Passaleva, G / Patel, G D / Patel, M / Paterson, S K / Patrick, G N / Patrignani, C / Pavel-Nicorescu, C / Pazos Alvarez, A / Pellegrino, A / Penso, G / Pepe Altarelli, M / Perazzini, S / Perego, D L / Perez Trigo, E / Pérez-Calero Yzquierdo, A / Perret, P / Perrin-Terrin, M / Pessina, G / Petrella, A / Petrolini, A / Phan, A / Picatoste Olloqui, E / Pie Valls, B / Pietrzyk, B / Pilař, T / Pinci, D / Plackett, R / Playfer, S / Plo Casasus, M / Polok, G / Poluektov, A / Polycarpo, E / Popov, D / Popovici, B / Potterat, C / Powell, A / Prisciandaro, J / Pugatch, V / Puig Navarro, A / Qian, W / Rademacker, J H / Rakotomiaramanana, B / Rangel, M S / Raniuk, I / Raven, G / Redford, S / Reid, M M / Dos Reis, A C / Ricciardi, S / Richards, A / Rinnert, K / Roa Romero, D A / Robbe, P / Rodrigues, E / Rodrigues, F / Rodriguez Perez, P / Rogers, G J / Roiser, S / Romanovsky, V / Rosello, M / Rouvinet, J / Ruf, T / Ruiz, H / Sabatino, G / Saborido Silva, J J / Sagidova, N / Sail, P / Saitta, B / Salzmann, C / Sannino, M / Santacesaria, R / Santamarina Rios, C / Santinelli, R / Santovetti, E / Sapunov, M / Sarti, A / Satriano, C / Satta, A / Savrie, M / Savrina, D / Schaack, P / Schiller, M / Schleich, S / Schlupp, M / Schmelling, M / Schmidt, B / Schneider, O / Schopper, A / Schune, M-H / Schwemmer, R / Sciascia, B / Sciubba, A / Seco, M / Semennikov, A / Senderowska, K / Sepp, I / Serra, N / Serrano, J / Seyfert, P / Shapkin, M / Shapoval, I / Shatalov, P / Shcheglov, Y / Shears, T / Shekhtman, L / Shevchenko, O / Shevchenko, V / Shires, A / Silva Coutinho, R / Skwarnicki, T / Smith, N A / Smith, E / Sobczak, K / Soler, F J P / Solomin, A / Soomro, F / Souza De Paula, B / Spaan, B / Sparkes, A / Spradlin, P / Stagni, F / Stahl, S / Steinkamp, O / Stoica, S / Stone, S / Storaci, B / Straticiuc, M / Straumann, U / Subbiah, V K / Swientek, S / Szczekowski, M / Szczypka, P / Szumlak, T / T'Jampens, S / Teodorescu, E / Teubert, F / Thomas, C / Thomas, E / van Tilburg, J / Tisserand, V / Tobin, M / Topp-Joergensen, S / Torr, N / Tournefier, E / Tourneur, S / Tran, M T / Tsaregorodtsev, A / Tuning, N / Ubeda Garcia, M / Ukleja, A / Urquijo, P / Uwer, U / Vagnoni, V / Valenti, G / Vazquez Gomez, R / Vazquez Regueiro, P / Vecchi, S / Velthuis, J J / Veltri, M / Viaud, B / Videau, I / Vieira, D / Vilasis-Cardona, X / Visniakov, J / Vollhardt, A / Volyanskyy, D / Voong, D / Vorobyev, A / Voss, H / Wandernoth, S / Wang, J / Ward, D R / Watson, N K / Webber, A D / Websdale, D / Whitehead, M / Wiedner, D / Wiggers, L / Wilkinson, G / Williams, M P / Williams, M / Wilson, F F / Wishahi, J / Witek, M / Witzeling, W / Wotton, S A / Wyllie, K / Xie, Y / Xing, F / Xing, Z / Yang, Z / Young, R / Yushchenko, O / Zangoli, M / Zavertyaev, M / Zhang, F / Zhang, L / Zhang, W C / Zhang, Y / Zhelezov, A / Zhong, L / Zvyagin, A

The European physical journal. C, Particles and fields

2012 Volume 72, Issue 8, Page(s) 2100

Abstract: The differential cross-section for the inclusive production ... ...

Abstract	The differential cross-section for the inclusive production of
Language	English
Publishing date	2012-08-10
Publishing country	France
Document type	Journal Article
ZDB-ID	1459069-4
ISSN	1434-6052 ; 1434-6044
ISSN (online)	1434-6052
ISSN	1434-6044
DOI	10.1140/epjc/s10052-012-2100-4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Measurement of

The European physical journal. C, Particles and fields

2012 Volume 72, Issue 6, Page(s) 2025

Abstract: The production ... ...

Abstract	The production of
Language	English
Publishing date	2012-06-07
Publishing country	France
Document type	Journal Article
ZDB-ID	1459069-4
ISSN	1434-6052 ; 1434-6044
ISSN (online)	1434-6052
ISSN	1434-6044
DOI	10.1140/epjc/s10052-012-2025-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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