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         "type" : "Publication",
         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/28756689a5f23fa376ec8f97cd9cfcaba/bjose",         
         "tags" : [
            "bj_all","ivlr","ml"
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         "intraHash" : "8756689a5f23fa376ec8f97cd9cfcaba",
         "interHash" : "79d9e3534913bca42ccc1ab6e2626076",
         "label" : "Domain-randomised instance-segmentation benchmark for soot in PIV images",
         "user" : "bjose",
         "description" : "",
         "date" : "2025-12-19 13:48:16",
         "changeDate" : "2025-12-19 13:48:16",
         "count" : 2,
         "pub-type": "article",
         "journal": "Machine Learning: Science and Technology","publisher":"IOP Publishing",
         "year": "2025", 
         "url": "https://doi.org/10.1088/2632-2153/ae2565", 
         
         "author": [ 
            "Basil Jose","Klaus Peter Geigle","Fabian Hampp"
         ],
         "authors": [
         	
            	{"first" : "Basil",	"last" : "Jose"},
            	{"first" : "Klaus",	"last" : "Peter Geigle"},
            	{"first" : "Fabian",	"last" : "Hampp"}
         ],
         "volume": "6","number": "4","pages": "040504","abstract": "Access to high-level statistical information from scientific image-based diagnostics is facilitated by the capacity to segment quantity-of-interest (QoI) signal accurately from measurement noise and interferences. In particular under conditions with diverse nuisance signatures, deep learning (DL) pipelines can outperform algorithm-based computer vision (CV) strategies and simultaneously enhance domain-invariance. DL-pipelines aim to overcome the generalisation bottleneck through augmentations and learnable parameters, supplemented by huge amounts of data. Yet, manual pixel-accurate annotation for scientific tasks is prohibitively expensive and the robustness of the trained models is often impeded by the underlying narrow and domain-specific set of training data. In the current study, a previously developed domain-randomised pipeline for automatic annotation and synthetic training data generation is benchmarked using a physics-aware composite score. An ablation study for background variance, QoI placement strategy and QoI object source proportions is conducted. The benchmark, consisting of 72 synthetic training data generation recipes, highlights the optimal set of domain-randomisation parameters to strike the balance between domain-invariance and segmentation accuracy. Synthetic training datasets with a balanced mix of QoI objects, fused onto realistic background instances, are found to provide the most accurate models for segmentation. The best model is subsequently used for the inference on semantically challenging Mie scattering images containing particle-image velocimetry (PIV) tracer particles and soot filaments. The model\u2019s ability to detect soot structures with good accuracy is demonstrated and high-level soot filament area and contour statistics (e.g. curvature and fractal dimension) are used to delineate the effects of turbulent flow on soot filament structures. The present study highlights key parameters to tune domain randomisation strategies for DL-training pipelines and the pipeline usability and transferability is proved for autonomous semi-supervised learning. This eases access to high-level statistics in scientific image-based diagnostics.",
         "doi" : "10.1088/2632-2153/ae2565",
         
         "bibtexKey": "jose2025MLST"

      }
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         "type" : "Publication",
         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/29065da7c54e4fc7b24291d408a54b5bf/bjose",         
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            "bj_all","ivlr","ml"
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         "label" : "ML-based semantic segmentation for quantitative spray atomization description",
         "user" : "bjose",
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         "date" : "2025-12-19 13:44:58",
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         "pub-type": "article",
         "journal": "International Journal of Multiphase Flow",
         "year": "2025", 
         "url": "https://www.sciencedirect.com/science/article/pii/S0301932225000576", 
         
         "author": [ 
            "Basil Jose","Oliver Lammel","Fabian Hampp"
         ],
         "authors": [
         	
            	{"first" : "Basil",	"last" : "Jose"},
            	{"first" : "Oliver",	"last" : "Lammel"},
            	{"first" : "Fabian",	"last" : "Hampp"}
         ],
         "volume": "187","pages": "105179","abstract": "Fuel spray atomization in gas turbine systems significantly impacts the combustion process and thereby emission formation. Considering the necessity for quantitative description of the influence of operating conditions on the spray breakup mechanisms, a machine learning (ML) based methodology is introduced to accurately segment the dispersed liquid from the continuous gaseous phase in shadowgraphy images. The segmented images subsequently facilitate a high-level statistical analysis of gas-liquid-interface contours and ultimately instability dynamics. For this purpose, multiple ML models varying in architecture (Semantic FPN and DeepLabV3+), datasets and augmentations are benchmarked to achieve the best performance. Subsequently, the best model is validated and used to obtain conditional statistics on the detected spray contours of three different spray types (jet-in-crossflow, pressure swirl spray and prefilming airblast spray). The model showcases high robustness, transferability across spray configurations and accuracy along multiple never-seen sprays thereby illustrating the superiority of deep learning methods for scientific image segmentation tasks. Moreover, the inferred high-level statistical analysis provides novel quantitative insights into the involved turbulence-spray interactions aiding the understanding of jet, sheet and film atomization under highly turbulent flow conditions.",
         "issn" : "0301-9322",
         
         "doi" : "https://doi.org/10.1016/j.ijmultiphaseflow.2025.105179",
         
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         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/28464b1512c55e1fe3abb7b250d8e1c18/bjose",         
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         "label" : "Advanced statistics using semi-supervised AI models for shadowgraphy",
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         "date" : "2025-12-19 13:36:47",
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         "pub-type": "article",
         "journal": "Statusseminar SimTech",
         "year": "2025", 
         "url": "", 
         
         "author": [ 
            "Basil Jose","Fabian Hampp","Yeonse Kang"
         ],
         "authors": [
         	
            	{"first" : "Basil",	"last" : "Jose"},
            	{"first" : "Fabian",	"last" : "Hampp"},
            	{"first" : "Yeonse",	"last" : "Kang"}
         ],
         
         "bibtexKey": "jose2025ASSstatus"

      }
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      {
         "type" : "Publication",
         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/253ddd776f24a5989ad3d66d03ab350a7/ifu",         
         "tags" : [
            "dissertation","ML"
         ],
         
         "intraHash" : "53ddd776f24a5989ad3d66d03ab350a7",
         "interHash" : "11e37e4341f95a7e9ca4714c9e980f22",
         "label" : "Erweiterung der Leistungsfähigkeit tiefgezogener Blechbauteile mittels Prägens",
         "user" : "ifu",
         "description" : "",
         "date" : "2025-11-25 09:09:37",
         "changeDate" : "2025-12-03 07:44:32",
         "count" : 3,
         "pub-type": "phdthesis",
         
         "year": "2025", 
         "url": "", 
         
         "author": [ 
            "Stefan Walzer"
         ],
         "authors": [
         	
            	{"first" : "Stefan",	"last" : "Walzer"}
         ],
         
         "editor": [ 
            "Fraunhofer Verlag"
         ],
         "editors": [
         	
            	{"first" : "Fraunhofer",	"last" : "Verlag"}
         ],
         "abstract": "Vor dem Hintergrund der werkstofflichen Ressourceneffizienz sind die Hersteller moderner Blechkonstruktionen bestrebt, innovative Produktionsprozesse zu entwickeln, um ihre Produkte effizienter zu gestalten. Dies ermöglicht es den Herstellern, trotz des globalen wirtschaftlichen Drucks weiterhin konkurrenzfähig zu agieren. Diesen Gegebenheiten geschuldet, wurden in der Vergangenheit mehrere mechanische aber auch thermische Fertigungsverfahren entwickelt, die die Formgebungsgrenzen moderner Blechbauteile erweitern beziehungsweise das Einstellen von anforderungsgerechten mechanischen Eigenschaften \u2013 respektive Bauteileigenschaften \u2013 ermöglichen.\r\nBisherige Arbeiten zur Prozessgrenzenerweiterung von formgebenden Verfahren in der Blechumformung befassen sich überwiegend mit thermischen Fertigungsverfahren. Zu mechanischen Fertigungsverfahren mittels oberflächennahen Prägens liegen hingegen bisher nur unzureichend wissenschaftliche Untersuchungen vor. Gegenstand der vorliegenden Dissertation ist daher die numerische und experimentelle Erforschung der Wirkung von lokal geprägten Oberflächenstrukturen in Blechplatinen und die dadurch initiierte Erhöhung bzw. Reduktion der mechanischen Kennwerte des Platinenwerkstoffs im Hinblick auf dessen Formänderungsvermögen. Im Rahmen dieser Arbeit wird eine neuartige Herangehensweise des Prägens von Blechplatinen untersucht, die die Herstellung qualitativ hochwertiger Blechformteile mit verbesserten mechanischen Eigenschaften ermöglicht.\r\nInnerhalb experimenteller Versuchsreihen werden Blechplatinen aus einer höherfesten Blechgüte (Dualphasenstahl DP 600) oberflächennah mittels Prägen strukturiert und die Oberflächenintegrität der geprägten Strukturen durch Messung der Topographie, der Mikrohärte und Härte beschrieben. Die geprägten Platinen werden anschließend durch Zug- und Nakajima-Versuche hinsichtlich ihres Formänderungsvermögens analysiert, charakterisiert und mit dem Referenzwerkstoff verglichen.\r\nAusgewählte Prägestrukturen werden für den Übertrag auf repräsentative Blechbauteile herangezogen, um den Nachweis der Prozessgrenzenerweiterung hinsichtlich der Erhöhung der Grenzziehverhältnisse zu erbringen. Hierbei stehen insbesondere die lokalen Wechselwirkungen in jenen Bauteilbereichen im Fokus, die hinsichtlich des Bauteilversagens kritisch zu bewerten sind, wie z.B. der Stempelkantenradius einer Rundnapfgeometrie. Durch die festigkeitssteigernde Wirkung der geprägten Oberflächenstrukturen wird die Streck- bzw. Fließgrenze des Blechwerkstoffes lokal erhöht, wodurch das vorzeitige Ausdünnen der Blechdicke reduziert und somit das Werkstoffversagen durch Reißer verhindert wird. Abschließend werden die gewonnenen Erkenntnisse auf eine komplexere Bauteilgeometrie übertragen. Parallel hierzu werden geprägte Strukturbauteile durch quasi-statische Stauchversuche geprüft und anhand der ertragbaren Spitzenlasten und deren spezifische Energieaufnahmefähigkeit untersucht.\r\nDer wesentliche Erkenntnisgewinn dieser Arbeit besteht daher einerseits im Nachweis erfolgreicher Methoden zur Erweiterung von Prozessgrenzen beim Tiefziehen hinsichtlich der Erhöhung erreichbarer Ziehtiefen und andererseits im Nachweis der verbesserten Energieaufnahmefähigkeit tiefgezogener Blechbauteile.\r\nDie mit dieser Arbeit vorliegende neue Methodik zur lokalen Verfestigung von Blechbauteilen durch Eindrücken sphärischer Vertiefungen in die Blechoberfläche, insbesondere in Bereichen frühzeitiger Blechausdünnung sowie der verbesserten Kraftaufnahme während eines Deformationsvorgangs bieten langfristig neue Konstruktions- und Auslegungsmöglichkeiten für industriell gefertigte Blechbauteile. Eine Kombination aus konventionellen Blechplatinen und Tailored Embossed Blanks (TEB) kann dabei den Einsatzbereich festigkeitsgradierter Blechbauteile weitreichend ergänzen. Denkbar sind beispielweise Anwendungen im modernen Karosseriebau sowie im Baugewerbe für Befestigungselemente.\r\nAm Ende der Arbeit liegt neben der Quantifizierung der Prozessgrenzenerweiterung von lokal geprägten und tiefgezogenen Blechbauteilen ein umfangreiches Prozessverständnis zu dieser neuen Technologie vor. Zur Erweiterung der herstellungsspezifischen Prozessgrenzen werden darüber hinaus neuartige Anwendungsfelder aufgezeigt, die eine Verbesserung technischer und wirtschaftlicher Anforderungen im industriellen Umfeld ermöglichen.",
         "isbn" : "978-3-8396-2087-8",
         
         "language" : "deutsch",
         
         "bibtexKey": "walzer2025"

      }
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         "type" : "Publication",
         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/253ddd776f24a5989ad3d66d03ab350a7/roxyfoxy2211",         
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            "ML","dissertation"
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         "label" : "Erweiterung der Leistungsfähigkeit tiefgezogener Blechbauteile mittels Prägens",
         "user" : "roxyfoxy2211",
         "description" : "",
         "date" : "2025-11-25 09:05:13",
         "changeDate" : "2025-12-03 07:44:32",
         "count" : 3,
         "pub-type": "phdthesis",
         
         "year": "2025", 
         "url": "", 
         
         "author": [ 
            "Stefan Walzer"
         ],
         "authors": [
         	
            	{"first" : "Stefan",	"last" : "Walzer"}
         ],
         
         "editor": [ 
            "Fraunhofer Verlag"
         ],
         "editors": [
         	
            	{"first" : "Fraunhofer",	"last" : "Verlag"}
         ],
         "abstract": "Vor dem Hintergrund der werkstofflichen Ressourceneffizienz sind die Hersteller moderner Blechkonstruktionen bestrebt, innovative Produktionsprozesse zu entwickeln, um ihre Produkte effizienter zu gestalten. Dies ermöglicht es den Herstellern, trotz des globalen wirtschaftlichen Drucks weiterhin konkurrenzfähig zu agieren. Diesen Gegebenheiten geschuldet, wurden in der Vergangenheit mehrere mechanische aber auch thermische Fertigungsverfahren entwickelt, die die Formgebungsgrenzen moderner Blechbauteile erweitern beziehungsweise das Einstellen von anforderungsgerechten mechanischen Eigenschaften \u2013 respektive Bauteileigenschaften \u2013 ermöglichen.\r\nBisherige Arbeiten zur Prozessgrenzenerweiterung von formgebenden Verfahren in der Blechumformung befassen sich überwiegend mit thermischen Fertigungsverfahren. Zu mechanischen Fertigungsverfahren mittels oberflächennahen Prägens liegen hingegen bisher nur unzureichend wissenschaftliche Untersuchungen vor. Gegenstand der vorliegenden Dissertation ist daher die numerische und experimentelle Erforschung der Wirkung von lokal geprägten Oberflächenstrukturen in Blechplatinen und die dadurch initiierte Erhöhung bzw. Reduktion der mechanischen Kennwerte des Platinenwerkstoffs im Hinblick auf dessen Formänderungsvermögen. Im Rahmen dieser Arbeit wird eine neuartige Herangehensweise des Prägens von Blechplatinen untersucht, die die Herstellung qualitativ hochwertiger Blechformteile mit verbesserten mechanischen Eigenschaften ermöglicht.\r\nInnerhalb experimenteller Versuchsreihen werden Blechplatinen aus einer höherfesten Blechgüte (Dualphasenstahl DP 600) oberflächennah mittels Prägen strukturiert und die Oberflächenintegrität der geprägten Strukturen durch Messung der Topographie, der Mikrohärte und Härte beschrieben. Die geprägten Platinen werden anschließend durch Zug- und Nakajima-Versuche hinsichtlich ihres Formänderungsvermögens analysiert, charakterisiert und mit dem Referenzwerkstoff verglichen.\r\nAusgewählte Prägestrukturen werden für den Übertrag auf repräsentative Blechbauteile herangezogen, um den Nachweis der Prozessgrenzenerweiterung hinsichtlich der Erhöhung der Grenzziehverhältnisse zu erbringen. Hierbei stehen insbesondere die lokalen Wechselwirkungen in jenen Bauteilbereichen im Fokus, die hinsichtlich des Bauteilversagens kritisch zu bewerten sind, wie z.B. der Stempelkantenradius einer Rundnapfgeometrie. Durch die festigkeitssteigernde Wirkung der geprägten Oberflächenstrukturen wird die Streck- bzw. Fließgrenze des Blechwerkstoffes lokal erhöht, wodurch das vorzeitige Ausdünnen der Blechdicke reduziert und somit das Werkstoffversagen durch Reißer verhindert wird. Abschließend werden die gewonnenen Erkenntnisse auf eine komplexere Bauteilgeometrie übertragen. Parallel hierzu werden geprägte Strukturbauteile durch quasi-statische Stauchversuche geprüft und anhand der ertragbaren Spitzenlasten und deren spezifische Energieaufnahmefähigkeit untersucht.\r\nDer wesentliche Erkenntnisgewinn dieser Arbeit besteht daher einerseits im Nachweis erfolgreicher Methoden zur Erweiterung von Prozessgrenzen beim Tiefziehen hinsichtlich der Erhöhung erreichbarer Ziehtiefen und andererseits im Nachweis der verbesserten Energieaufnahmefähigkeit tiefgezogener Blechbauteile.\r\nDie mit dieser Arbeit vorliegende neue Methodik zur lokalen Verfestigung von Blechbauteilen durch Eindrücken sphärischer Vertiefungen in die Blechoberfläche, insbesondere in Bereichen frühzeitiger Blechausdünnung sowie der verbesserten Kraftaufnahme während eines Deformationsvorgangs bieten langfristig neue Konstruktions- und Auslegungsmöglichkeiten für industriell gefertigte Blechbauteile. Eine Kombination aus konventionellen Blechplatinen und Tailored Embossed Blanks (TEB) kann dabei den Einsatzbereich festigkeitsgradierter Blechbauteile weitreichend ergänzen. Denkbar sind beispielweise Anwendungen im modernen Karosseriebau sowie im Baugewerbe für Befestigungselemente.\r\nAm Ende der Arbeit liegt neben der Quantifizierung der Prozessgrenzenerweiterung von lokal geprägten und tiefgezogenen Blechbauteilen ein umfangreiches Prozessverständnis zu dieser neuen Technologie vor. Zur Erweiterung der herstellungsspezifischen Prozessgrenzen werden darüber hinaus neuartige Anwendungsfelder aufgezeigt, die eine Verbesserung technischer und wirtschaftlicher Anforderungen im industriellen Umfeld ermöglichen.",
         "isbn" : "978-3-8396-2087-8",
         
         "language" : "deutsch",
         
         "doi" : "https://doi.org/10.18419/opus-17576",
         
         "bibtexKey": "walzer2025"

      }
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         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/23a2cdeaf216b181a95713e929ac2d79b/roxyfoxy2211",         
         "tags" : [
            "KR","MG","ML","PH","imported"
         ],
         
         "intraHash" : "3a2cdeaf216b181a95713e929ac2d79b",
         "interHash" : "b3b14348307e6f0f5c6cfd3d6b4fa94d",
         "label" : "A strategy for minimizing the computational time of simulations involving near-surface embossing of sheet metal materials",
         "user" : "roxyfoxy2211",
         "description" : "",
         "date" : "2025-11-19 10:00:13",
         "changeDate" : "2025-11-19 10:00:13",
         "count" : 2,
         "pub-type": "inproceedings",
         "booktitle": "Material Forming: ESAFORM 2024","series": "Materials Research Proceedings","publisher":"Materials Research Forum LLC",
         "year": "2024", 
         "url": "", 
         
         "author": [ 
            "Pascal Heinzelmann","Marcel Görz","Kim Rouven Riedmüller","Mathias Liewald"
         ],
         "authors": [
         	
            	{"first" : "Pascal",	"last" : "Heinzelmann"},
            	{"first" : "Marcel",	"last" : "Görz"},
            	{"first" : "Kim Rouven",	"last" : "Riedmüller"},
            	{"first" : "Mathias",	"last" : "Liewald"}
         ],
         
         "editor": [ 
            "Anna Carla Araujo","Arthur Cantarel","France Chabert","Adrian Korycki","Philippe Olivier","Fabrice Schmidt"
         ],
         "editors": [
         	
            	{"first" : "Anna Carla",	"last" : "Araujo"},
            	{"first" : "Arthur",	"last" : "Cantarel"},
            	{"first" : "France",	"last" : "Chabert"},
            	{"first" : "Adrian",	"last" : "Korycki"},
            	{"first" : "Philippe",	"last" : "Olivier"},
            	{"first" : "Fabrice",	"last" : "Schmidt"}
         ],
         "pages": "2262--2270",
         "doi" : "10.21741/9781644903131-249",
         
         "bibtexKey": "Heinzelmann.2024"

      }
,
      {
         "type" : "Publication",
         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/2562838ad9108e5593f8ed7f2e09d7252/roxyfoxy2211",         
         "tags" : [
            "Embossing;Folding;Roller","KR","MG","ML","pinching"
         ],
         
         "intraHash" : "562838ad9108e5593f8ed7f2e09d7252",
         "interHash" : "076fab0c4ba8061fbcbee8aed781c63d",
         "label" : "Evaluation of Forming Methods for the Pre-shaping of Miura-Structures Made of Sheet Metal Materials",
         "user" : "roxyfoxy2211",
         "description" : "",
         "date" : "2025-11-19 10:00:13",
         "changeDate" : "2025-11-19 10:00:13",
         "count" : 1,
         "pub-type": "incollection",
         "booktitle": "Production at the Leading Edge of Technology","series": "Lecture Notes in Production Engineering","publisher":"Springer International Publishing","address":"Cham",
         "year": "2021", 
         "url": "", 
         
         "author": [ 
            "Marcel Görz","Mathias Liewald","Kim Rouven Riedmüller"
         ],
         "authors": [
         	
            	{"first" : "Marcel",	"last" : "Görz"},
            	{"first" : "Mathias",	"last" : "Liewald"},
            	{"first" : "Kim Rouven",	"last" : "Riedmüller"}
         ],
         
         "editor": [ 
            "Bernd-Arno Behrens","Alexander Brosius","Welf-Guntram Drossel","Wolfgang Hintze","Steffen Ihlenfeldt","Peter Nyhuis"
         ],
         "editors": [
         	
            	{"first" : "Bernd-Arno",	"last" : "Behrens"},
            	{"first" : "Alexander",	"last" : "Brosius"},
            	{"first" : "Welf-Guntram",	"last" : "Drossel"},
            	{"first" : "Wolfgang",	"last" : "Hintze"},
            	{"first" : "Steffen",	"last" : "Ihlenfeldt"},
            	{"first" : "Peter",	"last" : "Nyhuis"}
         ],
         "pages": "75--84","abstract": "By applying the rules of technical origami, flat raw materials can be folded into cellular structures. This also includes semi-finished sheet metal materials. Such folded sheet metal structures can be used as multifunctional core materials for sandwich-structured composites, offering outstanding strength and stiffness properties at comparatively low component weights. Applications of this type of sandwich-structured composites comprise, for example, optically appealing claddings, heat exchangers and, in particular, lightweight constructions. With regard to the production of such folded sheet metal structures, previous research work has shown that the desired shape can only be achieved when the bending axes are pre-shaped before folding. Thus, during design phase of component and process design, deformation and strain hardening effects of this pre-shaping must be taken into account. In this respect, the present paper dealswith the evaluation of different forming methods for the pre-shaping of folding axes into sheet metal materials. As methods for applying appropriate grooves, roller pinching and embossing are investigated by means of experiment and simulation. Finally, this paper shows that embossing and roller pinching disclose highest potential for processes for the pre-shaping of grooves required prior to the folding of multifunctional core materials for sandwich-structured composites.",
         "isbn" : "978-3-030-78423-2",
         
         "file" : "Görz, Liewald et al 2021 - Evaluation of Forming Methods:Attachments/Görz, Liewald et al 2021 - Evaluation of Forming Methods.pdf:application/pdf",
         
         "doi" : "10.1007/978-3-030-78424-99",
         
         "bibtexKey": "Gorz.2021"

      }
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         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/23ceb95a1f359241bfc5d00723eb5be13/roxyfoxy2211",         
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         "label" : "Data-Driven Derivation of Sheet Metal Properties Gained from Punching Forces Using an Artificial Neural Network",
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         "author": [ 
            "Adrian Schenek","Marcel Görz","Mathias Liewald","Kim Rouven Riedmüller"
         ],
         "authors": [
         	
            	{"first" : "Adrian",	"last" : "Schenek"},
            	{"first" : "Marcel",	"last" : "Görz"},
            	{"first" : "Mathias",	"last" : "Liewald"},
            	{"first" : "Kim Rouven",	"last" : "Riedmüller"}
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         "volume": "926","pages": "2174--2182","abstract": "The ongoing digitization of production processes provides new possibilities and potentials for process monitoring of forming and stamping processes. The component quality achievable by these processes is strongly dependent on the properties of the sheet metal material, so that a permanent digital recording of material data offers high potential for monitoring each component produced. In this context, presented paper deals with a novel AI-based method for the direct determination of material parameters from measured punching force curves. Using software systems Python and Tensor-Flow, an artificial neural network was first set up to determine mechanical material parameters (output data) from punching force curves (input data). As data basis for the adopted neural network, force curves were measured during punching of various sheet metal materials using a punching tool equipped with a direct force measurement device. Punching force curves were experimentally determined for the sheet metal materials DP1200, DP1000, DP800, DP600, HX380LA, DC03 and DX54. Additionally, tensile tests were performed for these sheet metal materials to determine ultimate tensile strengths (Rm), yield strengths (Rp0.2, Re), uniform strains (Ag), elongations at break (At) and strain hardening exponents (n). The presented paper reveals that neural networks can accurately quantify the relationship between characteristic parameters of punching force curves and the mentioned mechanical material properties.",
         "file" : "Schenek, Görz et al 2022 - Data-Driven Derivation of Sheet Metal:Attachments/Schenek, Görz et al 2022 - Data-Driven Derivation of Sheet Metal.pdf:application/pdf",
         
         "doi" : "10.4028/p-41602a",
         
         "bibtexKey": "Schenek.2022"

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         "label" : "An alternative time-based strategy for the evaluation of forming limits using optical experimental measurements",
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         "author": [ 
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         "label" : "Determining the residual formability of shear-cut sheet metal edges by utilizing an ML based prediction model",
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         "booktitle": "Material Forming: ESAFORM 2024","series": "Materials Research Proceedings","publisher":"Materials Research Forum LLC",
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         "author": [ 
            "Marcel Görz","Adrian Schenek","Trong Quan Vo","Kim Rouven Riedmüller","Mathias Liewald"
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            	{"first" : "Marcel",	"last" : "Görz"},
            	{"first" : "Adrian",	"last" : "Schenek"},
            	{"first" : "Trong Quan",	"last" : "Vo"},
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            	{"first" : "Mathias",	"last" : "Liewald"}
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         "editor": [ 
            "Anna Carla Araujo","Arthur Cantarel","France Chabert","Adrian Korycki","Philippe Olivier","Fabrice Schmidt"
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            	{"first" : "Adrian",	"last" : "Korycki"},
            	{"first" : "Philippe",	"last" : "Olivier"},
            	{"first" : "Fabrice",	"last" : "Schmidt"}
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         "pages": "1799--1806",
         "doi" : "10.21741/9781644903131-199",
         
         "bibtexKey": "Gorz.2024"

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         "label" : "Improving Part Quality During Deep Drawing by Modifying Friction Conditions via the Ram Movement",
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         "booktitle": "Production at the Leading Edge of Technology","publisher":"Springer Nature Switzerland","address":"Cham",
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         "author": [ 
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            	{"first" : "Lukas",	"last" : "Hauser"},
            	{"first" : "Marcel",	"last" : "Görz"},
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            	{"first" : "Papdo",	"last" : "Tchasse"},
            	{"first" : "Marcel",	"last" : "Görz"},
            	{"first" : "Kim Rouven",	"last" : "Riedmüller"},
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         "bibtexKey": "Tchasse.2025"

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         "label" : "Evaluation of Feature Engineering Methods for the Prediction of Sheet Metal Properties from Punching Force Curves by an Artificial Neural Network",
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            	{"first" : "Marcel",	"last" : "Görz"},
            	{"first" : "Adrian",	"last" : "Schenek"},
            	{"first" : "Mathias",	"last" : "Liewald"},
            	{"first" : "Kim Rouven",	"last" : "Riedmüller"}
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         "editor": [ 
            "Mingming Zhang","Zhiwei Peng","Bowen Li","Sergio Neves Monteiro","Rajiv Soman","Jiann-Yang Hwang","Yunus Eren Kalay","Juan P. Escobedo-Diaz","John S. Carpenter","Andrew D. Brown","Shadia Ikhmayies"
         ],
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            	{"first" : "Mingming",	"last" : "Zhang"},
            	{"first" : "Zhiwei",	"last" : "Peng"},
            	{"first" : "Bowen",	"last" : "Li"},
            	{"first" : "Sergio Neves",	"last" : "Monteiro"},
            	{"first" : "Rajiv",	"last" : "Soman"},
            	{"first" : "Jiann-Yang",	"last" : "Hwang"},
            	{"first" : "Yunus Eren",	"last" : "Kalay"},
            	{"first" : "Juan P.",	"last" : "Escobedo-Diaz"},
            	{"first" : "John S.",	"last" : "Carpenter"},
            	{"first" : "Andrew D.",	"last" : "Brown"},
            	{"first" : "Shadia",	"last" : "Ikhmayies"}
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         "pages": "75--85","abstract": "The part quality that can be achieved in forming and stamping processes strongly depends on the properties of the sheet metal material to be processed. However, since these material properties may fluctuate considerably and thus lead to the production of scrap, it is important to monitor such material fluctuations during part production. For this, the ongoing digitization of production processes provides new possibilities for part or quality monitoring. In this context, a novel AI-based method for the direct determination of material parameters from punching force curves measured in production was presented in a past study by the authors. This paper deals with the investigation of three further methods for extracting features from these recorded measuring data. In addition to domain knowledge-based feature engineering, statistical feature extraction (PCA) as well as a derivative-based method are analyzed and compared with each other and with the previously used AI (ANN) regarding their prediction accuracy of sheet metal properties.\n\n\n\nThe part quality that can be achieved in forming and stamping processes strongly depends on the properties of the sheet metal material to be processed. However, since these material properties may fluctuate considerably and thus lead to the production of scrap, it is important to monitor such material fluctuations during part production. For this, the ongoing digitization of production processes provides new possibilities for part or quality monitoring. In this context, a novel AI-based method for the direct determination of material parameters from punching force curves measured in production was presented in a past study by the authors. This paper deals with the investigation of three further methods for extracting features from these recorded measuring data. In addition to domain knowledge-based feature engineering, statistical feature extraction (PCA) as well as a derivative-based method are analyzed and compared with each other and with the previously used AI (ANN) regarding their prediction accuracy of sheet metal properties.",
         "isbn" : "978-3-031-22575-8",
         
         "file" : "Görz, Schenek et al 2023 - Evaluation of Feature Engineering Methods:Attachments/Görz, Schenek et al 2023 - Evaluation of Feature Engineering Methods.pdf:application/pdf",
         
         "doi" : "10.1007/978-3-031-22576-58",
         
         "bibtexKey": "Gorz.2023b"

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            	{"first" : "Mathias",	"last" : "Liewald"},
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         "interHash" : "aa935f81760adb85fa1da7081a66d296",
         "label" : "Optimierung des Wissenstransfers durch KI und Explainable AI",
         "user" : "roxyfoxy2211",
         "description" : "",
         "date" : "2025-11-19 10:00:13",
         "changeDate" : "2025-11-19 10:00:13",
         "count" : 1,
         "pub-type": "inproceedings",
         "booktitle": "Tagungsband T 56: Innovationsstandort Europa","address":"Hannover",
         "year": "2025", 
         "url": "", 
         
         "author": [ 
            "Marcel Görz","Mathias Liewald","Kim Rouven Riedmueller","Adrian Schenek"
         ],
         "authors": [
         	
            	{"first" : "Marcel",	"last" : "Görz"},
            	{"first" : "Mathias",	"last" : "Liewald"},
            	{"first" : "Kim Rouven",	"last" : "Riedmueller"},
            	{"first" : "Adrian",	"last" : "Schenek"}
         ],
         
         "editor": [ 
            " Europäische Forschungsgesellschaft für Blechverarbeitung"
         ],
         "editors": [
         	
            	{"first" : "",	"last" : "Europäische Forschungsgesellschaft für Blechverarbeitung"}
         ],
         
         "file" : "Görz, Liewald et al 2025 - Optimierung des Wissenstransfers durch KI:Attachments/Görz, Liewald et al 2025 - Optimierung des Wissenstransfers durch KI.pdf:application/pdf",
         
         "bibtexKey": "Gorz.2025"

      }
,
      {
         "type" : "Publication",
         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/2296897a8a662ff612afbe1ce2640ab2b/roxyfoxy2211",         
         "tags" : [
            "AS","Cutting","KR","MG","ML","force","learning;Punching","quality;Machine","surface"
         ],
         
         "intraHash" : "296897a8a662ff612afbe1ce2640ab2b",
         "interHash" : "e99b30e3706a6407ba5517f526ec6bef",
         "label" : "Prediction of Cutting Surface Parameters in Punching Processes Aided by Machine Learning",
         "user" : "roxyfoxy2211",
         "description" : "",
         "date" : "2025-11-19 10:00:13",
         "changeDate" : "2025-11-19 10:00:13",
         "count" : 2,
         "pub-type": "incollection",
         "booktitle": "TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings","series": "The Minerals, Metals & Materials Series","publisher":"Springer Nature Switzerland","address":"Cham",
         "year": "2023", 
         "url": "", 
         
         "author": [ 
            "Adrian Schenek","Marcel Görz","Mathias Liewald","Kim Rouven Riedmüller"
         ],
         "authors": [
         	
            	{"first" : "Adrian",	"last" : "Schenek"},
            	{"first" : "Marcel",	"last" : "Görz"},
            	{"first" : "Mathias",	"last" : "Liewald"},
            	{"first" : "Kim Rouven",	"last" : "Riedmüller"}
         ],
         "pages": "607--619","abstract": "Punching represents one of the most frequently used manufacturing processes in the sheet metal processing industry. As an important quality criterion for shear cutting processes, the geometric shape of the cutting surface is considered. In this regard, the edge draw-in height, the clean cut proportion, the fracture surface height, and the burr are relevant parameters for monitoring the production quality in punching processes. These parameters can easily be measured in shear cutting processes with an open cutting line (e.g. using laser triangulation). For processes with a closed cutting line, however, such a measurement is often not possible due to the limited accessibility. The present paper therefore proposes a machine learning approach, which enables a data-driven prediction of cutting surface parameters based on measurable process data. The new approach presented in this paper is to pre-train a neural network on numerically determined cutting force curves. As an output, the neural network predicts the mentioned quality parameters of punched sheet metal component edges. The output of the numerically pre-trained neural network is evaluated for numerically and experimentally determined process data and cutting surface parameters.",
         "file" : "Schenek, Görz et al 2023 - Prediction of Cutting Surface Parameters:Attachments/Schenek, Görz et al 2023 - Prediction of Cutting Surface Parameters.pdf:application/pdf",
         
         "isbn" : "978-3-031-22523-9",
         
         "bibtexKey": "Schenek.2023"

      }
	  
   ]
}