Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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Energieeffizienzsteigerung mit IoT-basiertem Monitoringsystem

2024-09-22 , Uhlmann, Eckart , Polte, Julian , Geisert, Claudio

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Flexibles AM ermöglicht wettbewerbsfähige Produktion

2024-05 , Müller, Vinzenz , Fasselt, Janek Maria , Klötzer-Freese, Christian , Kruse, Tobias , Wagner, Florian

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Numerical and experimental assessment of liquid metal embrittlement in externally loaded spot welds

2024-01-30 , Prabitz, Konstantin , Antretter, Thomas , Rethmeier, Michael , El-Sari, Bassel , Schubert, Holger , Hilpert, Benjamin , Gruber, Martin , Sierlinger, Robert , Ecker, Werner

Zinc-based surface coatings are widely applied with high-strength steels in automotive industry. Some of these base materials show an increased brittle cracking risk during loading. It is necessary to examine electrogalvanized and uncoated samples of a high strength steel susceptible to liquid metal embrittlement during spot welding with applied external load. Therefore, a newly developed tensile test method with a simultaneously applied spot weld is conducted. A fully coupled 3D electrical, thermal, metallurgical and mechanical finite element model depicting the resistant spot welding process combined with the tensile test conducted is mandatory to correct geometric influences of the sample geometry and provides insights into the sample’s time dependent local loading. With increasing external loads, the morphology of the brittle cracks formed is affected more than the crack depth. The validated finite element model applies newly developed damage indicators to predict and explain the liquid metal embrittlement cracking onset and development as well as even ductile failure.

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Numerical chip formation analysis during high-pressure cooling in metal machining

2024 , Uhlmann, Eckart , Barth, Enrico , Bock-Marbach, Benjamin , Kuhnert, Jörg

Most metal turning processes utilize cutting fluids. Despite extensive experimental and analytical studies, the mechanisms of chip formation under consideration of a cutting fluid are still not entirely understood. Due to fluid-structure interaction, simulating wet cutting processes for an extended duration has not been feasible. The primary objective of this study is to utilize a simulation approach to provide additional information about the wet chip formation process in contrast to measurement methods, with a view to drawing conclusions. As methodology the Finite-Pointset-Method (FPM) is employed to simulate the chip formation process for dry, flood and specifically high-pressure cooling conditions during machining of carbon steel C45 as well as nickel-based alloy Inconel 718. Due to the increased relative velocity between workpiece and cutting fluid with the use of high-pressure cooling compared to flood cooling, numerical stability issues are present. Initially, the modeling approach to handle high-pressure cooling conditions is described and validated by an impact test. Subsequently the cutting simulation model is presented in detail and verified by measurements. The simulation results of stress, temperature and plastic strain rate fields are used to elucidate the observed discrepancies between various cutting fluid strategies in detail. These findings suggest explanations for the high efficiency of high-pressure cooling such as a decline of hydrostatic stresses or activation of ductile damaging.

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Detektion von Bindefehlern beim DED-Arc

2024-08 , Neumann, Benedikt Bog Man , Biegler, Max , Goecke, Sven-Frithjof , Rethmeier, Michael

In dem Beitrag wird aufbauend auf Machine-Learning-Modellen, welche bereits zum Überwachen des Schutzgasschweißen erforscht wurden, ein tiefes neuronales Netz (DNN) zum Monitoring beim DED-Arc von Aluminium vorgestellt. Ziel ist die Detektion von Bindefehlern in den aufgebauten Volumina auf Grundlage von in Echtzeit gemessenen Schweißstromquellensignalen. Es werden Merkmalsvariablen durch Vorverarbeitung extrahiert sowie die Korrelation zwischen den Merkmalsvariablen und den Defekten analysiert. Durch den vorgestellten Algorithmus werden diese automatisiert als Input an ein DNN übergeben. Das entwickelte und trainierte neuronale Netz erkennt anhand signifikanter Merkmale aus den Strom- und Spannungsdaten Bindefehler mit einer Genauigkeit von ca. 90 Prozent.

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Digital Twins within the Circular Economy: Literature Review and Concept Presentation

2024-03-26 , Mügge, Janine , Seegrün, Anne , Hoyer, Tessa-Katharina , Riedelsheimer, Theresa , Lindow, Kai

Digital twins offer a promising approach to sustainable value creation by providing specific life cycle data and enabling the monitoring and implementation of circular economy strategies throughout the product’s life cycle. By analyzing product, component, and material data, as well as process data, it is possible to create transparency throughout a product’s life cycle, build a data-driven product ecosystem, and establish new business and value creation models, from SMEs to large enterprises. This paper identifies application scenarios, their technological readiness level, and the challenges of digital twins for the circular economy in the manufacturing industry based on a systematic literature review. Gaps such as ensuring a continuous flow of information and taking into account the different levels of digitalization of companies are identified. As a main result, a holistic concept for the scoping of a digital twin for the circular economy is presented. One specific use case for end-of-life decision-making is elaborated upon. It is shown that the circular economy can be supported by digital twin data, especially for the optimal decision on end-of-life vehicles.

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KI zur Prozessüberwachung im Unterpulverschweißen

2024-01-15 , El-Sari, Bassel , Gook, Sergej , Biegler, Max , Rethmeier, Michael

Beim Unterpulverschweißen sind die Prozessgeräusche ein Indikator für eine gute Fügequalität. Diese Beurteilung kann i.d.R. nur von einer erfahrenen Fachkraft durchgeführt werden. Eine kürzlich entwickelte künstliche Intelligenz kann automatisch das akustische Prozesssignal anhand vortrainierter Merkmale klassifizieren und die Fügequalität anhand des Geräuschs beurteilen. Der Algorithmus, einmal richtig trainiert, kann den Prüfaufwand beim Unterpulverschweißen deutlich reduzieren.

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Application of AI-based welding process monitoring for quality control in pipe production

2024-07-29 , Gook, Sergej , El-Sari, Bassel , Biegler, Max , Rethmeier, Michael

The paper presents the experimental results into the development of a multi-channel system for monitoring and quality assurance of the multi-wire submerged arc welding (SAW) process for the manufacture of large diameter pipes. Process signals such as welding current, arc voltage and the acoustic signal emitted from the weld zone are recorded and processed to provide information on the stability of the welding process. It was shown by the experiments that the acoustic pattern of the SAW process in a frequency range between 30 Hz and 2.5 kHz contains the most diagnostic information. The on-line quality assessment of the weld seam produced is carried out in combination with methods of artificial intelligence (AI). From the results obtained, it can be concluded that the use of the latest concepts in welding and automation technology, combined with the high potential of AI, can achieve a new level of quality assurance in pipe manufacturing.

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Qualitätssicherung beim Laser-Pulver-Auftragschweißen

2024-02-01 , Lemke, Josefine

In der modernen Fertigungstechnik spielt der Einsatz von korrosionsbeständigem Stahl eine große Rolle. Die kohlenstoffarme Edelstahllegierung 1.4404 (AISI 316L) ist ein häufig eingesetzter Werkstoff in der additiven Fertigung. Hier kann das Laser-Pulver-Auftragschweißen (LPA), engl. Directed Energy Deposition DED-LB, das weit verbreitete Laser Powder Bed Fusion (L-PBF) aufgrund höherer Auftragsraten und seiner Flexibilität in einigen Bereichen ergänzen und sogar ersetzen. Mit interessanten Perspektiven in der Bremsscheibenfertigung für Automobile.

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Expulsion prevention in resistance spot welding of dissimilar joints with ultra-high strength steel: An analysis of the mechanism and effect of preheating current

2024 , Yang, Keke , El-Sari, Bassel , Olfert, Viktoria , Wang, Zhuoqun , Biegler, Max , Rethmeier, Michael , Meschut, Gerson

The widespread adoption of ultra-high strength steels, due to their high bulk resistivity, intensifies expulsion issues in resistance spot welding (RSW), deteriorating both the spot weld and surface quality. This study presents a novel approach to prevent expulsion by employing a preheating current. Through characteristic analysis of joint formation under critical welding current, the importance of plastic material encapsulation around the weld nugget (plastic shell) at high temperatures in preventing expulsion is highlighted. To evaluate the effect of preheating on the plastic shell and understand its mechanism in expulsion prevention, a two-dimensional welding simulation model for dissimilar ultra-high strength steel joints was established. The results showed that optimal preheating enhances the thickness of the plastic shell, improving its ability to encapsulate the weld nugget during the primary welding phase, thereby diminishing expulsion risks. Experimental validation confirmed that by employing the optimal preheating current, the maximum nugget diameter was enhanced to 9.42 mm, marking an increase of 13.4 % and extending the weldable current range by 27.5 %. Under quasi-static cross-tensile loading, joints with preheating demonstrated a 7.9 % enhancement in maximum load-bearing capacity compared to joints without preheating, showing a reproducible and complete pull-out failure mode within the heat-affected zone. This study offers a prevention method based on underlying mechanisms, providing a new perspective for future research on welding parameter optimization with the aim of expulsion prevention.

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