Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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  • Publication
    High-power laser beam welding for thick section steels - new perspectives using electromagnetic systems
    ( 2022)
    Rethmeier, M.
    ;
    Gumenyuk, A.
    ;
    Bachmann, M.
    In recent years, it was shown that the introduction of additional oscillating and permanent magnetic fields to laser beam and laser-arc hybrid welding can bring several beneficial effects. Examples are a contactless weld pool support for metals of high thickness suffering from severe drop-out when being welded conventionally or an enhanced stirring to improve the mixing of added filler material in the depth of the weld pool to guarantee homogeneous resulting mechanical properties of the weld. The latest research results show the applicability to various metal types over a wide range of thicknesses and welding conditions. The observations made were demonstrated in numerous experimental studies and a deep understanding of the interaction of the underlying physical mechanisms was extracted from numerical calculations.
  • Publication
    Selection of diamond coated silicon nitrides for use as ceramic end mills when machining glass and carbon fiber reinforced plastics
    ( 2021)
    Stawiszynski, Bartek
    ;
    Protz, Falk
    ;
    Uhlmann, Eckart
    The machining of advanced materials is challenging the tooling technology. To cope this, new cutting materials and geometries are developed. In this case, four types of silicon nitride based ceramics are investigated after coating with CVD-diamond for the use as ceramic end mills. To assess the wear resistance, model wear tests and damage evaluation were carried out and evaluated. After subsequent summary of the results, ceramic and cemented carbide substrate were selected for milling of glass and carbon fiber reinforced plastic (GFRP/CFRP) The determined force distribution and wear properties of the tools are presented and discussed. The diamond coated substrates are qualified by their wear profile and the measured constant cutting forces for use in the machining of GFRP/CFRP. Finally, the crack formation progress for diamond coated silicon nitrides when machining glass and carbon fiber reinforced plastics was demonstrated.
  • Publication
    Numerical Analysis of the Partial Penetration High Power Laser Beam Welding of Thick Sheets at High Process Speeds
    ( 2021)
    Artinov, A.
    ;
    Meng, X.
    ;
    Bachmann, M.
    ;
    Rethmeier, M.
    The present work is devoted to the numerical analysis of the high-power laser beam welding of thick sheets at different welding speeds. A three-dimensional transient multi-physics numerical model is developed, allowing for the prediction of the keyhole geometry and the final penetration depth. Two ray tracing algorithms are implemented and compared, namely a standard ray tracing approach and an approach using a virtual mesh refinement for a more accurate calculation of the reflection point. Both algorithms are found to provide sufficient accuracy for the prediction of the keyhole depth during laser beam welding with process speeds of up to 1.5 m min-1. However, with the standard algorithm, the penetration depth is underestimated by the model for a process speed of 2.5 m min-1 due to a trapping effect of the laser energy in the top region. In contrast, the virtually refined ray tracing approach results in high accuracy results for process speeds of both 1.5 m min-1 and 2.5 m min-1. A detailed study on the trapping effect is provided, accompanied by a benchmark including a predefined keyhole geometry with typical characteristics for the high-power laser beam welding of thick plates at high process speed, such as deep keyhole, inclined front keyhole wall, and a hump.
  • Publication
    Schweißen unter Zug - LME-Eingangsprüfung für die Autoindustrie
    ( 2021)
    El-Sari, Bassel
    ;
    Biegler, Max
    ;
    Rethmeier, Michael
    Der Trend zum Leichtbau und die Transformation zur E-Mobilität in der Automobilindustrie befeuern die Entwicklung neuer hochfester Stähle für den Karosseriebau. Derartige Werkstoffe sind beim Widerstandspunktschweißen besonders rissanfällig (LME). Das Schweißen unter Zug stellt eine effektive Methode um die LME-Anfälligkeit unterschiedlicher Werkstoffe qualitativ zu bestimmen.
  • Publication
    The Effects of HLAW Parameters for One Side T-Joints in 15 mm Thickness Naval Steel
    ( 2021)
    Churiaque, C.
    ;
    Sánchez-Amaya, J.M.
    ;
    Porrúa-Lara, M.
    ;
    Gumenyuk, A.
    ;
    Rethmeier, M.
    The present contribution is the first research reporting full penetration HLAW joints in 15 mm thick EH36 steel butt T-welds with square grooves on 2F welding position by single-sided welding. The effects of welding parameters were investigated to increase the quality of the joints. Conditions leading to defect-free full penetration welds fulfilling naval regulations includes a laser power of 12.5 kW, a welding speed of 1.6 m/min and the vertical laser offset distance from the flange of 1 mm. Advanced characterization of selected welds included a microstructural identification by optical microscopy, SEM, and XRD, revealing the presence of acicular, polygonal and Widmanstätten ferrite, lath martensite, and some retained austenite at FZ. Hardness and microhardness mapping tests showed values of 155 HV at base metal and 200 to 380 HV at the fusion zone connecting the web to the flange.
  • Publication
    Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination with Process Signals in Resistance Spot Welding of Advanced High-Strength Steels
    ( 2021)
    El-Sari, Bassel
    ;
    Biegler, Max
    ;
    Rethmeier, Michael
    Resistance spot welding is an established joining process for the production of safety-relevant components in the automotive industry. Therefore, consecutive process monitoring is essential to meet the high quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals, to ensure individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set, and the prediction of untrained data is challenging. The aim of this paper was to investigate the extrapolation capability of a multi-layer perceptron model. That means, the predictive performance of the model was tested with data that clearly differed from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the training datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of process signals on the predictive accuracy and robustness of artificial neural network algorithms. Especially, when predicting a data set from outside of the training space.
  • Publication
    Shielded metal arc welding of 9%Ni steel using matching ferritic filler metal
    ( 2021)
    El-Batahgy, A.
    ;
    Saiyah, A.
    ;
    Khafagi, S.
    ;
    Gumenyuk, A.
    ;
    Gook, S.
    ;
    Rethmeier, M.
    Motivated by the tensile strength loss of 9%Ni steel arc welded joints made using Ni-based austenitic filler metals, the feasibility of maintaining the tensile strength using matching ferritic filler metal has been demonstrated. In comparison with shielded metal arc welded joint made using Ni-based austenitic electrode ENiCrMo-6, higher tensile strength comparable to that of the base metal was obtained using matching ferritic electrode. Besides, sufficient impact toughness energies with much lower mismatch were obtained for weld metal and heat-affected zone. Welded joint with a lower mechanical mismatching is of considerable importance for achieving acceptable combination of tensile strength and impact toughness. A better combination of these mechanical properties is ensured by applying a post weld heat treatment.
  • Publication
    Quantifying mechanical properties of automotive steels with deep learning based computer vision algorithms
    ( 2020)
    Javaheri, E.
    ;
    Kumala, V.
    ;
    Javaheri, A.
    ;
    Rawassizadeh, R.
    ;
    Lubritz, J.
    ;
    Graf, B.
    ;
    Rethmeier, M.
    This paper demonstrates that the instrumented indentation test (IIT), together with a trained artificial neural network (ANN), has the capability to characterize the mechanical properties of the local parts of a welded steel structure such as a weld nugget or heat affected zone. Aside from force-indentation depth curves generated from the IIT, the profile of the indented surface deformed after the indentation test also has a strong correlation with the materials' plastic behavior. The profile of the indented surface was used as the training dataset to design an ANN to determine the material parameters of the welded zones. The deformation of the indented surface in three dimensions shown in images were analyzed with the computer vision algorithms and the obtained data were employed to train the ANN for the characterization of the mechanical properties. Moreover, this method was applied to the images taken with a simple light microscope from the surface of a specimen. Therefore, it is possible to quantify the mechanical properties of the automotive steels with the four independent methods: (1) force-indentation depth curve; (2) profile of the indented surface; (3) analyzing of the 3D-measurement image; and (4) evaluation of the images taken by a simple light microscope. The results show that there is a very good agreement between the material parameters obtained from the trained ANN and the experimental uniaxial tensile test. The results present that the mechanical properties of an unknown steel can be determined by only analyzing the images taken from its surface after pushing a simple indenter into its surface.
  • Publication
    Geometric distortion-compensation via transient numerical simulation for directed energy deposition additive manufacturing
    ( 2020)
    Biegler, Max
    ;
    Elsner, B.A.M.
    ;
    Graf, B.
    ;
    Rethmeier, Michael
    Components distort during directed energy deposition (DED) additive manufacturing (AM) due to the repeated localised heating. Changing the geometry in such a way that distortion causes it to assume the desired shape - a technique called distortion-compensation - is a promising method to reach geometrically accurate parts. Transient numerical simulation can be used to generate the compensated geometries and severely reduce the amount of necessary experimental trials. This publication demonstrates the simulation-based generation of a distortion-compensated DED build for an industrial-scale component. A transient thermo-mechanical approach is extended for large parts and the accuracy is demonstrated against 3d-scans. The calculated distortions are inverted to derive the compensated geometry and the distortions after a single compensation iteration are reduced by over 65%.