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Distortions in Additive Manufacturing (AM) Laser Metal Deposition (LMD) occur in the newly-built component due to rapid heating and solidification and can lead to shape deviations and cracking. This paper presents a novel approach to quantify the distortions experimentally and to use the results in numerical simulation validation. Digital Image Correlation (DIC) is applied together with optical filters to measure in-situ distortions directly on a wall geometry produced with LMD. The wall shows cyclic Expansion and shrinking with the edges bending inward and the top of the sample exhibiting a slight u-shape as residual distortions. Subsequently, a structural Finite Element Analysis (FEA) of the experiment is established, calibrated against experimental temperature profiles and used to predict the in-situ distortions of the sample. A comparison of the experimental and numerical results reveals a good agreement in length direction of the sample and quantitative deviations in height direction, which are attributed to the material model used. The suitability of the novel experimental approach for measurements on an AM sample is shown and the potential for the validated numerical model as a predictive tool to reduce trial-and-error and improve part quality is evaluated.
A 3D electro-thermomechanical model is established in order to investigate liquid metal embrittlement.
After calibration to a dual phase steel of the 1000 MPa tensile strength class, it is used to analyse the thermo-mechanical system of an experimental procedure to enforce liquid metal embrittlement during resistance spot welding. In this procedure, a tensile stress level is applied to zinc coated advanced high strength steel samples during welding. Thereby, liquid metal embrittlement formation is enforced, depending on the applied stress level and the selected material.
The model is suitable to determine and visualise the corresponding underlying stresses and strains responsible for the occurrence of liquid metal embrittlement. Simulated local stresses and strains show good conformity with experimentally observed surface crack locations.
Rapid localized heating and cooling during additive manufacturing using laser deposition method (LMD) lead to loss of dimensional accuracy as well as cracking of built parts. Finite-Element welding simulations allow prediction of geometrical deviations and accumulated residual stresses as well as their optimization before conducting experiments. Due to the great length of stacked welds, calculation times for fully transient thermomechanical simulations are currently long, the calculation stability suffers from the high number of contact bodies in the model and the modelling effort is high, as the geometries need to be sliced and positioned layer-wise.
In this contribution, an integrated modelling approach is demonstrated for a thin-walled LMD component made from 30 layers of 1.4404 (316L) stainless steel: Instead of the layer-by-layer modelling strategy commonly found in the literature, the whole component mesh is kept in one piece and the fully transient, layer-by-layer material deposition is implemented via element sets. In contrast to prior simulations, nonlinear contact between the layers does not have to be considered, significantly decreasing calculation times. The calculated distortions are compared to recently published, in-situ digital image correlation (DIC) measurements as well as numerical simulations conducted with the established layer-wise modelling strategy to judge result quality. Finally, the improvement in calculation time and ease-of-use is compared between both modelling approaches and conclusions regarding future usage for industrial-scale components are drawn.
Some zinc coated advanced high strength steels (AHSS), under certain manufacturing conditions, are known to be prone to liquid metal embrittlement (LME) during resistance spot welding. LME is an undesired phenomenon, which can cause both surface and internal cracks in a spot weld, potentially influencing its strength. An effort is made to understand influencing factors of LME better, and evaluate geometry-material combinations regarding their LME susceptibility. Manufacturers benefit from such knowledge because it improves the processing security of the materials. The experimental procedure of welding under external load is performed with samples of multiple AHSS classes with strengths up to 1200 MPa, including dual phase, complex phase and TRIP steels. This way, externally applied tensile load values are determined, which cause liquid metal embrittlement in the samples to occur. In the future, finite element simulation of this procedure gives access to in-situ stress and strain values present during LME formation. The visualization improves the process understanding, while a quantification of local stresses and strains allows an assessment of specific welded geometries.
Liquid metal embrittlement (LME) cracking can occur during resistance spot welding (RSW) in zinc-coated advanced high-strength steels (AHSS) for automotive production. In this study, a methodological variation of hold time is performed to investigate the process-related crack influence factors. A combination of numerical and experimental investigations confirms, that the extent of heat dissipation and re-heating of the sheet surface can be influenced and thus the degree of crack formation can be controlled in a targeted manner by the parameterisation of the hold time. The temperature and stress history of crack-free and crack-afflicted spot welds are analysed and a conclusion on the borders defining the LME active region is derived.
Die numerische Simulation hilft, Probleme bei additiven Bauprozessen früh zu erkennen und Optimierungspotentiale auszuschöpfen. Ziel ist, im additiven Auftragschweißen (DED) die Zahl der nötigen Versuche durch Vorhersagen zu verringern und Prozessgrößen zu visualisieren. Eine besondere Anwendung der Simulation ist die Generierung verzugskompensierter Geometrien: Durch die Berechnung des Bauteilverzugs kann die Geometrie vor dem Bauen so verändert werden, dass sie mit Verzug die gewünschte Toleranz erreicht. So kann Zerspanvolumen und Aufmaß reduziert werden.
The contact between train wheels and rail tracks is known to induce material degradation in the form of wear, and rolling contact fatigue in the railhead. Rails with a pearlitic microstructure have proven to provide the best wear resistance under severe wheel-rail interaction in heavy haul applications. High speed laser cladding, a state-of-the-art surface engineering technique, is a promising solution to repair damaged railheads. However, without appropriate preheating or processing strategies, the utilized steel grades lead to martensite formation and cracking during deposition welding.
In this study, laser cladding of low-alloy steel at very high speeds was investigated, without preheating the railheads. Process speeds of up to 27 m/min and laser power of 2 kW are used. The clad, heat affected zone and base material are examined for cracks and martensite formation by hardness tests and metallographic inspections. A methodology for process optimization is presented and the specimens are characterized for suitability. Within the resulting narrow HAZ, the hardness could be significantly reduced.
Das Widerstandspunktschweißen ist ein etabliertes Fügeverfahren in der Automobilindustrie. Es wird vor allem bei der Herstellung sicherheitsrelevanter Bauteile, zum Beispiel der Karosserie, eingesetzt. Daher ist eine kontinuierliche Prozessüberwachung unerlässlich, um die hohen Qualitätsanforderungen zu erfüllen. Künstliche neuronale Netzalgorithmen können zur Auswertung der Prozessparameter und -signale eingesetzt werden, um die individuelle Schweißpunktqualität zu gewährleisten. Die Vorhersagegenauigkeit solcher Algorithmen hängt von dem zur Verfügung gestellten Trainingsdatensatz ab. In diesem Beitrag wird untersucht, inwieweit die Vorhersagegüte eines künstlichen neuronalen Netzes durch Auswertung einer Prozessgröße, dem dynamischen Widerstand, verbessert werden kann.
Resistance spot welding is an established joining process for the production of safetyrelevant 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.