Wissenschaftliche Artikel der BAM
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- 2022 (5) (entfernen)
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- Artificial neural network (1)
- Bead-on-plate welds (1)
- Charpy impact toughness (1)
- Critical strain (1)
- DED (1)
- Data preparation (1)
- Electromagnetic backing (1)
- L-PBF (1)
- Laser Metal Deposition (LMD) (1)
- Laser Welding (1)
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The study deals with the influence of the heat input and the resulting cooling times on the microstructure and Charpy impact toughness of single-pass laser hybrid welded 20-mm thick high-strength steel S690QL. The main focus is on the change of the mechanical properties over the entire seam thickness. The cooling times were measured in-situ using a pyrometer and an optical fibre in three different depths of the seam where Charpy impact test specimens were also later taken. Thereby, three different heat inputs from 1.3 kJ/mm to 2 kJ/mm were investigated. Despite the observed decreased values of both t8/5-cooling time and the Charpy impact toughness in the root part of the seam, the required impact toughness of 38 J/cm2 could be reached in dependance on applied heat input, especially at the heat input of 1.6 kJ/mm.
Solidification cracking is still a serious problem in laser beam welding, especially in the welding of thick-walled plates. The influence of weld pool geometry on solidification cracking in partial penetration welding of thick plates is investigated within scope of this study. Therefore, a specific experimental setup of steel and quartz glass in butt configuration and on the side with high-speed camera were used to record the weld pool shape. In addition, the influence of laser inclination angle on the weld pool geometry and on solidification crack formation was investigated. The results show a bulge in the weld pool root, which is separated from an upper region by a necking region. This leads to a case where there are three different longitudinal region lengths with different solidification zones. This temporal sequence of solidification strongly promotes the formation of solidification cracks in the weld root.
With regard to efficient production, it is desirable to combine the respective advantages of additively and conventionally manufactured components. Particularly in the case of large-volume components that also include filigree or complex structures, it makes sense to divide the overall part into individual elements, which afterwards have to be joined by welding.
The following research represents a first step in fundamentally investigating and characterizing the joint welding of Laser Powder Bed Fusion (L-PBF) components made of Inconel 718. For this purpose, bead-on-plate welds were performed on plates manufactured using the L-PBF process and compared with the conventionally manufactured material. Conventional laser beam welding was used as welding process. The weld geometry was investigated as a function of the L-PBF build-up orientation. It was found that the welding depth and weld geometry differ depending on this orientation and in comparison to the conventional material.
The Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the quality prediction of various geometries, including weld beads, coatings and cubes. The results were compared to ANNs trained with process parameters such as laser power, scan speed and powder mass flow. It was shown that the use of measurement data provides additional value. Neural networks trained with measurement data achieve significantly higher prediction accuracy, especially for more complex geometries.
Laser metal deposition (LMD) as an additive manufacturing technique became increasingly important in recent years and thus the demand for component safety. This is the reason, for the need for reliable in-situ defect detection techniques. For laser beam weld seams an optical measurement technique based on an optical flow algorithm was successfully used to define the critical straining conditions that lead to hot cracking. This algorithm was adapted for bead-on-plate weld seams on LMD deposited layers of IN718 alloy while performing external strain on the specimen in an externally loaded hot cacking test facility. The resulting transversal hot cracks along the weld seam were localized via X-Ray inspection and the type of cracking confirmed by Scanning Electron Microscopy (SEM). The strain distribution was measured in the vicinity of the solidification front and correlated to the detected hot cracks. Based on the results this technique could be adopted for LMD experiments.