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- Additive manufacturing (6)
- Artificial neural network (4)
- Laser metal deposition (4)
- DED (3)
- Design of experiments (3)
- Quality assurance (3)
- Cladding parameter (2)
- Data preparation (2)
- Inconel 718 (2)
- Laser cladding (2)
Organisationseinheit der BAM
Die Weiterentwicklung des Laser-Pulver-Auftragschweißens im Bereich der additiven Fertigung stellt dieses Verfahren vor neue Herausforderungen.
Bauteile können schnell und endformnah hergestellt werden, was diese Technik für den Prototypenbau sowie Kleinserienfertigung interessant macht.
Die Vielzahl an Varianten, um ein Volumen aus einzelnen Schichten aufzubauen, erhöhen die Möglichkeiten die Eigenschaften des entstehenden Bauteils zu beeinflussen. Hierfür müssen Strategien entwickelt werden, welche einen gleiclunäßigen Materialauftrag bei gleichzeitig optimalen Materialeigenschaften gewährleisten.
Die durchgeführten Untersuchungen behandeln den Einfluss von Pendelstrategien sowie Spiralstrategien auf die Temperaturentwicklung und stellen diese vergleichend gegenüber. Um den unterschiedlichen Bedingungen bei der Bauteilfertigung Rechnung zu tragen, wurden Versuche in Randbereichen sowie im zentralen Bereich des Testobjekts untersucht. Die Ergebnisse verdeutlichen die Notwendigkeit angepasste Baustrategien zu entwickeln, um durch einen stabilen Fertigungsprozess Qualität und Eigenschaften eines Bauteils gewährleisten zu können.
The process of laser cladding has become more important during recent years because of its broad application for cladding, repair or additive manufacturing. In the field of mechanical engineering, one use is the repair of turbine blades. For high quality and reliability of the repaired components, it is necessary to adjust the weld bead geometry to the specific repair task. The bead geometry influences the metallurgical bonding and the degree of dilution as well as the formation of defects like pores or cracks. Therefore, it is important to know the effects of the different parameters on the welding bead. A valuable tool to meet this industrial challenge is the design of experiments (DoE). In this context, the user can choose between a huge number of test plans. Greater Profit of information is expected by a larger test range. In order to confirm the acceptance, a five-step full factorial test plan is compared to a central composite design in this paper. Moreover, the limits of the experimental range are indicated and restrictions can be derived. As the results show, the essential effects are detected with a full factorial test plan as well as with a central composite design. Merely the effect strength could not always be specified unambiguously. On this account and in consideration of cost efficiency, the use of central compound design is recommended in industrial applications.
Laser metal deposition (LMD) is an established technology for two-dimensional surface coatings. It offers high deposition rates, high material flexibility, and the possibility to deposit material on existing components. Due to these features, LMD has been increasingly applied for additive manufacturing of 3D structures in recent years. Compared to previous coating applications, additive manufacturing of 3D structures leads to new challenges regarding LMD process knowledge. In this paper, the process steps for LMD as additive manufacturing technology are described. The experiments are conducted using titanium alloy Ti-6Al-4Vand Inconel 718. Only the LMD nozzle is used to create a shielding gas atmosphere. This ensures the high geometric flexibility needed for additive manufacturing, although issues with the restricted size and quality of the shielding gas atmosphere arise. In the first step, the influence of process parameters on the geometric dimensions of single weld beads is analyzed based on design of experiments. In the second step, a 3D build-up strategy for cylindrical specimen with high dimensional accuracy is described. Process parameters, travel paths, and cooling periods between layers are adjusted. Tensile tests show that mechanical properties in the as-deposited condition are close to wrought material. As practical example, the fir-tree root profile of a turbine blade is manufactured. The feasibility of LMD as additive technology is evaluated based on this component.
Pores in additive manufactured metal parts occur due to different reasons and affect the part Quality negatively. Few investigations on the origins of porosity are available, especially for Ni-based super alloys. This paper presents a new study to examine the influence of common processing Parameters on the Formation of pores in parts built by laser metal Deposition using Inconel 718 powder. Further, a comparison between the computed tomography (CT) and the Archimedes method was made. The Investigation Shows that CT is able to identify different kinds of pores and to give further Information about their distribution. The identification of some pores as well as their shape can be dependent on the Parameter Setting of the Analysis tool. Due to limited measurement Resolution, CT is not able to identify correctly pores with Diameters smaller than 0.1 mm, which leads to a false decrease on Overall porosity. The applied Archimedes method is unable to differentiate between gas porosity and other Kinds of holes like internal cracks or lack of Fusion, but it delivered a proper value for Overall porosity. The method was able to provide suitable data for the statistical Evaluation with design of Experiments, which revealed significant Parameters ont he Formation of pores in LMD.
The process of laser metal deposition can be applied in many ways. Mostly, it is relevant to coating, for repair welding and for additive manufacturing. To increase the effectiveness and the productiveness, a good process understanding is necessary. Statistical test planning is effectual and often used for this purpose. For financial and temporal reasons, a restriction of the test space is reasonable. In this case, it is recommended to use a D-optimal experimental design which is practically applied to extend existing test plans or if process Limits are known. This paper investigates the applicability of a D-optimum experimental design for the laser metal deposition. The results are compared to the current results of a full factorial test plan.
Known restrictions are used for the limitation of the test space. Ti6Al4 is utilized as Substrate material and powder. Comparable results of the D-optimal experimental design and of the full factorial test plan can be demonstrated. However, 80 % of time can be saved by the experimental procedure. For this reason, the application of D-optimal experimental design for laser metal deposition is recommend.
The advantage of selective laser melting (SLM) is its high accuracy and geometrical flexibility.
Because the maximum size of the components is limited by the process chamber, possibilities must be found to combine several parts manufactured by SLM. An application where this is necessary, is, for example, the components of gas turbines, such as burners or oil return pipes, and inserts, which can be joined by circumferential welds. However, only a few investigations to date have been carried out for the welding of components produced by SLM. The object of this paper is, therefore, to investigate the feasibility of laser beam welding for joining SLM tube connections made of nickel-based alloys.
For this purpose, SLM-manufactured Inconel 625 and Inconel 718 tubes were welded with a Yb:YAG disk laser and subsequently examined for residual stresses and defects. The results showed that the welds had no significant influence on the residual stresses. A good weld quality could be achieved in the seam circumference. However, pores and pore nests were found in the final overlap area, which meant that no continuous good welding quality could be accomplished. Pore formation was presumably caused by capillary instabilities when the laser power was ramped out.
Additive manufacturing promises a high potential for the maritime sector. Directed Energy Deposition (DED) in particular offers the opportunity to produce large-volume maritime components like propeller hubs or blades without the need of a costly casting process. The post processing of such components usually generates a large amount of aluminum bronze grinding waste. The aim of the presented project is to develop a sustainable circular AM process chain for maritime components by recycling aluminum bronze grinding waste to be used as raw material to manufacture ship Propellers with a laser-powder DED process. In the present paper, grinding waste is investigated using a dynamic image Analysis system and compared to commercial DED powder. To be able to compare the material quality and to verify DED process parameters, semi-academic sample geometries are manufactured.
Nickelbasis-Superlegierungen sind seit vielen Jahren in unterschiedlichen Industrieanwendungen im Einsatz. Aufgrund der großen Heißrissneigung ist das Schweißen dieser Werkstoffe jedoch bei einer Vielzahl von Legierungen problematisch. Neue Arbeiten auf dem Gebiet zeigen, dass entgegen den gängigen Theorien auch reduzierte Schweißgeschwindigkeiten eine Tendenz zur Verringerung der Rissneigung aufweisen. Bisher existieren jedoch kaum Erkenntnisse zum Prozessverhalten in diesem Parameterbereich. In dieser Arbeit wird daher der Einfluss der relevanten Prozessparameter beim Elektronenstrahlschweißen (EBW) auf die Nahtgestalt im Bereich geringer Vorschubgeschwindigkeiten untersucht. Auf Grundlage der gewonnenen Erkenntnisse soll ein Ansatz zum rissfreien Fügen von komplexen Nickelbasis-Superlegierung gebildet werden. Die praktische Umsetzbarkeit wird abschließend anhand einiger Probeschweißungen an einem besonders heißrissgefährdeten Werkstoff demonstriert. Um fehlerfreie Verbindungen zu ermöglichen, wurden zunächst die relevanten Parameter für die Einstellung von Nahtbreite, Einschweißtiefe, Aspektverhältnis und Nahtfläche anhand einer Versuchsreihe mit 17 Blindschweißungen auf einer 12 mm dicken Platte aus Inconel 718 bestimmt. Die genaue Beschreibung des Einflusses der als signifikant identifizierten Faktoren erfolgte über die Anwendung einer Regressions- und Varianzanalyse. Die Ergebnisse zeigen, dass die Einschweißtiefe, die Nahtbreite, das Aspektverhältnis sowie die Nahtfläche vorrangig über den Strahlstrom, die Fokuslage sowie den Vorschub beeinflusst werden können. Auf Basis der gebildeten statistischen Modelle erfolgte die Vorhersage geeigneter Parameter für eine finale Versuchsreihe. Die abschließenden Demonstratorschweißungen wurden exemplarisch an einer Nickelbasis-Gusslegierung mit besonders hohem Ausscheidungsphasenanteil durchgeführt. Hierfür wurden Schweißungen im I- Stoß an 6,5 mm und 10 mm dicken Blechen ausgeführt. Trotz der mangelnden Schweißeignung und dem hohen Anteil an Ausscheidungsphase des Werkstoffes, zeigten sich nach Optimierung der Prozessparameter keine Heißrisse mehr.
Ni-based superalloys are well established in various industrial applications, because of their excellentmechanical properties and corrosion resistance at high temperatures. Despite the high development stage anda common industrial use of these alloys, hot cracking remains a major challenge limiting the weldability ofthe materials. As commonly known, the hot cracking susceptibility during welding increases with the amountof precipitation phases. Hence, a large amount of highstrength Ni-Alloys is rated as non-weldable. A newapproach based on electron beam welding at low feed rates shows great potential for reducing the hotcracking tendency of precipitation-hardened alloys. However, geometry and properties of the weld seamdiffer significantly in comparison to the common process range for practical uses. The aim of this study is toinvestigate the influence of welding parameters on the seam geometry at low feed rates between 1 mm/s and10 mm/s. For this purpose, 25 bead on plate welds on a 12 mm thick sheet made of Inconel 718 are carriedout. First, the relevant parameters are identified by performing a screening. Then the effects discovered arefurther studied by using a central composite design. The results show a significant difference between theanalyzed weld seam geometry in comparison to the well-known appearance of electron beam welded seams.
Additive manufacturing, and therefore directed energy deposition, is
gaining more and more interest from industrial users. However, quality assurance for the components produced is still a challenge. Machine learning, especially using artificial neuronal networks, is a potential method for ensuring a high-quality standard. Based on process Parameters and monitoring data, part quality can be predicted. A further advantage is the ability to constantly learn and adopt to slight process changes.
First tests using artificial neural networks focus on the prediction of track geometry. The results show that even a small data set is enough to provide high accuracy in the predictions. In this work, an artificial neural network for the predictive analysis of relative density in laser powder cladding has been developed. A central composite experimental design is used to generate 19 data sets. Input variables are laser power, feed rate and powder mass flow. Cubes are built up where density is considered as a target value. Several neural networks are trained and evaluated with these data sets. Different topologies and initial weights are considered. The best network reaches a confidence level of around 90 % for the prediction of relative density based on the process parameters. Finally, the optimization of the generalization performance is investigated. To this purpose, methods of variation in error limit as well as cross-validation are applied. In this way, density is predictable by an artificial neural network with an accuracy of about 95 %.
Laserstrahlschweißen additiv gefertigter Bauteile: Einsetzbarkeit bestehender Bewertungsvorschriften
(2021)
Bei der additiven Fertigung im Pulverbett ist die Bauteilgröße durch den Bauraum begrenzt. Das Verbindungsschweißen additiv gefertigter Teile bietet eine Möglichkeit diese Größenbegrenzung aufzuheben. Aufgrund des spezifischen Spannungs- und Gefügezustands im additiv aufgebauten Teil ist jedoch unklar, inwiefern bestehende Bewertungsvorschriften des Verbindungsschweißens auch für Schweißnähte an additiv gefertigten Bauteilen geeignet sind. Dies wird anhand des Laserstrahlschweißens additiv gefertigter Rohrverbindungen untersucht. Die Schweißnähte werden mittels visueller Prüfung, metallografischer Untersuchungen sowie Computertomografie ausgewertet. Die festgestellten Fehlerarten sind vergleichbar zu konventionellen Bauteilen. Dies ist ein Indikator dafür, dass bestehende Bewertungsvorschriften die möglichen auftretenden Defekte auch für Schweißnähte an additiven Bauteilen abbilden.
Directed energy deposition (DED) has been in industrial use as a coating process for many years. Modern applications include the repair of existing components and additive manufacturing. The main advantages of DED are high deposition rates and low energy input. However, the process is influenced by a variety of parameters affecting the component quality. Artificial neural networks (ANNs) offer the possibility of mapping complex processes such as DED. They can serve as a tool for predicting optimal process parameters and quality characteristics. Previous research only refers to weld beads: a transferability to additively manufactured three-dimensional components has not been investigated. In the context of this work, an ANN is generated based on 86 weld beads. Quality categories (poor, medium, and good) are chosen as target variables to combine several quality features. The applicability of this categorization compared to conventional characteristics is discussed in detail. The ANN predicts the quality category of weld beads with an average accuracy of 81.5%. Two randomly generated parameter sets predicted as “good” by the network are then used to build tracks, coatings,walls, and cubes. It is shown that ANN trained with weld beads are suitable for complex parameter predictions in a limited way.
Prognose von Qualitätsmerkmalen durch Anwendung von KI-Methoden beim Directed 10 Energy Deposition
(2022)
Dieser Beitrag enthält die Ergebnisse eines im Rahmen der DVS Forschung entwickelten Ansatzes zur Qualitätssicherung im Directed Energy Deposition. Es basiert auf der Verarbeitung verschiedener während des Prozesses gesammelter Sensordaten unter Anwendung Künstlicher Neuronale Netze (KNN). So ließen sich die Qualitätsmerkmale Härte und Dichte auf der Datenbasis von 50 additiv gefertigten Probenwürfel mit einer Abweichung < 2 % vorhersagen. Des Weiteren wurde die Übertragbarkeit des KNN auf eine Schaufelgeometrie untersucht. Auch hier ließen sich Härte und Dichte hervorragend prognostizieren (Abweichung < 1,5 %), sodass der Ansatz als validiert betrachtet werden kann.
With additive manufacturing in the powder bed, the component size is limited by the installation space. Joint welding of additively manufactured parts offers a possibility to remove this size limitation. However, due to the specific stress and microstructure state in the additively built material, it is unclear to what extent existing evaluation rules of joint welding are also suitable for welds on additive components. This is investigated using laser beam welding of additively manufactured pipe joints. The welds are evaluated by means of visual inspection, metallographic examinations as well as computed tomography. The types of defects found are comparable to conventional components. This is an indicator that existing evaluation regulations also map the possible defects occurring for weld seams on additive components.
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.
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.