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The capability to produce complexly and individually shaped metallic parts is one of the main advantages of the laser powder bed fusion (PBF LB/M) process. Development of material and machine specific process parameters is commonly based on results acquired from small cubic test coupons of about 10 mm edge length. Such cubes are usually used to conduct an optimization of process parameters to produce dense material. The parameters are then taken as the basis for the manufacturing of real part geometries. However, complex geometries go along with complex thermal histories during the manufacturing process, which can significantly differ from thermal conditions prevalent during the production of simply shaped test coupons. This may lead to unexpected and unpredicted local inhomogeneities of the microstructure and defect distribution in the final part and it is a root cause of reservations against the use of additive manufacturing for the production of safety relevant parts. In this study, the influence of changing thermal conditions on the resulting melt pool depth of 316L stainless steel specimens is demonstrated. A variation of thermo-graphically measured intrinsic preheating temperatures was triggered by an alteration of inter layer times and a variation of cross section areas of specimens for three distinct sets of process parameters. Correlations between the preheating temperature, the melt pool depth, and occurring defects were analyzed. The limited expressiveness of the results of small density cubes is revealed throughout the systematic investigation. Finally, a clear recommendation to consider thermal conditions in future process parameter optimizations is given.
The capability to produce complexly and individually shaped metallic parts is one of the main advantages of the laser powder bed fusion (PBF LB/M) process. Development of material and machine specific process parameters is commonly based on results acquired from small cubic test coupons of about 10 mm edge length. Such cubes are usually used to conduct an optimization of process parameters to produce dense material. The parameters are then taken as the basis for the manufacturing of real part geometries. However, complex geometries go along with complex thermal histories during the manufacturing process, which can significantly differ from thermal conditions prevalent during the production of simply shaped test coupons. This may lead to unexpected and unpredicted local inhomogeneities of the microstructure and defect distribution in the final part and it is a root cause of reservations against the use of additive manufacturing for the production of safety relevant parts. In this study, the influence of changing thermal conditions on the resulting melt pool depth of 316L stainless steel specimens is demonstrated. A variation of thermo-graphically measured intrinsic preheating temperatures was triggered by an alteration of inter layer times and a variation of cross section areas of specimens for three distinct sets of process parameters. Correlations between the preheating temperature, the melt pool depth, and occurring defects were analyzed. The limited expressiveness of the results of small density cubes is revealed throughout the systematic investigation. Finally, a clear recommendation to consider thermal conditions in future process parameter optimizations is given.
For the wide acceptance of the use of additive manufacturing (AM), it is required to provide reliable testing methods to ensure the safety of the additively manufactured parts. A possible solution could be the deployment of in-situ monitoring during the build process. However, for laser powder bed fusion using metal powders (PBF-LB/M) only a few in-situ monitoring techniques are commercially available (optical tomography, melt pool monitoring), which have not been researched to an extent that allows to guarantee the adherence to strict quality and safety standards.
In this contribution, we present results of a study of PBF-LB/M printed parts made of the nickel-based superalloy Haynes 282. The formation of defects was provoked by local variations of the process parameters and monitored by thermography, optical tomography and melt pool monitoring. Afterwards, the defects were characterized by computed tomography (CT) to identify the detection limits of the used in-situ techniques.
For the wide acceptance of the use of additive manufacturing (AM), it is required to provide reliable testing methods to ensure the safety of the additively manufactured parts. A possible solution could be the deployment of in-situ monitoring during the build process. However, for laser powder bed fusion using metal powders (PBF-LB/M ) only a few in-situ monitoring techniques are commercially available (optical tomography, melt pool monitoring) but not researched to an extent that allows to guarantee the adherence to strict quality and safety standards.
In this contribution, we present results of a study of PBF-LB/M printed parts made of the nickel-based superalloy Haynes 282. The formation of defects was provoked by local variations of the process parameters and monitored by thermography, optical tomography and melt pool monitoring. Afterwards, the defects were characterized by computed tomography (CT) to identify the detection limits of the used in-situ techniques.
The appearance of irregularities such as keyhole porosity is a major challenge for the production of metal parts by laser powder bed fusion (PBF-LB/M). The utilization of thermographic in-situ monitoring is a promising approach to extract the thermal history which is closely related to the formation of irregularities. In this study, we investigate the utilization of convolutional neural networks to predict keyhole porosity based on thermographic features. Here, the porosity information calculated from an x-ray micro computed tomography scan is used as reference. Feature engineering is performed to enable the model to learn the complex physical characteristics of the porosity formation. The model is examined with regard to the choice of hyperparameters, the significance of thermal features and characteristics of the data acquisition. Based on the results, future demands on irregularity prediction in PBF-LB/M are derived.
The formation of defects such as keyhole pores is a major challenge for the production of metal parts by Laser Powder Bed Fusion (LPBF). The LPBF process is characterized by a large number of influencing factors which can be hard to quantify. Machine Learning (ML) is a prominent tool to predict the outcome of complex processes on the basis of different sensor data. In this study, a ML model for defect prediction is created using thermographic image features as input data. As a reference, the porosity information calculated from an x-ray Micro Computed Tomography (µCT) scan of the produced specimen is used. Physical knowledge about the keyhole pore formation is incorporated into the model to increase the prediction accuracy. From the prediction result, the quality of the input data is evaluated and future demands on in-situ monitoring of LPBF processes are formulated.
In-situ Prozessüberwachung in der additiven Fertigung von Metallen (PBF-LB /M) mittels TT und ET
(2022)
Durch die additive Fertigung ergeben sich durch die nun mögliche wirtschaftliche Fertigung hochgradig individueller und komplexer metallischer Bauteile in kleinen Stückzahlen bis hinunter zum Einzelstück für viele Industriebereiche ganz neue Möglichkeiten.
Gleichzeitig entstehen jedoch neue Herausforderungen im Bereich der Qualitätssicherung, da sich auf statistischen Methoden beruhende Ansätze nicht anwenden lassen, ohne wiederum die Vorteile der Fertigung massiv einzuschränken.
Eine mögliche Lösung für dieses Problem liegt in der Anwendung verschiedener In-situ-Überwachungstechniken während des Bauprozesses. Jedoch sind nur wenige dieser Techniken kommerziell verfügbar und noch nicht so weit erforscht, dass die Einhaltung strenger Qualitäts- und Sicherheitsstandards gewährleistet werden kann. In diesem Beitrag stellen wir die Ergebnisse einer Studie über mittels L-PBF gefertigte Probekörper aus der Nickelbasis-Superlegierung Haynes 282 vor, bei denen die Bildung von Defekten durch lokale Variationen der Prozessparameter wie der Laserleistung provoziert wurde. Die Proben wurden in-situ mittels Thermographie, optischer Tomographie, Schmelzbadüberwachung und Wirbelstromprüfung sowie ex-situ mittels Computertomographie (CT) überwacht, mit dem Ziel, die Machbarkeit und die Aussichten der einzelnen Methoden für die zuverlässige Erkennung der Bildung relevanter Defekte zu bewerten.
Avoiding the formation of defects such as keyhole pores is a major challenge for the production of metal parts by Laser Powder Bed Fusion (LPBF). The use of in-situ monitoring by thermographic cameras is a promising approach to detect defects, however the data is hard to analyze by conventional algorithms. Therefore, we investigate the use of Machine Learning (ML) in this study, as it is a suitable tool to model complex processes with many influencing factors. A ML model for defect prediction is created based on features extracted from process thermograms. The porosity information calculated from an x-ray Micro Computed Tomography (µCT) scan is used as reference. Physical characteristics of the keyhole pore formation are incorporated into the model to increase the prediction accuracy. Based on the prediction result, the quality of the input data is inferred and future demands on in-situ monitoring of LPBF processes are derived.
Infrared thermography is a technique that allows to measure the temperatures of objects by analyzing the intensity of the thermal emission without the need of direct contact with very high spatial and temporal resolution. As the temperature is a fundamental factor for the additive manufacturing processes of metals, infrared thermography can provide experimental data that can be used for the validation of simulations and improving the understanding of the processes as well as for in-situ process monitoring for nondestructive evaluation (NDE) for quality control.
In this talk we will provide an overview over the possibilities of state of the art thermographic in-situ monitoring systems for the DED-LB/M and PBF-LB/M processes and the challenges such as phase transitions and unknown emissivity values in respect to the determination of real temperatures. We define the requirements for different camera systems in various configurations and give examples on the selection of appropriate measurement parameters and data acquisition techniques as well as on techniques for data analysis and interpretation. Finally, we compare in-situ monitoring methods against post NDE methods by analyzing the advantages and disadvantages of both.
This research was funded by BAM within the Focus Area Materials.
Im Rahmen des Themenfeldprojektes „Process Monitoring of AM“ (ProMoAM) evaluiert die Bundesanstalt für Materialforschung und -Prüfung (BAM) gegenwärtig die Anwendbarkeit verschiedenster ZfP-Verfahren zur in-situ Prozessüberwachung in der additiven Fertigung (AM) von Metallen in Hinblick auf die Qualitätssicherung.
Einige der wichtigsten Messgrößen sind hierbei die Temperatur des Schmelzbades und die Abkühlrate, welche starken Einfluss auf das Gefüge und die Eigenspannung haben. Aufgrund der Zugänglichkeit zum Werkstück während des Bauprozesses bieten sich optische Verfahren zu Temperaturbestimmung an. Hierbei stellen jedoch u. a. die hohe Bandbreite der zu messenden Temperaturen, die Bestimmung der Emissivität und ihre Änderung bei Phasenübergängen der verwendeten Legierung große experimentelle Herausforderungen dar. Eine weitere Herausforderung stellt für die IR-Spektroskopie die Absorption durch das Schutzgas und weitere optische Elemente dar. Um diese auch in einem industriellen Umfeld kompensieren zu können, wurde eine Methode entwickelt, die das gemessene Spektrum bei der Verfestigung des Werkstoffes als Referenz nutzt. In diesem Beitrag wird die Anwendung dieser Methode für die IR-Spektrometrie als auch Thermografische Messungen beim Laser-Pulver-Auftragschweißen von 316L gezeigt, wobei beide Methoden weiterhin in Hinblick auf ihre individuellen Vor- und
Nachteile miteinander verglichen werden.