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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.
Safety-critical applications of products manufactured by laser powder bed fusion (PBF-LB/M) are still limited to date. This is mainly due to a lack of knowledge regarding the complex relationship between process, structure, and resulting properties. The assurance of homogeneity of the microstructure and homogeneity of the occurrence and distribution of defects within complexly shaped geometries is still challenging. Unexpected and unpredicted local inhomogeneities may cause catastrophic failures. The identification of material specific and machine specific process parameter windows for production of fully dense simple laboratory specimens is state of the art. However, the incorporation of changing thermal conditions that a complexly shaped component can be faced with during the manufacturing process is often neglected at the stage of a process window determination. This study demonstrates the tremendous effect of changing part temperatures on the defect occurrence for the broadly used stainless steel alloy AISI 316L. Process intrinsic variations of the surface temperature are caused by heat accumulation which was measured by use of a temperature adjusted mid-wavelength infrared (MWIR) camera. Heat accumulation was triggered by simple yet effective temporal and geometrical restrictions of heat dissipation. This was realized by a variation of inter layer times and reduced cross section areas of the specimens. Differences in surface temperature of up to 800 K were measured. A severe development of keyhole porosity resulted from these distinct intrinsic preheating temperatures, revealing a shift of the process window towards unstable melting conditions. The presented results may serve as a warning to not solely rely on process parameter optimization without considering the actual process conditions a real component is faced with during the manufacturing process. Additionally, it motivates the development of representative test specimens.
Safety-critical applications of products manufactured by laser powder bed fusion (PBF-LB/M) are still limited to date. This is mainly due to a lack of knowledge regarding the complex relationship between process, structure, and resulting properties. The assurance of homogeneity of the microstructure and homogeneity of the occurrence and distribution of defects within complexly shaped geometries is still challenging. Unexpected and unpredicted local inhomogeneities may cause catastrophic failures. The identification of material specific and machine specific process parameter windows for production of fully dense simple laboratory specimens is state of the art. However, the incorporation of changing thermal conditions that a complexly shaped component can be faced with during the manufacturing process is often neglected at the stage of a process window determination. This study demonstrates the tremendous effect of changing part temperatures on the defect occurrence for the broadly used stainless steel alloy AISI 316L. Process intrinsic variations of the surface temperature are caused by heat accumulation which was measured by use of a temperature adjusted mid-wavelength infrared (MWIR) camera. Heat accumulation was triggered by simple yet effective temporal and geometrical restrictions of heat dissipation. This was realized by a variation of inter layer times and reduced cross section areas of the specimens. Differences in surface temperature of up to 800 K were measured. A severe development of keyhole porosity resulted from these distinct intrinsic preheating temperatures, revealing a shift of the process window towards unstable melting conditions. The presented results may serve as a warning to not solely rely on process parameter optimization without considering the actual process conditions a real component is faced with during the manufacturing process. Additionally, it motivates the development of representative test specimens.
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 prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing
workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers.
Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. Themodel is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) μm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample
labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity.
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.