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The project ProMoAM is presented. The goal of the project is to evaluate which NDT techniques or combination of techniques is suited for in-situ quality assurance in additive manufacturing of metals. To this end, also 3d-data fusion and visualization techniques are applied. Additional ex-situ NDT-techniques are used as references for defect detection and quantification. Feasability studies for NDT-techniques that are presently not applicable for in-situ use are performed as well.
The presentation gives a brief overview of the whole project and the different involved NDT-techniques.
For metal-based additive manufacturing, sensors and measuring systems for monitoring of the energy source, the build volume, the melt pool and the component geometry are already commercially available. Further methods of optics, spectroscopy and non-destructive testing are described in the literature as suitable for in-situ application, but there are only a few reports on practical implementations.
Therefore, a new BAM project aims to develop process monitoring methods for the in-situ evaluation of the quality of additively manufactured metal components. In addition to passive and active thermography, this includes optical tomography, optical emission and absorption spectroscopy, eddy current testing, laminography, X-ray backscattering and photoacoustic methods. These methods are used in additive manufacturing systems for selective laser melting, laser metal deposition and wire arc additive manufacturing. To handle the sometimes huge amounts of data, algorithms for efficient preprocessing are developed and characteristics of the in-situ data are extracted and correlated to defects and inhomogeneities, which are determined using reference methods such as computer tomography and metallography. This process monitoring and fusion of data of different measurement techniques should result in a significant reduction of costly and time-consuming, destructive or non-destructive tests after the production of the component and at the same time reduce the production of scrap.
Here, first results of simultaneous measurements of optical emission spectroscopy and thermography during the laser metal deposition process using 316L as building material are presented. Temperature values are extracted from spectroscopic data by fitting of blackbody emission spectra to the experimental data and compared with results from a thermographic camera. Measurements with and without powder flow reveal significant differences between welding at a pristine metal surface and previously melted positions on the build plate, illustrating the significant influence of the partial oxidation of the surface during the first welding process on subsequent welding. The measurement equipment can either be mounted stationary or following the laser path. While first results were obtained in the stationary mode, future applications for online monitoring of the build of whole parts in the mobile mode are planned.
This research was funded by BAM within the focus area Material.
Laser powder bed fusion (L-PBF) is one of the most promising additive manufacturing (AM) technologies for the production of complex metallic real part components. Due to the multitude of factors influencing process conditions and part quality and due to the layer-wise characteristic of the process, monitoring of process signatures seems to be mandatory in case of the production of safety critical components. Here, the iterative process nature enables unique access for in-situ monitoring during part manufacture. In this talk, the successful test of the synchronous use of a high-frequency infrared camera and a camera for long time exposure, working in the visible spectrum (VIS) and equipped with a near infrared filter (NIR), will be introduced as a machine manufacturer independent thermal detection monitoring set-up. Thereby, the synchronous use of an infrared camera and a VIS NIR camera combines the advantages of high framerate and high spatial resolution. The manufacture of a 316L stainless steel specimen, containing purposely seeded defects and volumes with forced changes of energy inputs, was monitored during the build. The measured thermal responses are analysed and compared with a defect mapping obtained by micro X-ray computed tomography (CT).
The first results regarding methods for data analysis, derived correlations between measured signals and detected defects as well as sources of possible data misinterpretation are presented in this talk.
Additive manufacturing offers a range of novel applications. However, the manufacturing process is complex and the production of almost defect-free parts with high reliability and durability is still a challenge. Thermography is a valuable tool for process surveillance, especially in metal additive manufacturing processes. The high process temperatures allow one to use cameras usually operating in the visible spectral range. Here, we compare the results of measurements during the manufacturing process of a commercial laser metal deposition setup using a mid wavelength infrared camera with those from a short wavelength infrared camera and those from a visual spectrum high-speed camera with band pass filter in the near infrared range.
Recording the temperature distribution of the layer under construction during laser powder bed fusion (L-PBF) is of utmost interest for a deep process understanding as well as for quality assurance and in situ monitoring means. While having a notable number of thermal monitoring approaches in additive manufacturing (AM), attempts at temperature calibration and emissivity determination are relatively rare. This study aims for the experimental temperature adjustment of an off-axis infrared (IR) thermography setup used for in situ thermal data acquisition in L-PBF processes. The temperature adjustment was conducted by means of the so-called contact method using thermocouples at two different surface conditions and two different materials: AISI 316L L-PBF bulk surface, AISI 316L powder surface, and IN718 powder surface. The apparent emissivity values for the particular setup were determined. For the first time, also corrected, closer to real emissivity values of the bulk or powder surface condition are published. In the temperature region from approximately 150 °C to 580 °C, the corrected emissivity was determined in a range from 0.2 to 0.25 for a 316L L-PBF bulk surface, in a range from 0.37 to 0.45 for 316L powder layer, and in a range from 0.37 to 0.4 for IN718 powder layer.
Thermography is one on the most promising techniques for in-situ monitoring for metal additive manufacturing processes. The high process dynamics and the strong focus of the laser beam cause a very complex thermal history within the produced specimens, such as multiple heating cycles within single layer expositions. This complicates data interpretation, e.g., in terms of cooling rates. A quantity that is easily calculated is the time a specific area of the specimen is at a temperature above a chosen threshold value (TOT). Here, we discuss variations occurring in time-over-threshold-maps during manufacturing of a defect free cuboid specimen.
Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data Integration are presented.
Due to the rapid thermal cycles involved in additive manufacturing of metals, high internal stresses and peculiar microstructures occur, which influence the parts mechanical properties. To systematically examine their formation, in-process measurements of the temperature are needed. Since the part emissivity is strongly inhomogeneous and rapidly changing in the process, the applicability of thermography for the determination of thermodynamic temperatures is limited. Measuring the thermal radiation in different wavelengths simultaneously, temperature and emissivity can be separated. Here, we present results of a preliminary study using multispectral thermography to obtain real temperatures and emissivities in directed energy deposition (DED) processes.
Due to the rapid thermal cycles involved in additive manufacturing of metals, high internal stresses and peculiar microstructures occur, which influence the parts mechanical properties. To systematically examine their formation, in-process measurements of the temperature are needed. Since the part emissivity is strongly inhomogeneous and rapidly changing in the process, the applicability of thermography for the determination of thermodynamic temperatures is limited. Measuring the thermal radiation in different wavelengths simultaneously, temperature and emissivity can be separated. Here, we present results of a preliminary study using multispectral thermography to obtain real temperatures and emissivities in directed energy deposition (DED) processes.
Introduction to ProMoAM
(2021)
Das pulverbettbasierte selektive Laserstrahlschmelzen (engl. laser powder bed fusion, L-PBF) ist die am weitesten verbreitete additive Fertigungstechnologie für die Herstellung metallischer Komponenten. Unter der Vielzahl an Einflussfaktoren auf die Qualität und die mechanischen Eigenschaften von L PBF-Bauteilen hat die Zwischenlagenzeit (engl. inter layer time, ILT) bisher kaum Beachtung in der wissenschaftlichen Literatur gefunden, obwohl sie je nach Bauraumausnutzungsgrad stark variieren kann. In diesem Vortrag werden Ergebnisse einer Studie präsentiert, die den Einfluss der ILT in Kombination mit der Bauteilhöhe und unter Berücksichtigung verschiedener Volumenenergiedichten am Beispiel der austenitischen Stahllegierung AISI 316L untersucht. Die Fertigungsprozesse wurden in situ mittels Thermographiekamera überwacht, sodass Informationen über das Abkühlverhalten der Bauteile während des Prozesses gewonnen werden konnten. Es wurden signifikante Effekte der ILT und der Bauteilhöhe auf Wärmeakkumulation, Mikrostruktur, Schmelzbadgeometrie und Härte festgestellt. Ferner konnte ein Anstieg von Defektdichten bei einem gegenseitigen Wechselspiel aus Bauteilhöhe und ILT aufgezeigt werden. Die Zwischenlagenzeit wurde somit als kritischer Faktor für die L-PBF-Fertigung von Realbauteilen identifiziert.
Additive manufacturing of metals offers the opportunity to build parts with a high degree of complexity without additional costs, opening a new space for design optimization. However, the processes are highly complex and due to the rapid thermal cycles involved, high internal stresses and peculiar microstructures occur, which influence the parts mechanical properties. To systematically examine the formation of internal stresses and the microstructure, in-process spatially resolved measurements of the part temperature are needed. If the emissivity of the inspected part is known, its thermodynamic temperature can be reconstructed by a suited radiometric model. However, in additive manufacturing of metals, the emissivity of the part surface is strongly inhomogeneous and rapidly changing due to variations of, e.g., the degree of oxidation, the material state and temperature. Thus, here, the applicability of thermography in the determination of thermodynamic temperatures is limited. However, measuring the process thermal radiation at different wavelengths simultaneously enables one to separate temperature and emissivity spatially resolved to obtain further insight into the process. Here, we present results of an initial study using multispectral thermography to obtain real temperatures and emissivities in the powderfree LMD process.
Additive manufacturing of metals gains increasing relevance in the industrial field for part production. However, especially for safety relevant applications, a suitable quality assurance is needed. A time and cost efficient route to achieve this goal is in-situ monitoring of the build process. Here, the BAM project ProMoAM (Process monitoring in additive manufacturing) is briefly introduced and recent advances of BAM in the field of in-situ monitoring of the L-PBF and the LMD process using thermography are presented.
At the Workshop "Neutron and Synchrotron Monitoring in Aerospace Advanced Manufacturing" at the Institute of Materials Physics in Space, German Aerospace Center (DLR) in Cologne, we presented on the opportunities and our experiences of using a powder based additive manufacturing process for in-space manufacturing applications in microgravity.
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.
The detection of internal irregularities is crucial for quality assessment in metal-based additive manufacturing (AM) technologies such as laser powder bed fusion (L-PBF). The utilization of in-process thermography as an in situ monitoring tool in combination with post-process X-ray micro computed tomography (XCT) as a reference technique has shown great potential for this aim. Due to the small irregularity dimensions, a precise registration of the datasets is necessary as a
requirement for correlation. In this study, the registration of thermography and XCT reference datasets of a cylindric specimen containing keyhole pores is carried out for the development of a porosity prediction model. The considered datasets show variations in shape, data type and dimensionality, especially due to shrinkage and material elevation effects present in the manufactured part. Since the resulting deformations are challenging for registration, a novel preprocessing methodology is introduced that involves an adaptive volume adjustment algorithm which is based on the porosity distribution in the specimen. Thus, the implementation of a simple three-dimensional image-to-image registration is enabled. The results demonstrate the influence of the part deformation on the resulting porosity location and the importance of registration in terms of irregularity prediction.
Results of the project ProMoAM (Process monitoring in additive manufacturing) presented. Results from in-situ eddy current testing, optical emission spectroscopy, thermography, optical tomography as well as particle and gas emission spectroscopy are summarized and correlated to results from computed tomography for future in-situ defect detection.