Ingenieurwissenschaften und zugeordnete Tätigkeiten
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In this study, we present an enhanced deep learning framework for the prediction of porosity based on thermographic in-situ monitoring data of laser powder bed fusion processes. The manufacturing of two cuboid specimens from Haynes 282 (Ni-based alloy) powder was monitored by a short-wave infrared camera. We use thermogram feature data and x-ray computed tomography data to train a convolutional neural network classifier. The classifier is used to perform a multi-class prediction of the spatially resolved porosity level in small sub-volumes of the specimen bulk.
Laser Powder Bed Fusion (L-PBF), as one of the most promising production process in the field of metal additive manufacturing, enables traditional constructive solutions to be rethought and the manufacturing of optimized components according to the "form follows function" principle. The most significant obstacle for a broad industrial application of the L-PBF process is the inadequate quality assurance during the manufacturing process so far, leading to high production costs. Although several mainly camera based commercial in-process monitoring systems are already available, a deep understanding of the interpretation of the monitored data and correlation with actual defects is still lacking. One reason for this is the reduction of the complex process signature to just one measurement value.
The focus of this contribution is the presentation of the multispectral optical tomography as alternative to single measurand in-situ monitoring systems. The potential of this approach is hereby shown on L-PBF printed samples with induced process instabilities. Beyond that, an in-house developed L-PBF printer for further testing of multi-sensor in-situ monitoring systems is presented.
Additive manufacturing processes are increasingly being used in industrial applications. Especially powder bed fusion processes are of high interest due to their capability to economically produce individual, highly complex and functionally integrated components in small batches.
However, the quality assurance of these components remains a challenge. Internal defects and undesirable microstructures and surface conditions can deteriorate the mechanical properties. Especially for use in safety-relevant applications, new design and inspection concepts are needed that take these factors into account.
This talk presents typical defects and microstructure phenomena resulting from the laser powder bed fusion process and identifies challenges and opportunities for non-destructive testing from a manufacturing engineering perspective. In particular, the possibility of a process-integrated quality control is shown based on current research results.
Laser powder bed fusion is used to create near net shape metal parts with a high degree of freedom in geometry design. When it comes to the production of safety critical components, a strict quality assurance is mandatory. An alternative to cost-intensive non-destructive testing of the produced parts is the utilization of in-situ process monitoring techniques. The formation of defects is linked to deviations of the local thermal history of the part from standard conditions. Therefore, one of the most promising monitoring techniques in additive manufacturing is thermography. In this study, features extracted from thermographic data are utilized to investigate the thermal history of cylindrical metal parts. The influence of process parameters, part geometry and scan strategy on the local heat distribution and on the resulting part porosity are presented. The suitability of the extracted features for in-situ process monitoring is discussed.
The quality of additively manufactured components is significantly influenced by the process parameters used during production. Thus, sensors and measuring systems are already commercially available for process monitoring, at least in metal-based additive manufacturing. However, it is not yet possible to detect defects and inhomogeneities directly or indirectly during the building process. The aim of the project ProMoAM is to develop spectroscopic and non-destructive testing methods for the in-situ evaluation of the quality of additively manufactured metal components in laser- or arc-based AM processes. In addition to passive and active methods of thermography, this includes optical tomography, optical emission spectroscopy, eddy current testing, laminography (radiography), X-ray backscattering, particle emission spectroscopy and photoacoustic methods.