Additive Fertigung
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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 of metallic components (PBF-LB/M) is gaining acceptance in industry. However, the high costs and lengthy qualification processes required for printed components create the need for more effective in-situ monitoring and testing methods. This article proposes multispectral Optical Tomography (OT) as a new approach for monitoring the PBF-LB/M process. Compared to other methods, OT is a low-cost process monitoring method that uses long-time exposure imaging to observe the build process. However, it lacks time resolution compared to expensive thermographic sensor systems. Monochromatic OT (1C-OT) is already commercially available and observes the building process layer-wise using a single wavelength window in the NIR range. Multispectral OT (nC-OT) utilizes a similar setup but can measure multiple wavelength ranges per location simultaneously. By comparing the classical 1C-OT and nC-OT approaches, this article examines the advantages of nC-OT (two channel OT and RGB-OT) in reducing the false positive rate for process deviations and approximating maximum temperatures for a better comparison between different build processes and materials. This could ultimately reduce costs and time for part qualification. The main goal of this contribution is to assess the advantages of nC-OT compared to 1C-OT for in-situ process monitoring of PBF-LB/M.
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.