Additive Fertigung
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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.
In this paper shortwave infrared (SWIR) thermographic measurements of the manufacturing of thin single-line walls via laser metal deposition (LMD) are presented. As the thermographic camera is mounted fixed to the welding arm, an acceleration sensor was used to assist in reconstructing the spatial position from the predefined welding path. Hereby we could obtain data sets containing the size of the molten pool and the oxide covered areas as functions of the position in the workpiece. Furthermore, the influence of the acquisition wavelength onto the thermograms was investigated in a spectral range from 1250 nm to 1550 nm. All wavelengths turned out to be usable for the in-situ process monitoring of the LMD process. The longer wavelengths are shown to be beneficial for the lower temperature range, while shorter wavelengths show more details within the molten pool.
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.
Active thermography is a fast, contactless and non-destructive technique that can be used to detect internal defects in different types of material. Volumetric irregularities such as the presence of pores in materials produced by the Additive Manufacturing processes can strongly affect the thermophysical and the mechanical properties of the final component.
In this work, an experimental investigation aimed at detecting different pores in a sample made of stainless AISI 316L produced by Laser Powder Bed Fusion (L-PBF) was carried out using pulsed thermography in reflection mode. The capability of the technique and the adopted setups in terms of geometrical and thermal resolution, acquisition frequency and energy Density of the heating source were assessed to discern two contiguous pores as well as to detect a single pore. Moreover, a quantitative indication about the minimum resolvable pore size among the available and analysed defects was provided. A powerful tool to assess the Limits and the opportunities of the pulsed technique in terms of detectability and localizability was provided by comparing active thermography results to Computed Tomography as well as a related Finite Element Analysis (FEA) to simulate the pulsed heating transfer with Comsol.
Thermography is one on the most promising techniques for in-situ monitoring of metal additive manufacturing processes. Especially in laser powder bed fusion 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 an almost defect free cuboid specimen.
Additive manufacturing (AM) offers a range of novel applications. However, the manufacturing process is complex and the production of defect-free parts with high reliability and durability is still a challenge. Thermography is a valuable tool for process surveillance, especially in metal AM 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-IR camera with those from a visual spectrum high-speed camera with band pass filter in the near-IR range.