Ingenieurwissenschaften und zugeordnete Tätigkeiten
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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.
Joining of dissimilar material has become highly popular research subject in the automobile industry due to the reduced weight and thereby increasing the fuel efficiency. Infrared thermography can be used as a natural tool to measure the temperature near the welding region and correlate the distribution of temperature to the weld quality. In the present work the quality of the dissimilar welded sample is identified using the temperature distribution in the vicinity of the weld pool region. A numerical model for CMT continues welding process has been modeled and simulated for the first time and compared with the experimental measurement.
In this study, the feasibility of using non-contact Infrared thermography as a potential tool to monitor the CMT welding process is explored. The presence of internal defects such as porosity, lack of filler material deposition and formation of improper weld bead produce perturbations in the surface temperature which can be identified using an Infrared thermography technique. We present recent results obtained from online monitoring of the the dissimilar joining using CMT weld brazing of Aluminum and Steel using a transmission mode measurement approach. The effect of loss of zinc coating on the weldability of the cold metal transfer joining of aluminum to galvanised steel was investigated. A correlation between measured online thermal indications with the weld anomalies is successfully attempted and the results are compared with the conventional post-weld NDT inspection methods.
Online (passive) thermographic inspection of overlap joints of aluminium and zinc coated steel sheets made by cold metal Transfer weld brazing process was explored. Different experimental Trials were conducted for demonstrating the feasibility of thermographic inspection to detect the porosities, improper weld bead and to differentiate the pre weld temperature. The whole process was monitored using infrared cameras in different wavelength region.
Image analysis algorithms were developed to reconstruct the thermal images that contain the signatures of the weld defects and to extract the pre weld temperature and ist evolution with distance from the centre of the weld torch. Post-weld radiography lends strong Support to the observations.