In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography

Mohr, Gunther; Altenburg, Simon J.; Ulbricht, Alexander; Heinrich, Philipp; Baum, Daniel; Maierhofer, Christiane; Hilgenberg, Kai

doi:10.3390/met10010103

Cited by 112 publications

(59 citation statements)

References 28 publications

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“…This error diminishes once the spatial temperature gradients have decreased due to lateral heat flow. These limitations are discussed in more detail by Mohr et al [20]. Despite these limitations, qualitative analysis of the thermography data revealed results that contribute to the understanding of the presented RS results.…”

Section: Thermographymentioning

confidence: 88%

Separation of the Formation Mechanisms of Residual Stresses in LPBF 316L

Ulbricht

Altenburg

Sprengel

et al. 2020

Metals

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Rapid cooling rates and steep temperature gradients are characteristic of additively manufactured parts and important factors for the residual stress formation. This study examined the influence of heat accumulation on the distribution of residual stress in two prisms produced by Laser Powder Bed Fusion (LPBF) of austenitic stainless steel 316L. The layers of the prisms were exposed using two different border fill scan strategies: one scanned from the centre to the perimeter and the other from the perimeter to the centre. The goal was to reveal the effect of different heat inputs on samples featuring the same solidification shrinkage. Residual stress was characterised in one plane perpendicular to the building direction at the mid height using Neutron and Lab X-ray diffraction. Thermography data obtained during the build process were analysed in order to correlate the cooling rates and apparent surface temperatures with the residual stress results. Optical microscopy and micro computed tomography were used to correlate defect populations with the residual stress distribution. The two scanning strategies led to residual stress distributions that were typical for additively manufactured components: compressive stresses in the bulk and tensile stresses at the surface. However, due to the different heat accumulation, the maximum residual stress levels differed. We concluded that solidification shrinkage plays a major role in determining the shape of the residual stress distribution, while the temperature gradient mechanism appears to determine the magnitude of peak residual stresses.

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Section: Thermographymentioning

confidence: 88%

Separation of the Formation Mechanisms of Residual Stresses in LPBF 316L

Ulbricht

Altenburg

Sprengel

et al. 2020

Metals

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“…Among the approaches of thermal condition monitoring, contactless measurement techniques are most common. Passive infrared (IR) thermography is a technology which was used by several groups of authors for thermal in situ process monitoring means [7,[9][10][11][12][13][14]. IR thermography can acquire data of thermal emissions of the process layer by layer with variations in spatial and temporal resolutions, depending on the particular equipment and setup [12].…”

Section: Thermal Monitoring In L-pbfmentioning

confidence: 99%

Experimental Determination of the Emissivity of Powder Layers and Bulk Material in Laser Powder Bed Fusion Using Infrared Thermography and Thermocouples

Mohr

Nowakowski

Altenburg

et al. 2020

Metals

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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.

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“…For example, Zenzinger et al [ 35 ] demonstrated the link between the hotspots in OT images to the defect in the μCT scan of the layer. Recently, Mohr et al [ 38 ] also studied the OT images to detect a defect in the final part and compared the OT images with the μCT images. It was also verified that the hotspots in the OT images link to defect in the final part.…”

Section: Methodsmentioning

confidence: 99%

Inline Drift Detection Using Monitoring Systems and Machine Learning in Selective Laser Melting

et al. 2020

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Direct metal laser sintering, an additive manufacturing technique, has a huge growing demand in industries like aerospace, biomedical, and tooling sector due to its capability to manufacture complex parts with ease. Despite many technological advancements, the reliability and repeatability of the process are still an issue. Therefore, there is a demand for inline automatic fault detection and postprocessing tools to analyze the acquired in situ monitoring data aiming to provide better-quality assurance to the user. Herein, the treatment of the data obtained using the EOSTATE optical tomography monitoring system is focused. A balanced dataset is obtained with the help of computer tomography of the certified part (Stainless Steel CX cylindrical samples), through which a feature matrix is prepared, and the layers of the part are classified either having "Drift" or "No-drift." The model is trained with the feature matrix and tested on benchmark parts (Maraging Steel) and on an industrial part (knuckle, automotive part) manufactured in AlSi10Mg. The proposed semisupervised approach shows promising results for presented case studies. Thus, the semisupervised machine learning approach, if adopted, could prove to be a cost effective and fast approach to postprocess the in situ monitoring data with much ease.

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In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography

Cited by 112 publications

References 28 publications

Separation of the Formation Mechanisms of Residual Stresses in LPBF 316L

Separation of the Formation Mechanisms of Residual Stresses in LPBF 316L

Experimental Determination of the Emissivity of Powder Layers and Bulk Material in Laser Powder Bed Fusion Using Infrared Thermography and Thermocouples

Inline Drift Detection Using Monitoring Systems and Machine Learning in Selective Laser Melting

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