Additive manufacturing of metals has emerged as a potential technology for companies to create highly integrated and individualized products. In particular, powder-based laser metal deposition has advantages such as flexibility and multimaterial capabilities. It is possible to mix powders and create alloys inside the melt-pool during the build process. Consequently, purpose made material combinations with set or even varying thermal properties can be realized. Inherently, the process becomes increasingly challenging because of the great number of variables. Analyzation of the manufactured part ensures top quality and detects errors and defects. To accomplish this, specimens have to be x-rayed or ground and cut into microsections. In order to save time and keep the parts’ integrity, a new method uses temperature data from the process to determine irregularities. During the additive manufacturing process, a 680 W diode laser melts the substrate and the powder locally. The powder is composed of 42% nickel and 58% iron. A pyrometer samples the temperature of the molten pool at a spectral range from 1.45 to 1.85 μm. The recorded data are mapped onto the toolpath of the process head. A script converts the time dependent signal to spatially resolved temperature points. The feedrate and the laser status aid to synchronize the data throughout. As a result, the overlaid melt-pool temperature visualizes the process and creates a tomography for the produced part. Initial experiments show that errors and defects like porosities and cavities are identifiable inside the manufactured structure. Furthermore, correlations between the visualization and errors detected with microsections are possible. Overall, this technique is an addition to the repertoire of data visualization and quality control in additive manufacturing and can be transferred to other machines and laser processes.
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May 2020
Research Article|
May 06 2020
Defect detection in additive manufacturing via a toolpath overlaid melt-pool-temperature tomography Available to Purchase
R. Bernhard
;
R. Bernhard
1
Clausthaler Zentrum für Materialtechnik
, Agricolastraße 2, 38678 Clausthal-Zellerfeld, Germany
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P. Neef;
P. Neef
1
Clausthaler Zentrum für Materialtechnik
, Agricolastraße 2, 38678 Clausthal-Zellerfeld, Germany
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H. Wiche;
H. Wiche
1
Clausthaler Zentrum für Materialtechnik
, Agricolastraße 2, 38678 Clausthal-Zellerfeld, Germany
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C. Hoff;
C. Hoff
2
Laser Zentrum Hannover e.V.
, Hollerithallee 8, 30419 Hannover, Germany
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J. Hermsdorf;
J. Hermsdorf
2
Laser Zentrum Hannover e.V.
, Hollerithallee 8, 30419 Hannover, Germany
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S. Kaierle;
S. Kaierle
2
Laser Zentrum Hannover e.V.
, Hollerithallee 8, 30419 Hannover, Germany
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V. Wesling
V. Wesling
1
Clausthaler Zentrum für Materialtechnik
, Agricolastraße 2, 38678 Clausthal-Zellerfeld, Germany
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R. Bernhard
1
P. Neef
1
H. Wiche
1
C. Hoff
2
J. Hermsdorf
2
S. Kaierle
2
V. Wesling
1
1
Clausthaler Zentrum für Materialtechnik
, Agricolastraße 2, 38678 Clausthal-Zellerfeld, Germany
2
Laser Zentrum Hannover e.V.
, Hollerithallee 8, 30419 Hannover, Germany
®
Note: This paper is part of the Special Collection: Proceedings of the International Congress of Applications of Lasers & Electro-Optics (ICALEO 2019).
J. Laser Appl. 32, 022055 (2020)
Article history
Received:
April 01 2020
Accepted:
April 01 2020
Citation
R. Bernhard, P. Neef, H. Wiche, C. Hoff, J. Hermsdorf, S. Kaierle, V. Wesling; Defect detection in additive manufacturing via a toolpath overlaid melt-pool-temperature tomography. J. Laser Appl. 1 May 2020; 32 (2): 022055. https://doi.org/10.2351/7.0000055
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