TY - JOUR

T1 - Defect detection in additive manufacturing via a toolpath overlaid melt-pool-temperature tomography

AU - Bernhard, R.

AU - Neef, P.

AU - Wiche, H.

AU - Hoff, C.

AU - Hermsdorf, J.

AU - Kaierle, S.

AU - Wesling, V.

N1 - Funding Information: The authors acknowledge the Ministry for Science and Culture of Lower Saxony and the European Regional Development Fund (ERDF) for the funding and support [duration of implementation: 1 July 2018–30 June 2021; Project No. ZW6-8501 8048 (wGROTESK)].

PY - 2020/5/1

Y1 - 2020/5/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85110295018&partnerID=8YFLogxK

U2 - 10.2351/7.0000055

DO - 10.2351/7.0000055

M3 - Article

AN - SCOPUS:85110295018

VL - 32

JO - Journal of laser applications

JF - Journal of laser applications

SN - 1042-346X

IS - 2

M1 - 022055

ER -