<abstract xmlns="http://www.w3.org/1999/xhtml">

The quality of the powder layers in the 3D printing process is extremely important and directly corresponds to the quality of the structures made with this technology. Therefore, it is essential to control it. It can be made in-line with a vision system combined with image processing algorithms, which can significantly improve control of the process and help with the adjustment of powder spreading systems, especially in case of difficult-to-feed powders like magnetic ones – e.g., Fe-based metallic glass powder – Fe56.04Co13.45Nb5.5B25. In this work, two algorithms – machine learning – Support Vector Machines (SVM), deep learning – Convolutional Neural Networks (CNN) – were evaluated for their ability to detect and classify the enumerated anomalies based on powder layer images. The SVM algorithm makes it possible to efficiently and quickly analyze the powder-spreading process. CNN, however, appears to be a more promising choice for the developed application, as they alleviate the need for complex image operations.
</abstract>

The quality of the powder layers in the 3D printing process is extremely important and directly corresponds to the quality of the structures made with this technology. Therefore, it is essential to control it. It can be made in-line with a vision system combined with image processing algorithms, which can significantly improve control of the process and help with the adjustment of powder spreading systems, especially in case of difficult-to-feed powders like magnetic ones – e.g., Fe-based metallic glass powder – Fe56.04Co13.45Nb5.5B25. In this work, two algorithms – machine learning – Support Vector Machines (SVM), deep learning – Convolutional Neural Networks (CNN) – were evaluated for their ability to detect and classify the enumerated anomalies based on powder layer images. The SVM algorithm makes it possible to efficiently and quickly analyze the powder-spreading process. CNN, however, appears to be a more promising choice for the developed application, as they alleviate the need for complex image operations.

Application of machine vision for the detection of powder bed defects in additive manufacturing processes

Materials Science-Poland

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The quality of the powder layers in the 3D printing process is extremely important and directly corresponds to the quality of the structures made with this...

Input image	Processed image	Decision	Comment
		NOK	many defects
		OK	few defects
		NOK	very many defects

Application of machine vision for the detection of powder bed defects in additive manufacturing processes

Publicado en línea: 04 sept 2023

Páginas: 214 - 226

Recibido: 07 abr 2023

Aceptado: 02 jun 2023

DOI: https://doi.org/10.2478/msp-2023-0013

Palabras clave
additive manufacturing, powder deposition, image processing, vision systems

© 2023 Marcin Korzeniowski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

Fig. 6.

Fig. 7.

Fig. 8.

Fig. 9.

Fig. 10.

Example results obtained during the algorithm, with a view of the input images of the powder layer, the processed images, and the result of the classification

Application of machine vision for the detection of powder bed defects in additive manufacturing processes

Publicado en línea: 04 sept 2023

Páginas: 214 - 226

Recibido: 07 abr 2023

Aceptado: 02 jun 2023

DOI: https://doi.org/10.2478/msp-2023-0013

Palabras claveadditive manufacturing, powder deposition, image processing, vision systems

© 2023 Marcin Korzeniowski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

Fig. 6.

Fig. 7.

Fig. 8.

Fig. 9.

Fig. 10.

Example results obtained during the algorithm, with a view of the input images of the powder layer, the processed images, and the result of the classification

Palabras clave
additive manufacturing, powder deposition, image processing, vision systems