Have a personal or library account? Click to login

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

Materials Science-Poland
Volume 41 (2023): Issue 1 (March 2023)
By:
Open Access
|Sep 2023

Figures & Tables

Fig. 1.

View of the working platform with a scraper in the form of a blade: A) side view, B) top view)
View of the working platform with a scraper in the form of a blade: A) side view, B) top view)

Fig. 2.

A) Installed BVS002C camera with BAE000K illuminator, B) Camera above the working field
A) Installed BVS002C camera with BAE000K illuminator, B) Camera above the working field

Fig. 3.

A) Software configuration of the captured image and restriction of the analysis area to a circle and B) automatic recognition of the area with extracted features
A) Software configuration of the captured image and restriction of the analysis area to a circle and B) automatic recognition of the area with extracted features

Fig. 4.

Views of distributed powder using different processing variables
Views of distributed powder using different processing variables

Fig. 5.

Application of the segmentation algorithm to extract features corresponding to irregularities in the distributed powder at different scraper travel speeds: A) Input image, 40 mms/s, B) Segmented image, 40mm/s, C) Removal of the load from the scraper, input image, 100 mms/s, D) Segmented image, 100 mm/s
Application of the segmentation algorithm to extract features corresponding to irregularities in the distributed powder at different scraper travel speeds: A) Input image, 40 mms/s, B) Segmented image, 40mm/s, C) Removal of the load from the scraper, input image, 100 mms/s, D) Segmented image, 100 mm/s

Fig. 6.

Image processing algorithm used in the developed application
Image processing algorithm used in the developed application

Fig. 7.

View of the powder layer distributed in an unacceptable way and classified by algorithm as NOK. A) Input image with limited region of interest, B) output image
View of the powder layer distributed in an unacceptable way and classified by algorithm as NOK. A) Input image with limited region of interest, B) output image

Fig. 8.

View of the powder layer speeded acceptably and classified by algorithm as OK. A) Input image with limited region of interest, and B) output image
View of the powder layer speeded acceptably and classified by algorithm as OK. A) Input image with limited region of interest, and B) output image

Fig. 9.

Image classification algorithm used in the study
Image classification algorithm used in the study

Fig. 10.

A),B) View of the powder layer distributed in an unacceptable manner with the anomaly maps and C) powder distributed acceptably
A),B) View of the powder layer distributed in an unacceptable manner with the anomaly maps and C) powder distributed acceptably

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

Input imageProcessed imageDecisionComment
NOKmany defects
OKfew defects
NOKvery many defects
DOI: https://doi.org/10.2478/msp-2023-0013 | Journal eISSN: 2083-134X | Journal ISSN: 2083-1331
Journal RSS Feed
Language: English
Page range: 214 - 226
Submitted on: Apr 7, 2023
Accepted on: Jun 2, 2023
Published on: Sep 4, 2023
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Keywords:
additive manufacturing,
powder deposition,
image processing,
vision systems
Related subjects:
Materials sciences,
Materials sciences, other,
Nanomaterials,
Functional and smart materials,
Materials characterization and properties

© 2023 Marcin Korzeniowski, Aleksandra Małachowska, Marta Wiatrzyk, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Previous articleVolume 41 (2023): Issue 1 (March 2023)