Details
| Original language | English |
|---|---|
| Pages (from-to) | 1585-1598 |
| Number of pages | 14 |
| Journal | International Journal of Advanced Manufacturing Technology |
| Volume | 132 |
| Issue number | 3-4 |
| Early online date | 21 Mar 2024 |
| Publication status | Published - May 2024 |
Abstract
Tools for implementing a systematic quality management are necessary for the use of material extrusion as an additive manufacturing process for products with high quality requirements. Well-defined quality classes are crucial for ensuring that the requirements for a product can be communicated transparently and that the existing properties can be evaluated. Furthermore, there is a lack of capable measurement equipment for the acquisition of process data during the production process. To address these challenges, the present paper introduces an image processing system that determines quality indicators for individual layers in terms of imperfect surface percentages and the number of imperfections. The central element of the hardware is an adaptive darkfield illumination, which leads to high-contrast images. In addition, five types of layer subareas are identified in a segmentation step. Unsupervised machine learning methods are then used to detect imperfections in each layer subarea. In the segmentation, the current layer can be distinguished from irrelevant image background regions with an F-measure of 0.981. For the layer-wise measurement of the quality indicators, relative measurement errors with standard deviations of 25 to 76.1% are found. After evaluating the capabilities of the image processing system, a proposal for limits of quality classes is derived by monitoring several material extrusion processes. For this purpose, three quality classes for each of the five layer subareas are deduced from the process scatter measured by the image processing system. The results are an important contribution to the industrialization of material extrusion in safety–critical areas such as medical technology or the aerospace industry.
Keywords
- Additive manufacturing, Fused deposition modeling, Image processing, Material extrusion, Process monitoring, Quality classes
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Software
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: International Journal of Advanced Manufacturing Technology, Vol. 132, No. 3-4, 05.2024, p. 1585-1598.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Determination of quality classes for material extrusion additive manufacturing using image processing
AU - Oleff, Alexander
AU - Küster, Benjamin
AU - Overmeyer, Ludger
N1 - Funding Information: Open Access funding enabled and organized by Projekt DEAL. Substantial parts of this work are part of the research project 20714 N of the Research Community for Quality (FQS), AugustSchanz-Str. 21A, 60433 Frankfurt/Main and have been funded by the AiF within the program for sponsorship by Industrial Joint Research (IGF) of the German Federal Ministry of Economic Afairs and Energy based on an enactment of the German Parliamen
PY - 2024/5
Y1 - 2024/5
N2 - Tools for implementing a systematic quality management are necessary for the use of material extrusion as an additive manufacturing process for products with high quality requirements. Well-defined quality classes are crucial for ensuring that the requirements for a product can be communicated transparently and that the existing properties can be evaluated. Furthermore, there is a lack of capable measurement equipment for the acquisition of process data during the production process. To address these challenges, the present paper introduces an image processing system that determines quality indicators for individual layers in terms of imperfect surface percentages and the number of imperfections. The central element of the hardware is an adaptive darkfield illumination, which leads to high-contrast images. In addition, five types of layer subareas are identified in a segmentation step. Unsupervised machine learning methods are then used to detect imperfections in each layer subarea. In the segmentation, the current layer can be distinguished from irrelevant image background regions with an F-measure of 0.981. For the layer-wise measurement of the quality indicators, relative measurement errors with standard deviations of 25 to 76.1% are found. After evaluating the capabilities of the image processing system, a proposal for limits of quality classes is derived by monitoring several material extrusion processes. For this purpose, three quality classes for each of the five layer subareas are deduced from the process scatter measured by the image processing system. The results are an important contribution to the industrialization of material extrusion in safety–critical areas such as medical technology or the aerospace industry.
AB - Tools for implementing a systematic quality management are necessary for the use of material extrusion as an additive manufacturing process for products with high quality requirements. Well-defined quality classes are crucial for ensuring that the requirements for a product can be communicated transparently and that the existing properties can be evaluated. Furthermore, there is a lack of capable measurement equipment for the acquisition of process data during the production process. To address these challenges, the present paper introduces an image processing system that determines quality indicators for individual layers in terms of imperfect surface percentages and the number of imperfections. The central element of the hardware is an adaptive darkfield illumination, which leads to high-contrast images. In addition, five types of layer subareas are identified in a segmentation step. Unsupervised machine learning methods are then used to detect imperfections in each layer subarea. In the segmentation, the current layer can be distinguished from irrelevant image background regions with an F-measure of 0.981. For the layer-wise measurement of the quality indicators, relative measurement errors with standard deviations of 25 to 76.1% are found. After evaluating the capabilities of the image processing system, a proposal for limits of quality classes is derived by monitoring several material extrusion processes. For this purpose, three quality classes for each of the five layer subareas are deduced from the process scatter measured by the image processing system. The results are an important contribution to the industrialization of material extrusion in safety–critical areas such as medical technology or the aerospace industry.
KW - Additive manufacturing
KW - Fused deposition modeling
KW - Image processing
KW - Material extrusion
KW - Process monitoring
KW - Quality classes
UR - http://www.scopus.com/inward/record.url?scp=85188153702&partnerID=8YFLogxK
U2 - 10.1007/s00170-024-13269-5
DO - 10.1007/s00170-024-13269-5
M3 - Article
AN - SCOPUS:85188153702
VL - 132
SP - 1585
EP - 1598
JO - International Journal of Advanced Manufacturing Technology
JF - International Journal of Advanced Manufacturing Technology
SN - 0268-3768
IS - 3-4
ER -