---
_id: '52816'
abstract:
- lang: eng
text: "Manufacturing companies face the challenge of reaching required quality standards.
Using\r\noptical sensors and deep learning might help. However, training deep
learning algorithms\r\nrequire large amounts of visual training data. Using domain
randomization to generate synthetic\r\nimage data can alleviate this bottleneck.
This paper presents the application of synthetic\r\nimage training data for optical
quality inspections using visual sensor technology. The results\r\nshow synthetically
generated training data are appropriate for visual quality inspections."
author:
- first_name: Iris
full_name: Gräßler, Iris
id: '47565'
last_name: Gräßler
orcid: 0000-0001-5765-971X
- first_name: Michael
full_name: Hieb, Michael
id: '72252'
last_name: Hieb
citation:
ama: 'Gräßler I, Hieb M. Creating Synthetic Training Datasets for Inspection in
Machine Vision Quality Gates in Manufacturing. In: Lectures. AMA Service
GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany; 2023:253-524. doi:10.5162/smsi2023/d7.4'
apa: Gräßler, I., & Hieb, M. (2023). Creating Synthetic Training Datasets for
Inspection in Machine Vision Quality Gates in Manufacturing. Lectures,
253–524. https://doi.org/10.5162/smsi2023/d7.4
bibtex: '@inproceedings{Gräßler_Hieb_2023, title={Creating Synthetic Training Datasets
for Inspection in Machine Vision Quality Gates in Manufacturing}, DOI={10.5162/smsi2023/d7.4},
booktitle={Lectures}, publisher={AMA Service GmbH, Von-Münchhausen-Str. 49, 31515
Wunstorf, Germany}, author={Gräßler, Iris and Hieb, Michael}, year={2023}, pages={253–524}
}'
chicago: Gräßler, Iris, and Michael Hieb. “Creating Synthetic Training Datasets
for Inspection in Machine Vision Quality Gates in Manufacturing.” In Lectures,
253–524. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023.
https://doi.org/10.5162/smsi2023/d7.4.
ieee: 'I. Gräßler and M. Hieb, “Creating Synthetic Training Datasets for Inspection
in Machine Vision Quality Gates in Manufacturing,” in Lectures, Nuremberg,
2023, pp. 253–524, doi: 10.5162/smsi2023/d7.4.'
mla: Gräßler, Iris, and Michael Hieb. “Creating Synthetic Training Datasets for
Inspection in Machine Vision Quality Gates in Manufacturing.” Lectures,
AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023, pp.
253–524, doi:10.5162/smsi2023/d7.4.
short: 'I. Gräßler, M. Hieb, in: Lectures, AMA Service GmbH, Von-Münchhausen-Str.
49, 31515 Wunstorf, Germany, 2023, pp. 253–524.'
conference:
end_date: 2023-05-11
location: Nuremberg
name: SMSI 2023. Sensor and Measurement Science International
start_date: 2023-05-08
date_created: 2024-03-25T10:16:24Z
date_updated: 2024-03-25T11:05:53Z
department:
- _id: '152'
doi: 10.5162/smsi2023/d7.4
keyword:
- synthetic training data
- machine vision quality gates
- deep learning
- automated inspection and quality control
- production control
language:
- iso: eng
page: 253-524
publication: Lectures
publication_status: published
publisher: AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany
quality_controlled: '1'
status: public
title: Creating Synthetic Training Datasets for Inspection in Machine Vision Quality
Gates in Manufacturing
type: conference
user_id: '5905'
year: '2023'
...
---
_id: '27111'
abstract:
- lang: eng
text: In the industry 4.0 era, there is a growing need to transform unstructured
data acquired by a multitude of sources into information and subsequently into
knowledge to improve the quality of manufactured products, to boost production,
for predictive maintenance, etc. Data-driven approaches, such as machine learning
techniques, are typically employed to model the underlying relationship from data.
However, an increase in model accuracy with state-of-the-art methods, such as
deep convolutional neural networks, results in less interpretability and transparency.
Due to the ease of implementation, interpretation and transparency to both domain
experts and non-experts, a rule-based method is proposed in this paper, for prognostics
and health management (PHM) and specifically for diagnostics. The proposed method
utilizes the most relevant sensor signals acquired via feature extraction and
selection techniques and expert knowledge. As a case study, the presented method
is evaluated on data from a real-world quality control set-up provided by the
European prognostics and health management society (PHME) at the conference’s
2021 data challenge. With the proposed method, our team took the third place,
capable of successfully diagnosing different fault modes, irrespective of varying
conditions.
author:
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Lars
full_name: Muth, Lars
id: '77313'
last_name: Muth
orcid: 0000-0002-2938-5616
- first_name: Meike Claudia
full_name: Wohlleben, Meike Claudia
id: '43991'
last_name: Wohlleben
orcid: 0009-0009-9767-7168
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Aimiyekagbon OK, Muth L, Wohlleben MC, Bender A, Sextro W. Rule-based Diagnostics
of a Production Line. In: Do P, King S, Fink O, eds. Proceedings of the European
Conference of the PHM Society 2021. Vol 6. ; 2021:527-536. doi:10.36001/phme.2021.v6i1.3042'
apa: Aimiyekagbon, O. K., Muth, L., Wohlleben, M. C., Bender, A., & Sextro,
W. (2021). Rule-based Diagnostics of a Production Line. In P. Do, S. King, &
O. Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021
(Vol. 6, Issue 1, pp. 527–536). https://doi.org/10.36001/phme.2021.v6i1.3042
bibtex: '@inproceedings{Aimiyekagbon_Muth_Wohlleben_Bender_Sextro_2021, title={Rule-based
Diagnostics of a Production Line}, volume={6}, DOI={10.36001/phme.2021.v6i1.3042},
number={1}, booktitle={Proceedings of the European Conference of the PHM Society
2021}, author={Aimiyekagbon, Osarenren Kennedy and Muth, Lars and Wohlleben, Meike
Claudia and Bender, Amelie and Sextro, Walter}, editor={Do, Phuc and King, Steve
and Fink, Olga}, year={2021}, pages={527–536} }'
chicago: Aimiyekagbon, Osarenren Kennedy, Lars Muth, Meike Claudia Wohlleben, Amelie
Bender, and Walter Sextro. “Rule-Based Diagnostics of a Production Line.” In Proceedings
of the European Conference of the PHM Society 2021, edited by Phuc Do, Steve
King, and Olga Fink, 6:527–36, 2021. https://doi.org/10.36001/phme.2021.v6i1.3042.
ieee: 'O. K. Aimiyekagbon, L. Muth, M. C. Wohlleben, A. Bender, and W. Sextro, “Rule-based
Diagnostics of a Production Line,” in Proceedings of the European Conference
of the PHM Society 2021, 2021, vol. 6, no. 1, pp. 527–536, doi: 10.36001/phme.2021.v6i1.3042.'
mla: Aimiyekagbon, Osarenren Kennedy, et al. “Rule-Based Diagnostics of a Production
Line.” Proceedings of the European Conference of the PHM Society 2021,
edited by Phuc Do et al., vol. 6, no. 1, 2021, pp. 527–36, doi:10.36001/phme.2021.v6i1.3042.
short: 'O.K. Aimiyekagbon, L. Muth, M.C. Wohlleben, A. Bender, W. Sextro, in: P.
Do, S. King, O. Fink (Eds.), Proceedings of the European Conference of the PHM
Society 2021, 2021, pp. 527–536.'
conference:
name: PHM Society European Conference
date_created: 2021-11-03T12:26:39Z
date_updated: 2023-09-22T09:13:01Z
department:
- _id: '151'
doi: 10.36001/phme.2021.v6i1.3042
editor:
- first_name: Phuc
full_name: Do, Phuc
last_name: Do
- first_name: Steve
full_name: King, Steve
last_name: King
- first_name: Olga
full_name: Fink, Olga
last_name: Fink
intvolume: ' 6'
issue: '1'
keyword:
- PHME 2021
- Feature Selection Classification
- Feature Selection Clustering
- Interpretable Model
- Transparent Model
- Industry 4.0
- Real-World Diagnostics
- Quality Control
- Predictive Maintenance
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://papers.phmsociety.org/index.php/phme/article/download/3042/1812
oa: '1'
page: 527-536
publication: Proceedings of the European Conference of the PHM Society 2021
publication_status: published
quality_controlled: '1'
status: public
title: Rule-based Diagnostics of a Production Line
type: conference
user_id: '9557'
volume: 6
year: '2021'
...