Details
| Original language | English |
|---|---|
| Pages (from-to) | 92-97 |
| Number of pages | 6 |
| Journal | Procedia CIRP |
| Volume | 127 |
| Early online date | 10 Oct 2024 |
| Publication status | Published - 2024 |
| Event | 10th CIRP Conference on Assembly Technology and Systems, CATS 2024 - Karlsruhe, Germany Duration: 24 Apr 2024 → 26 Apr 2024 |
Abstract
In previous work, a concept for a novel flexible part feeding system based on aerodynamic feeding was presented. In contrast to conventional part feeding systems, such as vibratory bowl feeders, aerodynamic part feeding systems use controlled pressurized air jets to reorient the workpieces. The proposed concept aims to increase flexibility and eliminate retooling times to meet the challenges of modern automated production with regard to increasing uncertainties, shorter product life cycles and higher cost pressure. Regarding these objectives, the determination of the workpiece poses in the feeding system requires flexible image processing, which will be enabled using machine learning and synthetic datasets. This work presents a framework which enables the classification of workpiece poses using a standard industrial camera and convolutional neural networks (CNN). The training of CNNs usually requires large datasets. However, in order to eliminate the need to create and label datasets of real images, which would cause machine downtimes and increase the retooling effort, the CNNs will be trained with synthetic datasets. Based on the CAD-data of the workpieces, artificial images of each pose are rendered using the open source engine Blender. Several CNN architectures are selected, trained with the artificial datasets and evaluated using real images of the workpieces. The results show that high classification accuracies of over 99 % can be achieved. It is concluded that the presented approach enables the classification of workpiece poses with high accuracy, while eliminating the need for the cumbersome creation of real image datasets. The approach is not limited to the use in the aerodynamic part feeding system but can be transferred to other applications in which the orientation of a workpiece has to be classified.
Keywords
- separated by semicolons, Type your keywords here
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Procedia CIRP, Vol. 127, 2024, p. 92-97.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Workpiece Pose Classification Framework for Flexible Part Feeding Systems Using Machine Learning and Synthetic Datasets
AU - Kolditz, Torge
AU - Müller, Pitt
AU - Raatz, Annika
N1 - Publisher Copyright: © 2024 Elsevier B.V.. All rights reserved.
PY - 2024
Y1 - 2024
N2 - In previous work, a concept for a novel flexible part feeding system based on aerodynamic feeding was presented. In contrast to conventional part feeding systems, such as vibratory bowl feeders, aerodynamic part feeding systems use controlled pressurized air jets to reorient the workpieces. The proposed concept aims to increase flexibility and eliminate retooling times to meet the challenges of modern automated production with regard to increasing uncertainties, shorter product life cycles and higher cost pressure. Regarding these objectives, the determination of the workpiece poses in the feeding system requires flexible image processing, which will be enabled using machine learning and synthetic datasets. This work presents a framework which enables the classification of workpiece poses using a standard industrial camera and convolutional neural networks (CNN). The training of CNNs usually requires large datasets. However, in order to eliminate the need to create and label datasets of real images, which would cause machine downtimes and increase the retooling effort, the CNNs will be trained with synthetic datasets. Based on the CAD-data of the workpieces, artificial images of each pose are rendered using the open source engine Blender. Several CNN architectures are selected, trained with the artificial datasets and evaluated using real images of the workpieces. The results show that high classification accuracies of over 99 % can be achieved. It is concluded that the presented approach enables the classification of workpiece poses with high accuracy, while eliminating the need for the cumbersome creation of real image datasets. The approach is not limited to the use in the aerodynamic part feeding system but can be transferred to other applications in which the orientation of a workpiece has to be classified.
AB - In previous work, a concept for a novel flexible part feeding system based on aerodynamic feeding was presented. In contrast to conventional part feeding systems, such as vibratory bowl feeders, aerodynamic part feeding systems use controlled pressurized air jets to reorient the workpieces. The proposed concept aims to increase flexibility and eliminate retooling times to meet the challenges of modern automated production with regard to increasing uncertainties, shorter product life cycles and higher cost pressure. Regarding these objectives, the determination of the workpiece poses in the feeding system requires flexible image processing, which will be enabled using machine learning and synthetic datasets. This work presents a framework which enables the classification of workpiece poses using a standard industrial camera and convolutional neural networks (CNN). The training of CNNs usually requires large datasets. However, in order to eliminate the need to create and label datasets of real images, which would cause machine downtimes and increase the retooling effort, the CNNs will be trained with synthetic datasets. Based on the CAD-data of the workpieces, artificial images of each pose are rendered using the open source engine Blender. Several CNN architectures are selected, trained with the artificial datasets and evaluated using real images of the workpieces. The results show that high classification accuracies of over 99 % can be achieved. It is concluded that the presented approach enables the classification of workpiece poses with high accuracy, while eliminating the need for the cumbersome creation of real image datasets. The approach is not limited to the use in the aerodynamic part feeding system but can be transferred to other applications in which the orientation of a workpiece has to be classified.
KW - separated by semicolons
KW - Type your keywords here
UR - http://www.scopus.com/inward/record.url?scp=85208555666&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2024.07.017
DO - 10.1016/j.procir.2024.07.017
M3 - Conference article
AN - SCOPUS:85208555666
VL - 127
SP - 92
EP - 97
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 10th CIRP Conference on Assembly Technology and Systems, CATS 2024
Y2 - 24 April 2024 through 26 April 2024
ER -