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Abstract

We demonstrate in this study that a rotating magnetic field (RMF) and spinning magnetic particles using this kind of magnetic field give rise to a motion mechanism capable of triggering mixing effect in liquids. In this experimental work two mixing mechanisms were used, magnetohydrodynamics due to the Lorentz force and mixing due to magnetic particles under the action of RMF, acted upon by the Kelvin force. To evidence these mechanisms,we report mixing time measured during the neutralization process (weak acid-strong base) under the action of RMF with and without magnetic particles. The efficiency of the mixing process was enhanced by a maximum of 6.5% and 12.8% owing to the application of RMF and the synergistic effect of magnetic field and magnetic particles, respectively.
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Authors and Affiliations

Rafał Rakoczy
1
ORCID: ORCID
Marian Kordas
1
ORCID: ORCID
Agata Markowska-Szczupak
1
ORCID: ORCID
Maciej Konopacki
1
ORCID: ORCID
Adrian Augustyniak
1
ORCID: ORCID
Joanna Jabłońska
1
Oliwia Paszkiewicz
1
ORCID: ORCID
Kamila Dubrowska
1
Grzegorz Story
1
Anna Story
1
Katarzyna Ziętarska
1
Dawid Sołoducha
1
Tomasz Borowski
1
Marta Roszak
2
Bartłomiej Grygorcewicz
2
ORCID: ORCID
Barbara Dołęgowska
2
ORCID: ORCID

  1. West Pomeranian University of Technology in Szczecin, Faculty of Chemical Technology and Engineering, Department of Chemical and Process Engineering, al. Piastów 42,71-065 Szczecin, Poland
  2. Pomeranian Medical University in Szczecin, Chair of Microbiology, Immunology and Laboratory Medicine, Department of Laboratory Medicine, al. Powstańców Wielkopolskich 72, 70-111 Szczecin, Poland
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Abstract

This study investigates image processing techniques for detecting surface cracks in spring steel components, with a focus on applications like Magnetic Particle Inspection (MPI) in industries such as railways and automotive. The research details a comprehensive methodology that covers data collection, software tools, and image processing methods. Various techniques, including Canny edge detection, Hough Transform, Gabor Filters, and Convolutional Neural Networks (CNNs), are evaluated for their effectiveness in crack detection. The study identifies the most successful methods, providing valuable insights into their performance. The paper also introduces a novel batch processing approach for efficient and automated crack detection across multiple images. The trade-offs between detection accuracy and processing speed are analyzed for the Morphological Top-hat filter and Canny edge filter methods. The Top-hat method, with thresholding after filtering, excelled in crack detection, with no false positives in tested images. The Canny edge filter, while efficient with adjusted parameters, needs further optimization for reducing false positives. In conclusion, the Top-hat method offers an efficient approach for crack detection during MPI. This research offers a foundation for developing advanced automated crack detection system, not only to spring sector but also extends to various industrial processes such as casting and forging tools and products, thereby widening the scope of applicability.
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Bibliography

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Authors and Affiliations

Marcin M. Marciniak
1

  1. Rzeszow University of Technology, Poland

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