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Article

A Self-Organizing Fuzzy Logic Classifier for Benchmarking Robot-Aided Blasting of Ship Hulls

1
Engineering Product Development Pillar, Singapore University of Technology and Design, 8 Somapah Rd, Singapore 487372, Singapore
2
Optoelectronics Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
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Brightsun Marine Pte Ltd, 9 Tuas Ave 8, Singapore 639224, Singapore
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(11), 3215; https://doi.org/10.3390/s20113215
Received: 4 May 2020 / Revised: 29 May 2020 / Accepted: 2 June 2020 / Published: 5 June 2020
(This article belongs to the Section Intelligent Sensors)
Regular dry dock maintenance work on ship hulls is essential for maintaining the efficiency and sustainability of the shipping industry. Hydro blasting is one of the major processes of dry dock maintenance work, where human labor is extensively used. The conventional methods of maintenance work suffer from many shortcomings, and hence robotized solutions have been developed. This paper proposes a novel robotic system that can synthesize a benchmarking map for a previously blasted ship hull. A Self-Organizing Fuzzy logic (SOF) classifier has been developed to benchmark the blasting quality of a ship hull similar to blasting quality categorization done by human experts. Hornbill, a multipurpose inspection and maintenance robot intended for hydro blasting, benchmarking, and painting, has been developed by integrating the proposed SOF classifier. Moreover, an integrated system solution has been developed to improve dry dock maintenance of ship hulls. The proposed SOF classifier can achieve a mean accuracy of 0.9942 with an execution time of 8.42 µs. Realtime experimenting with the proposed robotic system has been conducted on a ship hull. This experiment confirms the ability of the proposed robotic system in synthesizing a benchmarking map that reveals the benchmarking quality of different areas of a previously blasted ship hull. This sort of a benchmarking map would be useful for ensuring the blasting quality as well as performing efficient spot wise reblasting before the painting. Therefore, the proposed robotic system could be utilized for improving the efficiency and quality of hydro blasting work on the ship hull maintenance industry. View Full-Text
Keywords: self-organizing fuzzy logic classifier; benchmarking blasting quality; hydro blasting; ship hull maintenance; robotics for ship maintenance industry self-organizing fuzzy logic classifier; benchmarking blasting quality; hydro blasting; ship hull maintenance; robotics for ship maintenance industry
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MDPI and ACS Style

Muthugala, M.A.V.J.; Le, A.V.; Cruz, E.S.; Rajesh Elara, M.; Veerajagadheswar, P.; Kumar, M. A Self-Organizing Fuzzy Logic Classifier for Benchmarking Robot-Aided Blasting of Ship Hulls. Sensors 2020, 20, 3215. https://doi.org/10.3390/s20113215

AMA Style

Muthugala MAVJ, Le AV, Cruz ES, Rajesh Elara M, Veerajagadheswar P, Kumar M. A Self-Organizing Fuzzy Logic Classifier for Benchmarking Robot-Aided Blasting of Ship Hulls. Sensors. 2020; 20(11):3215. https://doi.org/10.3390/s20113215

Chicago/Turabian Style

Muthugala, M. A.V.J., Anh V. Le, Eduardo S. Cruz, Mohan Rajesh Elara, Prabakaran Veerajagadheswar, and Madhu Kumar. 2020. "A Self-Organizing Fuzzy Logic Classifier for Benchmarking Robot-Aided Blasting of Ship Hulls" Sensors 20, no. 11: 3215. https://doi.org/10.3390/s20113215

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