Penggunaan Metode Image Processing Sebagai Alat Karakterisasi Hasil Pelapisan pada Lambung Kapal

  • Dianita Wardani Politeknik Perkapalan Negeri Surabaya, Surabaya, Indonesia
  • Imah Luluk K Politeknik Perkapalan Negeri Surabaya, Surabaya, Indonesia
  • M Rizal Fahmi Politeknik Perkapalan Negeri Surabaya, Surabaya, Indonesia
  • Agus Khumaidi Politeknik Perkapalan Negeri Surabaya, Surabaya, Indonesia
  • Basuki Rahmat Politeknik Perkapalan Negeri Surabaya, Surabaya, Indonesia
  • Nur Aini Aziza Politeknik Perkapalan Negeri Surabaya, Surabaya, Indonesia
  • Widya Rika Puspita Politeknik Negeri Batam, Batam, Indonesia
Keywords: coating, corrosion, MSE

Abstract

Indonesia is a maritime country that has a large territorial water, one of the efforts to protect the ship's hull from corrosion is by using coating technology. In this research, the process of characterizing the results of image processing on the hull was carried out to determine the corrosion level of the hull. 4 stages of the process, including: Taking samples and images of the layers of the ship parts that need maintenance and coating. In making a prototype, the coating results are assembled using the mini PCNVIDIA Jatson Nano using a webcam, then the images obtained will be processed using Edge detection uses a cany to obtain the contours of the bilge cross section of the ship. Next, using the Neural Network as an artificial material to create images captured from the captured prototype results on the results of coating or coating on the observed parts of the ship. The results of various image captures are processed and observed for shapes, patterns, contours, corrosion and coatings that are formed. The image processing method can be used as an inspection method for coating results with readings on data and software with 2 readings on data, namely reject or accept. Gradient values ​​are related to changes in MSE, so gradient values ​​cannot be used as a reference to justify model performance. With the addition of iterations, the value of the neural network and neural targets produced is linear.

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References

A. H. Sudibyo, A., & Iswanto, “Optimalisasi perawatan dan perbaikan kapal di PT. X,” Jurnal Teknologi Industri, vol. 9, no. 2, pp. 74–81, 2016.

L. N. Budiyanto;Sulistyo;, “PERLINDUNGAN KOROSI PADA MATERIAL BAJA A36 MELALUI PROSES PENGECETAN UNTUK LAMBUNG KAPAL,” Jurnal Saintek Maritim, vol. 21, no. 1, pp. 1–23, 2020.

Y. Zhang, D., Wei, D., Liu, B., Wang, F., & Zhang, “Performance and cost analysis of anti-corrosive coating system for ship hulls,” Ocean Engineering, vol. 167, pp. 164–171, 2016.

S.-J. Lee and J.-J. Lai, “The effects of electropolishing (EP) process parameters on corrosion resistance of 316L stainless steel,” J Mater Process Technol, vol. 140, pp. 206–210, Sep. 2003, doi: 10.1016/S0924-0136(03)00785-4.

F. Rahim, M., Fitranto, A., Luthfi, M. A., & Mustaqimah, “Analysis of Metal Coating Process Optimization on Aluminum Alloy AA6061 using Design of Experiment Method,” IOP Conf Ser Mater Sci Eng, vol. 1, p. 333, 2018.

B. Utomo, S. Sugeng, S. Sulaiman, and A. Windyandari, “Aplikasi Teknik Pembersihan Plat Baja Karbon Pada Lambung Kapal Dengan Metode Sandblasting,” Jurnal Pengabdian Vokasi, vol. 1, no. 2, pp. 79–82, 2019.

F. Huang, Y., Liu, Y., Wang, X., Wang, X., & Wang, “Corrosion performance of sandblasted metal surfaces for marine environment,” pp. 1132–1140, 2016.

M. Iqbal et al., “Penggunaan SEM dan Image-J dalam Mempelajari Ketebalan Lapisan Mikrostruktur,” Journal of Applied Electrical Engineering, vol. 5, no. 2, pp. 69–74, Dec. 2021, doi: 10.30871/JAEE.V5I2.3746.

H. A. Abdulhameed, R. A., Mohammed, A. J., Abdul-Jabbar, N. H., & Al-Janabi, “Corrosion of ship hull steel and its control techniques,” Engineering Science and Technology, an International Journal, vol. 2, no. 24, pp. 397–407, 2021.

L. Budiyanto and Y. Yulianto, “Degradasi Lapisan Cat Pelindung Korosi Pada Plat Lambung Kapal Terhadap Aliran Air Laut, Air Tawar, dan Air Payau,” Dinamika Bahari, vol. 3, no. 1, pp. 29–35, 2022, doi: 10.46484/db.v3i1.302.

S. Kumar, R. Singh, N. S. Maurya, and R. Vikram, “Monitoring of Corrosion in the Pipeline of a Distribution Network Using Weight Loss Method and Image Processing Technique,” J Mater Eng Perform, pp. 1–7, Dec. 2022, doi: 10.1007/S11665-022-07750-Z/METRICS.

N. D. Hoang and V. D. Tran, “Image Processing-Based Detection of Pipe Corrosion Using Texture Analysis and Metaheuristic-Optimized Machine Learning Approach,” Comput Intell Neurosci, vol. 2019, 2019, doi: 10.1155/2019/8097213.

N. D. Hoang, “Image Processing-Based Pitting Corrosion Detection Using Metaheuristic Optimized Multilevel Image Thresholding and Machine-Learning Approaches,” Math Probl Eng, vol. 2020, 2020, doi: 10.1155/2020/6765274.

M. Khayatazad, L. De Pue, and W. De Waele, “Detection of corrosion on steel structures using automated image processing,” Developments in the Built Environment, vol. 3, p. 100022, Aug. 2020, doi: 10.1016/J.DIBE.2020.100022.

S. Zhu and X. Tan, “A Novel Automatic Image Annotation Method Based on Multi-Instance Learning,” Procedia Eng, vol. 15, pp. 3439–3444, Dec. 2011, doi: 10.1016/j.proeng.2011.08.644.

P. Howarth and S. Rueger, Evaluation of Texture Features for Content-Based Image Retrieval, vol. 3115. 2004. doi: 10.1007/978-3-540-27814-6_40.

I. Matsubara, T., Ikeda, T., Uehara, K., & Tsubaki, “Image database management system with indexing for content-based retrieval,” Journal of Imaging Science and Technology, vol. 4, no. 64, pp. 40606-1-40606–8, 2020.

P. Papadopoulos, S., Axenopoulos, A., & Daras, “A novel database for multimedia analytics research,” Multimed Tools Appl, vol. 78, no. 19, pp. 27609–27633, 2019.

A. Couto, E. M., de Moraes, J. R. M., & de Albuquerque Araújo, “An image database for automated fish species recognition,” Data Brief, no. 31, 2020.

R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural Features for Image Classification,” IEEE Trans Syst Man Cybern, vol. SMC-3, no. 6, pp. 610–621, 1973, doi: 10.1109/TSMC.1973.4309314.

D. Sherwood, B. Emmanuel, and I. Cole, “Implementation Analysis of GLCM and Naive Bayes Methods in Conducting Extractions on Dental Image,” IOP Conf Ser Mater Sci Eng, vol. 407, no. 1, p. 012146, Aug. 2018, doi: 10.1088/1757-899X/407/1/012146.

T. Sukma, A. Sukiman, S. Suwilo, and M. Zarlis, “Feature Extraction Method GLCM and LVQ in Digital Image-Based Face Recognition,” Sinkron : jurnal dan penelitian teknik informatika, vol. 4, no. 1, pp. 1–4, Sep. 2019, doi: 10.33395/SINKRON.V4I1.10199.

R. Andhika Surya, A. Fadlil, A. Yudhana, A. Dahlan, and J. Soepomo, “Ekstraksi Ciri Metode Gray Level Co-Occurrence Matrix (GLCM) dan Filter Gabor untuk Klasifikasi citra Batik Pekalongan,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 2, no. 2, pp. 23–26, Jul. 2017, doi: 10.30591/JPIT.V2I2.520.

D. Djumhariyanto, A. Bigwanto, and S. Mulyadi, “Analisis Proses Sandblasting dengan Variasi Jarak, Sudut dan Waktu Terhadap Kekasaran Permukaan dengan Metode Respon Surface,” ReTII, Nov. 2018, Accessed: Jun. 22, 2023. [Online]. Available: //journal.itny.ac.id/index.php/ReTII/article/view/1017

Published
2023-06-26
How to Cite
Wardani, D., Luluk K, I., Fahmi, M. R., Khumaidi, A., Rahmat, B., Aziza, N., & Puspita, W. (2023). Penggunaan Metode Image Processing Sebagai Alat Karakterisasi Hasil Pelapisan pada Lambung Kapal. Journal of Applied Electrical Engineering, 7(1), 37-41. https://doi.org/10.30871/jaee.v7i1.5435
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