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The Optimization of The Zinc Electroplating Bath Using Machine Learning And Genetic Algorithms (NSGA-II)

Yıl 2022, , 1050 - 1058, 31.12.2022
https://doi.org/10.17798/bitlisfen.1170707

Öz

In this study, our aim is to predict the compositions of zinc electroplating bath using machine learning method and optimize the organic additives with NSGA-II (Non-dominated Sorting Genetic Algorithm) optimization algorithm. Mask RCNN was utilized to classify the coated plates according to their appearance. The names of classes were defined as ”Full Bright”, ”Full Fail”, ”HCD Fail” and ”LCD Fail”. The intersection over union (IoU) values of the Mask RCNN model were determined in the range of 93–97%. Machine learning algorithms, MLP, SVR, XGB, RF, were trained using the classification of the coated panels whose classes were detected by the Mask RCNN. In the machine learning training, the additives in the electrodeposition bath were specified as input and the classes of the coated panels as output. From the trained models, RF gave the highest F1 scores for all the classes. The F1 scores of RF model for ”Full Bright”, ”Full Fail”, ”HCD Fail” and ”LCD Fail” are 0.95, 0.91, 1 and 0.80 respectively. Genetic algorithm (NSGA-II) was used to optimize the compositions of the bath. The trained RF models for all the classes were utilized as the objective function. The ranges of organic additives, which should be used for all the classes in the electrodeposition bath, were determined.

Teşekkür

The experiments reported in this paper were partially performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources) and some computing resources were provided by the National Center for High Perfor- mance Computing of Turkey (UHeM).

Kaynakça

  • R. Katirci, E. K. Yilmaz, O. Kaynar, and M. Zontul, “Automated evaluation of Cr-III coated parts using Mask RCNN and ML methods,” Surf. Coat. Technol., vol. 422, no. 127571, p. 127571, Sep. 2021.
  • E. Sezer, B. Ustamehmetoğlu, and R. Katirci, “Effects of a N,N-dimethyl-N-2-propenyl-2-propene-1-ammonium chloride-2-propenamide copolymer on bright nickel plating,” Surf. Coat. Technol., vol. 213, pp. 253–263, Dec. 2012.
  • R. Katirci and U. Yilmaz, “Statistical studies of Zn–Ni alloy coatings using Non-cyanide alkaline baths containing polyethyleneimine complexing agents,” Transactions of the IMF, vol. 92, no. 5, pp. 245–252, Sep. 2014.
  • R. Katirci, “A chrome coating from a trivalent chromium bath containing extremely low concentration of Cr3+ ions,” Int. J. Surf. Sci. Eng., vol. 10, no. 1, pp. 73–85, Jan. 2016.
  • L. N. Bengoa, W. R. Tuckart, N. Zabala, G. Prieto, and W. A. Egli, “Bronze electrodeposition from an acidic non-cyanide high efficiency electrolyte: Tribological behavior,” Surf. Coat. Technol., vol. 253, pp. 241–248, Aug. 2014.
  • Z. Lai et al., “Computational analysis and experimental evidence of two typical levelers for acid copper electroplating,” Electrochim. Acta, vol. 273, pp. 318–326, May 2018.
  • M. Kul, S. U. of Science, and 58050 Sivas Turkey Technology Department of Aeronautical Engineering, “Effect of process parameters on the electrodeposition of zinc on 1010 steel: Central composite design optimization,” Int. J. Electrochem. Sci., vol. 15, pp. 9779–9795, Oct. 2020.
  • M. Chotirach et al., “Systematic investigation of brightener’ s effects on alkaline non-cyanide zinc electroplating using HPLC and molecular modeling,” Mater. Chem. Phys., vol. 277, no. 125567, p. 125567, Feb. 2022.
  • N. Sorour, W. Zhang, E. Ghali, and G. Houlachi, “A review of organic additives in zinc electrodeposition process (performance and evaluation),” Hydrometallurgy, vol. 171, pp. 320–332, Aug. 2017.
  • R. Katirci, E. Sezer, and B. Ustamehmetoğlu, “Statistical optimisation of organic additives for maximum brightness and brightener analysis in a nickel electroplating bath,” Transactions of the IMF, vol. 93, no. 2, pp. 89–96, Mar. 2015.
  • K. Hameed, D. Chai, and A. Rassau, “Score-based mask edge improvement of Mask-RCNN for segmentation of fruit and vegetables,” Expert Syst. Appl., vol. 190, p. 116205, Mar. 2022.
  • B. Lenz, H. Hasselbruch, A. G. Holger, and Mehner, “Application of CNN networks for an automatic determination of critical loads in scratch tests on a-C:H:W coatings,” Surf. Coat. Technol., vol. 393, p. 125764, Jul. 2020.
  • J. Zhu, X. Wang, L. Kou, L. Zheng, and H. Zhang, “Prediction of control parameters corresponding to in-flight particles in atmospheric plasma spray employing convolutional neural networks,” Surf. Coat. Technol., vol. 394, p. 125862, Jul. 2020.
  • R. Katirci, H. Aktas, and M. Zontul, “The prediction of the ZnNi thickness and Ni % of ZnNi alloy electroplating using a machine learning method,” Transactions of the IMF, vol. 99, no. 3, pp. 162–168, May 2021.
  • M. P. M. Schlesinger, Modern Electroplating. Elsevier, 2011.
  • Y. Nakamura, N. Kaneko, M. Watanabe, and H. Nezu, “Effects of saccharin and aliphatic alcohols on the electrocrystallization of nickel,” J. Appl. Electrochem., vol. 24, no. 3, Mar. 1994.
  • A. Kirtis, M. Aasim, and R. Katirci, “Application of artificial neural network and machine learning algorithms for modeling the in vitro regeneration of chickpea (Cicer arietinum L.),” Plant Cell Tissue Organ Cult., Mar. 2022.
  • T. V. Dinh, H. Nguyen, X.-L. Tran, and N.-D. Hoang, “Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification,” Math. Probl. Eng., vol. 2021, Feb. 2021.
  • A. Rizwan, N. Iqbal, R. Ahmad, and D.-H. Kim, “WR-SVM Model Based on the Margin Radius Approach for Solving the Minimum Enclosing Ball Problem in Support Vector Machine Classification”.
  • Y. Wei, J. Jang-Jaccard, F. Sabrina, A. Singh, W. Xu, and S. Camtepe, “AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification,” IEEE Access, vol. 9, pp. 146810–146821, 2021.
  • M. Ramkumar, C. G. Babu, K. V. Kumar, D. Hepsiba, A. Manjunathan, and R. S. Kumar, “ECG Cardiac arrhythmias Classification using DWT, ICA and MLP Neural Networks,” J. Phys. Conf. Ser., vol. 1831, no. 1, p. 12015, Mar. 2021.
  • M. Ma et al., “XGBoost-based method for flash flood risk assessment,” J. Hydrol., vol. 598, p. 126382, Jul. 2021.
  • D. Zhang et al., “iBLP: An XGBoost-Based Predictor for Identifying Bioluminescent Proteins,” Comput. Math. Methods Med., vol. 2021, p. 6664362, Jan. 2021.
  • C. Villacampa-Calvo, B. Zaldivar, E. C. Garrido-Merchán, and D. Hernández-Lobato, “Multi-class Gaussian Process Classification with Noisy Inputs,” J. Mach. Learn. Res., vol. 22, no. 36, pp. 1–52, 2021.
  • B. Gips, “Texture-Based Seafloor Characterization Using Gaussian Process Classification,” IEEE J. Oceanic Eng., pp. 1–11, 2022.
  • Y. Chen, W. Zheng, W. Li, and Y. Huang, “Large group activity security risk assessment and risk early warning based on random forest algorithm,” Pattern Recognit. Lett., vol. 144, pp. 1–5, Apr. 2021.
  • K. Liu, X. Hu, H. Zhou, L. Tong, W. D. Widanage, and J. Marco, “Feature Analyses and Modeling of Lithium-Ion Battery Manufacturing Based on Random Forest Classification,” IEEE/ASME Trans. Mechatron., vol. 26, no. 6, pp. 2944–2955, Dec. 2021.
  • B.-C. Yan, H.-W. Wang, S.-W. F. Jiang, F.-A. Chao, and B. Chen, “MAXIMUM F1-SCORE TRAINING FOR END-TO-END MISPRONUNCIATION DETECTION AND DIAGNOSIS OF L2 ENGLISH SPEECH”.
  • B. Ćwiklinski, A. Giełczyk, and M. Choraś, “Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers,” Entropy, vol. 23, no. 1, Jan. 2021.
  • F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
  • K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, Apr. 2002.
  • E. D. Durmaz and R. Sahin, “NSGA-II and goal programming approach for the multi-objective single row facility layout problem,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 32, no. 3, pp. 941–955, 2017, doi: 10.17341/gazimmfd.337647.
  • N. Srinivas and K. Deb, “Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms,” Evolutionary Computation, vol. 2, no. 3, pp. 221–248, Sep. 1994, doi: 10.1162/evco.1994.2.3.221.
  • A. Ala, F. E. Alsaadi, M. Ahmadi, and S. Mirjalili, “Optimization of an appointment scheduling problem for healthcare systems based on the quality of fairness service using whale optimization algorithm and NSGA-II,” Sci. Rep., vol. 11, no. 1, p. 19816, Oct. 2021.
  • S. Verma, M. Pant, and V. Snasel, “A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems,” IEEE Access, vol. 9, pp. 57757–57791, 2021.
  • P. Wang, J. Huang, Z. Cui, L. Xie, and J. Chen, “A Gaussian error correction multi‐objective positioning model with NSGA‐II,” Concurr. Comput., vol. 32, no. 5, Mar. 2020.
  • J. Blank and K. Deb, “Pymoo: Multi-Objective Optimization in Python,” IEEE Access, vol. 8, pp. 89497–89509, 2020.
Yıl 2022, , 1050 - 1058, 31.12.2022
https://doi.org/10.17798/bitlisfen.1170707

Öz

Kaynakça

  • R. Katirci, E. K. Yilmaz, O. Kaynar, and M. Zontul, “Automated evaluation of Cr-III coated parts using Mask RCNN and ML methods,” Surf. Coat. Technol., vol. 422, no. 127571, p. 127571, Sep. 2021.
  • E. Sezer, B. Ustamehmetoğlu, and R. Katirci, “Effects of a N,N-dimethyl-N-2-propenyl-2-propene-1-ammonium chloride-2-propenamide copolymer on bright nickel plating,” Surf. Coat. Technol., vol. 213, pp. 253–263, Dec. 2012.
  • R. Katirci and U. Yilmaz, “Statistical studies of Zn–Ni alloy coatings using Non-cyanide alkaline baths containing polyethyleneimine complexing agents,” Transactions of the IMF, vol. 92, no. 5, pp. 245–252, Sep. 2014.
  • R. Katirci, “A chrome coating from a trivalent chromium bath containing extremely low concentration of Cr3+ ions,” Int. J. Surf. Sci. Eng., vol. 10, no. 1, pp. 73–85, Jan. 2016.
  • L. N. Bengoa, W. R. Tuckart, N. Zabala, G. Prieto, and W. A. Egli, “Bronze electrodeposition from an acidic non-cyanide high efficiency electrolyte: Tribological behavior,” Surf. Coat. Technol., vol. 253, pp. 241–248, Aug. 2014.
  • Z. Lai et al., “Computational analysis and experimental evidence of two typical levelers for acid copper electroplating,” Electrochim. Acta, vol. 273, pp. 318–326, May 2018.
  • M. Kul, S. U. of Science, and 58050 Sivas Turkey Technology Department of Aeronautical Engineering, “Effect of process parameters on the electrodeposition of zinc on 1010 steel: Central composite design optimization,” Int. J. Electrochem. Sci., vol. 15, pp. 9779–9795, Oct. 2020.
  • M. Chotirach et al., “Systematic investigation of brightener’ s effects on alkaline non-cyanide zinc electroplating using HPLC and molecular modeling,” Mater. Chem. Phys., vol. 277, no. 125567, p. 125567, Feb. 2022.
  • N. Sorour, W. Zhang, E. Ghali, and G. Houlachi, “A review of organic additives in zinc electrodeposition process (performance and evaluation),” Hydrometallurgy, vol. 171, pp. 320–332, Aug. 2017.
  • R. Katirci, E. Sezer, and B. Ustamehmetoğlu, “Statistical optimisation of organic additives for maximum brightness and brightener analysis in a nickel electroplating bath,” Transactions of the IMF, vol. 93, no. 2, pp. 89–96, Mar. 2015.
  • K. Hameed, D. Chai, and A. Rassau, “Score-based mask edge improvement of Mask-RCNN for segmentation of fruit and vegetables,” Expert Syst. Appl., vol. 190, p. 116205, Mar. 2022.
  • B. Lenz, H. Hasselbruch, A. G. Holger, and Mehner, “Application of CNN networks for an automatic determination of critical loads in scratch tests on a-C:H:W coatings,” Surf. Coat. Technol., vol. 393, p. 125764, Jul. 2020.
  • J. Zhu, X. Wang, L. Kou, L. Zheng, and H. Zhang, “Prediction of control parameters corresponding to in-flight particles in atmospheric plasma spray employing convolutional neural networks,” Surf. Coat. Technol., vol. 394, p. 125862, Jul. 2020.
  • R. Katirci, H. Aktas, and M. Zontul, “The prediction of the ZnNi thickness and Ni % of ZnNi alloy electroplating using a machine learning method,” Transactions of the IMF, vol. 99, no. 3, pp. 162–168, May 2021.
  • M. P. M. Schlesinger, Modern Electroplating. Elsevier, 2011.
  • Y. Nakamura, N. Kaneko, M. Watanabe, and H. Nezu, “Effects of saccharin and aliphatic alcohols on the electrocrystallization of nickel,” J. Appl. Electrochem., vol. 24, no. 3, Mar. 1994.
  • A. Kirtis, M. Aasim, and R. Katirci, “Application of artificial neural network and machine learning algorithms for modeling the in vitro regeneration of chickpea (Cicer arietinum L.),” Plant Cell Tissue Organ Cult., Mar. 2022.
  • T. V. Dinh, H. Nguyen, X.-L. Tran, and N.-D. Hoang, “Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification,” Math. Probl. Eng., vol. 2021, Feb. 2021.
  • A. Rizwan, N. Iqbal, R. Ahmad, and D.-H. Kim, “WR-SVM Model Based on the Margin Radius Approach for Solving the Minimum Enclosing Ball Problem in Support Vector Machine Classification”.
  • Y. Wei, J. Jang-Jaccard, F. Sabrina, A. Singh, W. Xu, and S. Camtepe, “AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification,” IEEE Access, vol. 9, pp. 146810–146821, 2021.
  • M. Ramkumar, C. G. Babu, K. V. Kumar, D. Hepsiba, A. Manjunathan, and R. S. Kumar, “ECG Cardiac arrhythmias Classification using DWT, ICA and MLP Neural Networks,” J. Phys. Conf. Ser., vol. 1831, no. 1, p. 12015, Mar. 2021.
  • M. Ma et al., “XGBoost-based method for flash flood risk assessment,” J. Hydrol., vol. 598, p. 126382, Jul. 2021.
  • D. Zhang et al., “iBLP: An XGBoost-Based Predictor for Identifying Bioluminescent Proteins,” Comput. Math. Methods Med., vol. 2021, p. 6664362, Jan. 2021.
  • C. Villacampa-Calvo, B. Zaldivar, E. C. Garrido-Merchán, and D. Hernández-Lobato, “Multi-class Gaussian Process Classification with Noisy Inputs,” J. Mach. Learn. Res., vol. 22, no. 36, pp. 1–52, 2021.
  • B. Gips, “Texture-Based Seafloor Characterization Using Gaussian Process Classification,” IEEE J. Oceanic Eng., pp. 1–11, 2022.
  • Y. Chen, W. Zheng, W. Li, and Y. Huang, “Large group activity security risk assessment and risk early warning based on random forest algorithm,” Pattern Recognit. Lett., vol. 144, pp. 1–5, Apr. 2021.
  • K. Liu, X. Hu, H. Zhou, L. Tong, W. D. Widanage, and J. Marco, “Feature Analyses and Modeling of Lithium-Ion Battery Manufacturing Based on Random Forest Classification,” IEEE/ASME Trans. Mechatron., vol. 26, no. 6, pp. 2944–2955, Dec. 2021.
  • B.-C. Yan, H.-W. Wang, S.-W. F. Jiang, F.-A. Chao, and B. Chen, “MAXIMUM F1-SCORE TRAINING FOR END-TO-END MISPRONUNCIATION DETECTION AND DIAGNOSIS OF L2 ENGLISH SPEECH”.
  • B. Ćwiklinski, A. Giełczyk, and M. Choraś, “Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers,” Entropy, vol. 23, no. 1, Jan. 2021.
  • F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
  • K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, Apr. 2002.
  • E. D. Durmaz and R. Sahin, “NSGA-II and goal programming approach for the multi-objective single row facility layout problem,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 32, no. 3, pp. 941–955, 2017, doi: 10.17341/gazimmfd.337647.
  • N. Srinivas and K. Deb, “Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms,” Evolutionary Computation, vol. 2, no. 3, pp. 221–248, Sep. 1994, doi: 10.1162/evco.1994.2.3.221.
  • A. Ala, F. E. Alsaadi, M. Ahmadi, and S. Mirjalili, “Optimization of an appointment scheduling problem for healthcare systems based on the quality of fairness service using whale optimization algorithm and NSGA-II,” Sci. Rep., vol. 11, no. 1, p. 19816, Oct. 2021.
  • S. Verma, M. Pant, and V. Snasel, “A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems,” IEEE Access, vol. 9, pp. 57757–57791, 2021.
  • P. Wang, J. Huang, Z. Cui, L. Xie, and J. Chen, “A Gaussian error correction multi‐objective positioning model with NSGA‐II,” Concurr. Comput., vol. 32, no. 5, Mar. 2020.
  • J. Blank and K. Deb, “Pymoo: Multi-Objective Optimization in Python,” IEEE Access, vol. 8, pp. 89497–89509, 2020.
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Ramazan Katırcı 0000-0003-2448-011X

Bilal Tekin 0000-0002-6690-3152

Yayımlanma Tarihi 31 Aralık 2022
Gönderilme Tarihi 6 Eylül 2022
Kabul Tarihi 23 Aralık 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

IEEE R. Katırcı ve B. Tekin, “The Optimization of The Zinc Electroplating Bath Using Machine Learning And Genetic Algorithms (NSGA-II)”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, c. 11, sy. 4, ss. 1050–1058, 2022, doi: 10.17798/bitlisfen.1170707.



Bitlis Eren Üniversitesi
Fen Bilimleri Dergisi Editörlüğü

Bitlis Eren Üniversitesi Lisansüstü Eğitim Enstitüsü        
Beş Minare Mah. Ahmet Eren Bulvarı, Merkez Kampüs, 13000 BİTLİS        
E-posta: fbe@beu.edu.tr