EVERGREEN

Joint Journal of Novel Carbon Resource Sciences and Green Asia Strategy

ISSN:2189-0420 (Print until Mar 2020)
ISSN:2432-5953 (Online)

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Predictive Surface Defect Detection in Particleboard Manufacturing using Defect Tracking Matrix–Principal Component Analysis Framework toward Zero Defect Manufacturing

Yustina Suhandini Tjahjaningsih1,2,*, Moses Laksono Singgih1, Putu Dana Karningsih1
1Department of Industrial and Systems Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
2Departement of Industrial Engineering, Universitas Panca Marga, Indonesia
*Author to whom correspondence should be addressed:
E-mail: yustina.suhandini@upm.ac.id (YST)
Evergreen, Vol. 12, Iss. 04, pp. 2184–2198, 2025
DOI: 10.5109/7402647
Creative Commons Attribution 4.0 International Creative Commons Attribution 4.0 International
Received: June 05, 2025 | Revised: August 14, 2025 | Accepted: December 17, 2025 | Published: December 2025
Abstract
Zero Defects Manufacturing (ZDM) is a proactive quality strategy aimed at preventing defects during production. This study proposes a novel integrated method using the Defect Tracking Matrix (DTM) and Principal Component Analysis (PCA) to predict the sources of surface defects in particleboard manufacturing. The authors evaluated twenty technical attributes and sixteen quality defects. Results showed that duct cleaning, setting blower, screen cleaning, press calibration, and blade sharpening were key contributors to detect patterns. The DTM-PCA framework improves traceability and helps implement ZDM through structured, data-driven analysis in a previously unexplored context.
Keywords
defect tracking matrix; particleboard industry; prediction; principal component analysis; quality control; zero defects manufacturing
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References
  1. 1) G. May, and D. Kiritsis, "Zero Defect Manufacturing Strategies and Platform for Smart Factories of Industry 4.0," Springer International Publishing, 2019 doi:10.1007/978-3-030-18180-2_11
  2. 2) A. Fundin, J. Lilja, Y. Lagrosen, and B. Bergquist, "Quality 2030: quality management for the future," Total Qual. Manag. Bus. Excell., 0 (0) 1-17 (2020) doi:10.1080/14783363.2020.1863778
  3. 3) A. Jan, Z., Ahamed, F., Mayer, W., Patel, N., Grossmann, G., Stumptner, M., & Kuusk, "Artificial intelligence for industry 4.0 : systematic review of applications, challenges, and opportunities," Expert Syst. Appl., 216 (Vol. 216, No. 119456) Vol. 216, No. 119456 (2023) doi:10.1016/j.eswa.2022.119456
  4. 4) B. Caiazzo, M. Di Nardo, T. Murino, A. Petrillo, G. Piccirillo, and S. Santini, "Towards zero defect manufacturing paradigm: a review of the state-of-the-art methods and open challenges," Comput. Ind., 134 103548 (2022) doi:10.1016/j.compind.2021.103548
  5. 5) J. Lindström, P. Kyösti, W. Birk, and E. Lejon, "An initial model for zero defect manufacturing," Appl. Sci., 10 (13) 1-16 (2020) doi:10.3390/app10134570
  6. 6) N. Leberruyer, J. Bruch, M. Ahlskog, and S. Afshar, "Toward zero defect manufacturing with the support of artificial intelligence—insights from an industrial application," Comput. Ind., 147 (July 2022) 103877 (2023) doi:10.1016/j.compind.2023.103877
  7. 7) N.E. Budiyanta, E.M. Yuniarno, T. Usagawa, and M.H. Purnomo, "Detection and tracking in human monitoring framework using modified direct 3d lidar point cloud classifier based on region cluster proposal," Evergreen, 11 (3) 2022-2034 (2024) doi:10.5109/7236849
  8. 8) N. Chauhan, D. Tomar, G. Singh, G. Mishra, and A. Mishra, "Crop prediction system using machine learning," Emerg. Trends Comput. Sci. Its Appl., 11 (2) 554-558 (2025) doi:10.1201/9781003606635-96
  9. 9) P. Saraswat, and R. Agrawal, "Artificial intelligence as key enabler for sustainable maintenance in the manufacturing industry: scope & challenges," Evergreen, 10 (4) 2490-2497 (2023) doi:10.5109/7162012
  10. 10) P. Sun, "A wood quality defect detection system based on deep learning and multicriterion framework," J. Sensors, 2022 (2022) doi:10.1155/2022/3234148
  11. 11) S.H. Lee, W.C. Lum, J.G. Boon, L. Kristak, P. Antov, M. Pedzik, T. Rogozinski, H.R. Taghiyari, M.A.R. Lubis, W. Fatriasari, S.M. Yadav, A. Chotikhun, and A. Pizzi, "Particleboard from agricultural biomass and recycled wood waste: a review," J. Mater. Res. Technol., 20 4630-4658 (2022) doi:10.1016/j.jmrt.2022.08.166
  12. 12) Z. Zhao, Z. Ge, M. Jia, X. Yang, R. Ding, and Y. Zhou, "A particleboard surface defect detection method research based on the deep learning algorithm," Sensors, 22 (20) (2022) doi:10.3390/s22207733
  13. 13) C. Zhang, C. Wang, L. Zhao, X. Qu, and X. Gao, "A method of particleboard surface defect detection and recognition based on deep learning," Wood Mater. Sci. Eng., 1-12 (2024) doi:10.1080/17480272.2024.2323579
  14. 14) S. Nurkomariyah, M. Firdaus, D.R. Nurrochmat, and J.T. Erbaugh, "Questioning the competitiveness of Indonesian wooden furniture in the global market," IOP Conf. Ser. Earth Environ. Sci., 285 (1) (2019) doi:10.1088/1755-1315/285/1/012015
  15. 15) H. Zhou, H. Xia, C. Fan, T. Lan, Y. Liu, Y. Yang, Y. Shen, and W. Yu, "Intelligent detection method for surface defects of particleboard based on super-resolution reconstruction," Forests, 15 (12) (2024) doi:10.3390/f15122196
  16. 16) J. Kang, Y. Cen, Y. Cen, K. Wang, and Y. Liu, "CFIS-yolo: a lightweight multi-scale fusion network for edge-deployable wood defect detection," 1-10 (2025). http://arxiv.org/abs/2504.11305
  17. 17) S. Singh, R. Batra, K. Rai, and S. Sujai, "Proactive quality evaluation: a novel strategy-assisted early detection in manufacturing," Proc. Eng. Sci., 6 (1) 343-352 (2024) doi:10.24874/PES.SI.24.02.017
  18. 18) K.S. Wang, "Towards zero-defect manufacturing (zdm)-a data mining approach," Adv. Manuf., 1 (1) 62-74 (2013) doi:10.1007/s40436-013-0010-9
  19. 19) I.T. Jollife, and J. Cadima, "Principal component analysis: a review and recent developments," Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 374 (2065) (2016) doi:10.1098/rsta.2015.0202
  20. 20) A. Papacharalampopoulos, D. Petrides, and P. Stavropoulos, "A defect tracking tool framework for multi-process products," Procedia CIRP, 79 (July 2018) 523-527 (2019) doi:10.1016/j.procir.2019.02.100
  21. 21) H. Wang, "Defects tracking in mass customisation production using defects tracking matrix combined with principal component analysis," Int. J. Prod. Res., 51 (6) 1852-1868 (2013) doi:10.1080/00207543.2012.718449
  22. 22) F. Psarommatis and D. Kiritsis, "A scheduling tool for achieving zero defect manufacturing (ZDM): A conceptual framework," Springer International Publishing, 2018 doi:10.1007/978-3-319-99707-0_34
  23. 23) R.S. Patil, R. V Patil, S.J. Thikane, and P.M. Patil, "Industry 4 . 0 : zero defect manufacturing (ZDM)," 4 (3) 12-16 (2019)
  24. 24) J.S. Lin, and K.H. Chen, "A novel decision support system based on computational intelligence and machine learning: towards zero-defect manufacturing in injection molding," J. Ind. Inf. Integr., 40 (April) 100621 (2024) doi:10.1016/j.jii.2024.100621
  25. 25) Y.S. Tjahjaningsih, A.B. Wijayanto, and A. Izzuddin, "Failure tracking matrix berbasis house of quality untuk merancang sistem informasi pemeliharaan (studi kasus di divisi p2 pt kti)," Pros. SENIATI, 178-188 (2019)
  26. 26) K. Grobler-Dębska, E. Kucharska, and J. Baranowski, "Formal scheduling method for zero-defect manufacturing," Int. J. Adv. Manuf. Technol., 118 (11-12) 4139-4159 (2022) doi:10.1007/s00170-021-08104-0
  27. 27) H. Wang, and Z. Lin, "Defects tracking matrix for mass customization production based on house of quality," Int. J. Flex. Manuf. Syst., 19 (4) 666-684 (2007) doi:10.1007/s10696-007-9025-5
  28. 28) T. Saaty, and L. Vargas, "Models, methods, concepts & applications of the analytic hierarchy process," Springer, 2012 doi:10.1007/978-1-4614-3597-6
  29. 29) H. Kumar, A.S. Wadhwa, S. Akhai, and A. Kaushik, "Parametric optimization of the machining performance of Al-SICP composite using a combination of response surface methodology and desirability function," Eng. Res. Express, 6 (2) (2024) doi:10.1088/2631-8695/ad38ff
  30. 30) Y. Chen, Y. Ding, F. Zhao, E. Zhang, Z. Wu, and L. Shao, "Surface defect detection methods for industrial products: areview," Appl. Sci., 11 (16) (2021)
  31. 31) T.S. Adeyemi, "Defect detection in manufacturing : an integrated deep learning approach," 153-176 (2024) doi:10.4236/jcc.2024.1210011
  32. 32) F. Kähler, O. Schmedemann, and T. Schüppstuhl, "Anomaly detection for industrial surface inspection: application in maintenance of aircraft components," Procedia CIRP, 107 (March) 246-251 (2022) doi:10.1016/j.procir.2022.05.197
  33. 33) M.N. Rahman, M.A. Wahid, M.F.M. Yasin, U. Abidin, and M.A. Mazlan, "Predictive numerical analysis on the mixing characteristics in a rotating detonation engine (RDE)," Evergreen, 8 (1) 123-130 (2021) doi:10.5109/4372268
  34. 34) A. Kumar, A.K. Chanda, and S. Angra, "Optimization of stiffness properties of composite sandwich using hybrid taguchi-gra-pca," Evergreen, 8 (2) 310-317 (2021) doi:10.5109/4480708
  35. 35) S. Akhai, P. Srivastava, V. Sharma, and A. Bhatia, "Investigating weld strength of AA8011-6062 alloys joined via friction-stir welding using the rsm approach," J. Phys. Conf. Ser., 1950 (1) (2021) doi:10.1088/1742-6596/1950/1/012016
  36. 36) P. Kumar, V. Sharma, D. Kumar, and S. Akhai, "Morphology and mechanical behavior of friction-stirred aluminum surface composite reinforced with graphene," Evergreen, 10 (1) 105-110 (2023) doi:10.5109/6781056
  37. 37) S. Akhai, and S. John, "Human performance in industrial design centers with small unit air human performance in industrial design centers with small" (January 2016) (2021) doi:10.13140/RG.2.2.21422.23361
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