Cut resistance evaluation of three-dimensional geometry-dependent coated structures by clustering validation analysis
Source abstract
Abstract
Abstract This study evaluates the impact of three-dimensional coating structures on the cut resistance of aramid knitted fabrics. Nine different coating geometries (elliptical, diamond, and stripes) were created using template matrices and tested for cut resistance according to EN ISO 13997. required to cut the fabric. The results showed that the geometric shape significantly influenced cut resistance, ranging from 7.8 N for elliptical patterns to 23.8 N for diamond patterns, which provided the highest level of protection. To support the experimental findings, an unsupervised machine learning approach was employed to provide an objective, data-driven validation of the differences in the coated layers. The application of a modified NbClust decision fusion approach explicitly linked the geometric parameters with the resulting cut resistance, confirming that the surface geometry fundamentally dictates the observed performance clusters. This quantitative analysis provided an objective validation of the grouping of coating structures, confirming that surface geometry is a key determinant influencing the cut resistance of protective textiles.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Textile materials and evaluations · Fiber-reinforced polymer composites · Mechanical Behavior of Composites
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Personal Protective Equipment , Central Institute for Labour Protection-National Research Institute , Czerniakowska 16, 00–701 , Warsaw , Poland
Lodz University of Technology, Institute of Electrical Engineering Systems , Stefanowskiego 18, 90-537 , Lodz , Poland
References (66)
Show source-provided references
1. Statistical Office (GUS), “Accidents at work 2022,” 2022, http://stat.gov.pl/obszary-tematyczne/rynek-pracy/warunki-pracy-wypadki-przy-pracy/ [Accessed: Oct. 13, 2025].
2. Statistical Office (GUS), “Accidents at work 2023,” 2023, http://stat.gov.pl/obszary-tematyczne/rynek-pracy/warunki-pracy-wypadki-przy-pracy/ [Accessed: Oct. 13, 2025].
3. Statistical Office (GUS), “Accidents at work during the period January to September 2024,” 2024, http://stat.gov.pl/obszary-tematyczne/rynek-pracy/warunki-pracy-wypadki-przy-pracy/ [Accessed: Oct 13, 2025].
4. F. Jiang, T. Su, L. Fang, K. Zha, and H. Cong, “Structural design and performance of cut-resistant fabrics with concave–convex arrays,” Polymers, vol. 16, no. 15, p. 2137, 2024, https://doi.org/10.3390/polym16152137.10.3390/polym16152137 ↗
5. Y. Zhai, L. Mao, Y. Shen, and X. Yan, “Research progress of cut-resistant textile materials,” Front Chem, vol. 9, 2021, https://doi.org/10.3389/fchem.2021.745467.10.3389/fchem.2021.745467 ↗
6. M. Messiry and S. Tarfawy, “Effect of weave structure on the slicing cut resistance of woven fabrics,” Text. Res. J., vol. 90, nos. 13–14, 2019, https://doi.org/10.1177/0040517519894393.10.1177/0040517519894393 ↗
7. V. Milašius, R. Milašius, E. Kumpikaitė, and A. Olšauskienė, “Influence of fabric structure on some technological and end–use properties,” Fibres Text East Eur., vol. 2, no. 41, pp. 48–51, 2003.
8. M. Matusiak, “Influence of the structural parameters of woven fabrics on their drapeability,” Fibres Text East Eur., vol. 25, no. 1(121), 2017, https://doi.org/10.5604/12303666.1227883.10.5604/12303666.1227883 ↗
9. M. Tokarska, “Neural model of the permeability features of woven fabrics,” Text. Res. J., vol. 74, no. 12, 2004, https://doi.org/10.1177/004051750407401203.10.1177/004051750407401203 ↗
10. Ł. Frącczak, R. Domagała, P. Zgórniak, and M. Matusiak, “Parameterization of seersucker woven fabrics using laser techniques,” AUTEX Res. J., vol. 19, no. 3, 2019. Available at: https://doi.org/0.1515/aut-2018-0042.10.1515/aut-2018-0042 ↗
11. M. Matusiak and Ł. Frącczak, “Investigation of 3D woven fabric topography using laser-scanning,” Fibres Text East Eur., vol. 26, no. 1, 2018, https://doi.org/10.5604/01.3001.0010.7801.10.5604/01.3001.0010.7801 ↗
12. E. Çeven and G. Günaydın, “Evaluation of some comfort and mechanical properties of knitted fabrics made of different regenerated cellulosic fibres,” Fibers Polym., vol. 22, no. 2, pp. 567–577, 2021, https://doi.org/10.1007/s12221-021-0246-0.10.1007/s12221-021-0246-0 ↗
13. M. F. Lai et al.., “Effects of different structures on the functional and mechanical properties of elastic knitted fabrics,” J. Text. Inst., vol. 113, no. 2, 2021, https://doi.org/10.1080/00405000.2020.1869448.10.1080/00405000.2020.1869448 ↗
14. S. Rebouillat and B. Steffenino, “High performance fibres and the mechanical attributes of cut resistant structures made therewith,” High Perform Struct. Mater., vol. 85, pp. 279–299, 2006, https://doi.org/10.2495/HPSM06028.10.2495/HPSM06028 ↗
15. X. Yan, L. Wu, S. Jin, W. Zhao, H. Cao, and Y. Ma, “Effect of curing conditions on the cutting resistance of fabrics coated with inorganic-powder-reinforced epoxy composite,” RSC Adv., vol. 10, no. 56, 2020, https://doi.org/10.1039/D0RA06386J.10.1039/D0RA06386J ↗
16. N. Panneke and A. Ehrmann, “Stab-resistant polymers – recent developments in materials and structures,” Polymers, vol. 15, no. 4, p. 983, 2023, https://doi.org/10.3390/polym15040983.10.3390/polym15040983 ↗
17. P. Dolez, S. Marsha, and R. McQueen, “Fibers and textiles for personal protective equipment: Review of recent progress and perspectives on future developments,” Textiles, vol. 2, no. 2, 2022, https://doi.org/10.3390/textiles2020020.10.3390/textiles2020020 ↗
18. P. Kropidłowska, E. Irzmańska, E. Korzeniewska, M. Tomczyk, and M. Jurczyk-Kowalska, “Evaluation of laser texturing in fabricating cut-resistant surfaces for protective gloves,” Text. Res. J., vol. 93, nos. 9–10, 2023, https://doi.org/10.1177/00405175221136564.10.1177/00405175221136564 ↗
19. L. Mao, H. Yu, L. Yao, Y. Shen, X. Yan, and Y. Ma, “Cut/puncture resistance and mechanisms of protective composite textiles reinforced with inorganic powder,” Mater. Des., vol. 237, 2024, https://doi.org/10.1016/j.matdes.2023.112575.10.1016/j.matdes.2023.112575 ↗
20. A. Bouasria, A. Nadi, A. Boukhriss, H. Hannache, O. Cherkaoui, and S. Gmouh, “Advances in polymer coating for functional finishing of textiles,” in Frontiers of Textile Materials, S. S Mohd, S Ahmed, and JN Sheikh, Eds., Cham, Springer, 2020.10.1002/9781119620396.ch3 ↗
21. P. Kropidłowska, M. Jurczyk-Kowalska, E. Irzmańska, T. Płociński, and R. Laskowski, “Effects of composite coatings functionalized with material additives applied on textile materials for cut resistant protective gloves,” Materials, vol. 14, no. 22, p. 6876, 2021, https://doi.org/10.3390/ma14226876.10.3390/ma14226876 ↗
22. M. V. M. Potočić and Z. Skenderi, “Mechanical properties of polyurethane coated knitted fabrics,” Fibres Text East Eur, vol. 4, no. 100, pp. 86–91, 2013.
23. J. Mayo, “Stab and puncture characterization of thermoplastic-impregnated aramid fabrics,” Int J Impact Eng, vol. 36, no. 9, 2009, https://doi.org/10.1016/j.ijimpeng.2009.03.006.10.1016/j.ijimpeng.2009.03.006 ↗
24. G. Thilagavathi, K. Rajendrakumar, and T. Kannaian, “Development of textile laminates for improved cut resistance,” J Eng Fiber Fabr, vol. 5, no. 2, 2010, https://doi.org/10.1177/155892501000500205.10.1177/155892501000500205 ↗
25. P. Kropidłowska, E. Irzmańska, and J. Sawicki, “Preliminary experimental investigation of cut-resistant materials: A biomimetic perspective,” AUTEX Res J, vol. 22, no. 4, 2022, https://doi.org/0.2478/aut-2021-0006.10.2478/aut-2021-0006 ↗
26. T. B. Vu, T. Vu-Khanh, and J. Lara, “Mechanics and mechanism of cut resistance of protective materials,” Theor Appl Fract Mech, vol. 52, no. 1, 2009, https://doi.org/10.1016/j.tafmec.2009.06.008.10.1016/j.tafmec.2009.06.008 ↗
27. P. Meyer, C. Döpke, and A. Ehrmann, “Improving adhesion of three-dimensional printed objects on textile fabrics by polymer coating,” J Eng Fiber Fabr, vol. 14, 2019, https://doi.org/10.1177/155892501989525.10.1177/1558925019895257 ↗
28. E. Irzmańska, A. Cichocka, A. K. Puszkarz, O. Olejnik, and P. Kropidłowska, “A new approach to implementing 3D-printed material structures for protective gloves with the use of ultrasonic and contact welding processes: A preliminary study,” Materials, vol. 17, no. 22, p. 5404, 2024, https://doi.org/10.3390/ma17225404.10.3390/ma17225404 ↗
29. T. B. Vu, T. Vu-Khanh, and J. Lara, “Effect of friction on cut resistance of polymers,” J. Thermoplast. Compos. Mater., vol. 18, no. 1, 2005, https://doi.org/10.1177/0892705705041157.10.1177/0892705705041157 ↗
30. V. M. Potočić Matković and Z. Skenderi, “Influence of modification on the structural and tensile properties of polyurethane coated knitted fabrics after natural weathering,” Fibres Text East Eur., vol. 25, no. 4., 2017, https://doi.org/10.5604/01.3001.0010.2812.10.5604/01.3001.0010.2812 ↗
31. C. Aggarwal and C. Reddy, Eds. Data Clustering: Algorithms and Applications, 1st ed., Chapman and Hall/CRC, 2014.
32. S. Wierzchoń and M. Kłopotek, Modern Algorithms of Cluster Analysis, Cham, Springer Int. Publ., 2018.10.1007/978-3-319-69308-8 ↗
33. R. Scitovski, K. Sabo, F. Martínez-Álvarez, and Š. Ungar, Cluster Analysis and Applications, Cham, Springer Int. Publ., 2021.10.1007/978-3-030-74552-3 ↗
34. A. E. Ezugwu et al.., “A comprehensive survey of clustering algorithms,” Eng Appl Artif Intell, vol. 110, 2022, https://doi.org/10.1016/j.engappai.2022.104743.10.1016/j.engappai.2022.104743 ↗
35. I. Sarker, “Machine learning: Algorithms, real-world applications and research directions,” SN Comput Sci, vol. 2, no. 3, p. 160, 2021, https://doi.org/10.1007/s42979-021-00592-x.10.1007/s42979-021-00592-x ↗
36. İ. Yazici, I. Shayea, and J. Din, “A survey of AI/ML applications in future mobile networks-enabled systems,” Eng Sci Technol Int J, vol. 44, 2023, https://doi.org/10.1016/j.jestch.2023.101455.10.1016/j.jestch.2023.101455 ↗
37. G. Oyewole and G. Thopil, “Data clustering: Application and trends,” Artif Intell Rev, vol. 56, pp. 6439–6475, 2023. https://doi.org/10.1007/s10462-022-10325-y.10.1007/s10462-022-10325-y ↗
38. J. Hu, Ed. Computer Technology for Textiles and Apparel, , Oxford, WP, 2011.10.1533/9780857093608 ↗
39. S. Hiremath, Y. Zhang, L. Zhang, and T.-W. Kim, “Machine learning approach to evaluating impact behavior in fabric-laminated composites,” Results Eng, vol. 23, 2024, https://doi.org/10.1016/j.rineng.2024.102576.10.1016/j.rineng.2024.102576 ↗
40. L. de Ferri et al.., “Non-invasive study of natural dyes on historical textiles from the collection of Michelangelo Guggenheim,” Spectrochim Acta. A Mol Biomol. Spectrosc., vol. 204, pp. 548–567, 2018, https://doi.org/10.1016/j.saa.2018.06.026.10.1016/j.saa.2018.06.026 ↗
41. S. Singh, Z. Kamble, and G. Neje, “Textile-reinforced composite structural-health monitoring systems,” Text Prog, vol. 56, no. 4, 2024, https://doi.org/10.1080/00405167.2024.2422749.10.1080/00405167.2024.2422749 ↗
42. V. Daghigh, H. Daghigh, and T. Lacy, “Review of ML applications for defect detection in composite materials,” MLWA, vol. 18, 2024, https://doi.org/10.1016/j.mlwa.2024.100600.10.1016/j.mlwa.2024.100600 ↗
43. F. Lamon, P. A. Carraro, L. Maragoni, and M. Quaresimin, “Multiscale strategy for micro-scale stress distribution,” Compos Part A, vol. 159, 2022, https://doi.org/10.1016/j.compositesa.2022.107026.10.1016/j.compositesa.2022.107026 ↗
44. L. Wang, K. Yu, D. Zhang, and K. Qian, “Cut resistant characteristics of organic high-performance yarns and STF/yarns,” J Ind Text, vol. 49, no. 10, 2020, https://doi.org/10.1177/1528083718811091.10.1177/1528083718811091 ↗
45. I. Abu-Mahfouz, A. Banerjee, and E. Rahman, “Evaluation of clustering techniques to predict surface roughness during turning of stainless-steel using vibration signals,” Materials, vol. 14, no. 17, p. 5050, 2021, https://doi.org/10.3390/ma14175050.10.3390/ma14175050 ↗
46. E. Irzmańska, K. Mizera, N. Litwicka, and K. Sałasińska, “An approach to testing antivandal composite materials as a function of their thickness and striker shape – A case study,” Polymers, vol. 16, no. 5, p. 591, 2024, https://doi.org/10.3390/polym16050591.10.3390/polym16050591 ↗
47. M. Azeem, M. Shahid, I. Masin, and M. Petru, “Design and development of textile-based wearable sensors for real-time biomedical monitoring; a review,” J. Text. Inst., vol. 116, no. 1, 2024, https://doi.org/10.1080/00405000.2024.2318500.10.1080/00405000.2024.2318500 ↗
48. T. Panskyi and V. Mosorov, “A step towards the majority-based clustering validation decision fusion method,” Autom Pomiary Gospod Ochr Sr, vol. 11, no. 2, 2021, https://doi.org/10.35784/iapgos.2596.10.35784/iapgos.2596 ↗
49. B. A. Hassan, N. B. Tayfor, A. A. Hassan, A. M. Ahmed, T. A Rashid, and N. N. Abdalla, “From A-to-Z review of clustering validation indices,” Neurocomputing, vol. 601, 2024, https://doi.org/10.1016/j.neucom.2024.128198.10.1016/j.neucom.2024.128198 ↗
50. T. Ullmann, C. Hennig, and A. Boulesteix, “Validation of cluster analysis results on validation data: A systematic framework,” WIREs Data Min Knowl Discov, vol. 12, no. 3, p. e1444, 2022, https://doi.org/10.1002/widm.1444.10.1002/widm.1444 ↗
51. J. Rojas-Thomas and M. Santos, “New internal clustering validation measure for contiguous arbitrary-shape clusters,” Int J Intell Syst, vol. 36, pp. 5506–5529, 2021, https://doi.org/10.1002/int.22521.10.1002/int.22521 ↗
52. A. Tarekegn, K. Michalak, and M. Giacobini, “Cross-validation approach to evaluate clustering algorithms: An experimental study using multi-label datasets,” SN Comput Sci, vol. 1, no. 263, 2020, https://doi.org/10.1007/s42979-020-00283-z.10.1007/s42979-020-00283-z ↗
53. N. A. Di and V. Catania, “On external measures for validation of fuzzy partitions,” in Foundations of Fuzzy Logic and Soft Computing. LNCS 4529, Springer, 2007.
54. K. Draszawka and J. Szymański, “External validation measures for nested clustering of text documents,” in Emerging Intelligent Technologies in Industry, SCI 369, Springer, 2011.10.1007/978-3-642-22732-5_18 ↗
55. Y. Liu, Z. Li, H. Xiong, X. Gao, J. Wu, and S. Wu, “Understanding and enhancement of internal clustering validation measures,” IEEE Trans Cybern, vol. 43, no. 3, 2013, https://doi.org/10.1109/TSMCB.2012.2220543.10.1109/TSMCB.2012.2220543 ↗
56. X. Gao and M. Yang, “Understanding and enhancement of internal clustering validation indexes for categorical data,” Algorithms, vol. 11, no. 11, p. 177, 2018, https://doi.org/10.3390/a11110177.10.3390/a11110177 ↗
57. L. Vendramin, R. Campello, and E. Hruschka, “Relative clustering validity criteria: A comparative overview,” Stat. Anal. Data Min., vol. 3, pp. 209–235, 2010, https://doi.org/10.1002/sam.10080.10.1002/sam.10080 ↗
58. M. Naldi, A. C. P. L. F. Carvalho, and R.J.G.B Campello, “Cluster ensemble selection based on relative validity indexes,” Data Min. Knowl. Discov., vol. 27, pp. 259–289, 2013. https://doi.org/10.1007/s10618-012-0290-x.10.1007/s10618-012-0290-x ↗
59. O. Arbelaitz, I. Gurrutxaga, J. Muguerza, J. M. Pérez, and I. Perona, “An extensive comparative study of cluster validity indices,” Pattern Recognit., vol. 46, no. 1, 2013, https://doi.org/10.1016/j.patcog.2012.07.021.10.1016/j.patcog.2012.07.021 ↗
60. M. Gagolewski, M. Bartoszuk, and A. Cena, “Are cluster validity measures (in) valid?,” Inf Sci, vol. 581, pp. 620–636, 2021, https://doi.org/10.1016/j.ins.2021.10.004.10.1016/j.ins.2021.10.004 ↗
61. I. Gurrutxaga, J. Muguerza, O. Arbelaitz, J. M. Pérez, and J. I. Martín, “Towards a standard methodology to evaluate internal cluster validity indices,” Pattern Recognit Lett, vol. 32, no. 3, 2011, https://doi.org/10.1016/j.patrec.2010.11.006.10.1016/j.patrec.2010.11.006 ↗
62. A. Yera, O. Arbelaitz, J. L. Jodra, I. Gurrutxaga, J. M. Pérez, and J. Muguerza, “Analysis of several decision fusion strategies for clustering validation. Strategy definition, experiments and validation,” Pattern Recognit Lett, vol. 85, pp. 42–48, 2017, https://doi.org/10.1016/j.patrec.2016.11.009.10.1016/j.patrec.2016.11.009 ↗
63. K. Kryszczuk and P. Hurley, “Estimation of the number of clusters using multiple clustering validity indices,” in Multiple Classifier Syst. LNCS 5997, Springer, 2010.10.1007/978-3-642-12127-2_12 ↗
64. S. Rothe, B. Kudszus, and D. Söffker, “Does classifier fusion improve the overall performance? Numerical analysis of data and fusion method characteristics influencing classifier fusion performance,” Entropy, vol. 21, no. 9, p. 866, 2019, https://doi.org/10.3390/e21090866.10.3390/e21090866 ↗
65. M. Charrad, N. Ghazzali, V. Boiteau, and A. Niknafs, “NbClust: An R package for determining the relevant number of clusters in a data set,” J Stat Softw, vol. 61, no. 6, 2014, https://doi.org/10.18637/jss.v061.i06.10.18637/jss.v061.i06 ↗
66. T. Panskyi, E. Korzeniewska, A. Firych-Nowacka. 2024. Educational data clustering in secondary school sensor-based engineering courses using active learning approaches. Appl. Sci., vol. 14, no. 12, p. 5071. https://doi.org/10.3390/app14125071.10.3390/app14125071 ↗