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A method for analyzing the texture features of free-form surface polishing paths based on co-occurrence matrix

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Abstract

Surface quality analysis of polished surface has been the subject of many classic studies in surface polishing technology and is a key indicator for evaluating the polishing path. However, as an important part of surface quality, surface texture features have been ignored in many related researches. In this paper, a new method to analyze the texture features of polishing based on co-occurrence matrix is proposed. It provides a new perspective of surface quality analysis focusing on surface texture features. It extends the previous approach termed the residual level co-occurrence matrix (RLCM) focusing on the distribution of surface polishing residues, leading to more targeted and stabilized evaluation results. This method can be used in the path planning stage to estimate the polishing quality of the planned path without physical processing, which can avoid resource waste in the physical world. Furthermore, in this method, the usage of images is avoided, which can ensure that the results are not affected by the light and image quality. Simulation experiments as well as empirical investigations were conducted to verify the feasibility of the method. The results of both consistently reveal that the proposed method is able to accurately describe the texture feature and correctly analyze the texture feature.

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All data generated or analyzed during this study are included in this published article.

References

  1. Sun Y, Jia J, Xu J, Chen M, Niu J (2022) Path, feedrate and trajectory planning for free-form surface machining: a state-of-the-art review. Chin J Aeronaut 35(8):12–29

    Article  Google Scholar 

  2. Grzesik W, Rech J, Żak K (2015) Characterization of surface textures generated on hardened steel parts in high-precision machining operations. Int J Adv Manuf Technol 78:2049–2056

    Article  Google Scholar 

  3. Xu J, Xu L, Geng Z, Sun Y, Tang K (2020) 3D surface topography simulation and experiments for ball-end CNC milling considering dynamic feedrate. CIRP J Manuf Sci Technol 31:210–223

    Article  Google Scholar 

  4. Wang Z, Lin X, Shi Y, Zhang Y, Chen Z (2020) Reducing roughness of freeform surface through tool orientation optimization in multi-axis polishing of blisk. Int J Adv Manuf Technol 108:917–929

    Article  Google Scholar 

  5. Liu W, Tu X, Jia Z, Wang W, Ma X, Bi X (2013) An improved surface roughness measurement method for micro-heterogeneous texture in deep hole based on gray-level co-occurrence matrix and support vector machine. Int J Adv Manuf Technol 69:583–593

    Article  Google Scholar 

  6. Grigoriev AY, Myshkin NK (2015) Comparing Surface Roughness and Texture Concepts. In: Proceedings of BALTTRIB’ 6(1):6–69

  7. Menezes PL, Kishore KSV, Lovell MR (2011) Role of surface texture, roughness, and hardness on friction during unidirectional sliding. Tribol Lett 41:1–15

    Article  Google Scholar 

  8. Zhang KS, Liu K, Gao TY, Qiao YL, Zhang Y, Liu XJ, Wang W, Ye JX (2021) The unrecognized importance of roughness directionality to polymer wear. Wear 486–487(6):204084

  9. Hamdavi S, Ya HH, Rao TVVLN (2016) Effect of surface texturing on hydrodynamic performance of journal bearings. J Eng Appl Sci 11:172–176

    Google Scholar 

  10. Yayoglu YE, Toomey RG, Crane NB, Gallant ND (2022) Laser machined micropatterns as corrosion protection of both hydrophobic and hydrophilic magnesium. J Mech Behav Biomed Mater 125:104920

  11. Hladnik A, Lazar M (2011) Paper and board surface roughness characterization using laser profilometry and gray level cooccurrence matrix. Nord Pulp Pap Res J 26:99–105

    Article  Google Scholar 

  12. Zhang J, Tan T (2002) Brief review of invariant texture analysis methods. Pattern Recogn 35:735–747

    Article  MATH  Google Scholar 

  13. Peckinpaugh SH (1991) An improved method for computing gray-level cooccurrence matrix based texture measures. CVGIP Graph Mod Image Process 53:574–580

  14. Soh LK, Tsatsoulis C (1999) Texture analysis of sar sea ice imagery using gray level co-occurrence matrices. IEEE Trans Geosci Remote Sens 37:780–795

    Article  Google Scholar 

  15. Chopra S, Marfurt KJ (2007) Volumetric curvature attributes adding value to 3D seismic data interpretation. Society of Exploration Geophysicists - 77th SEG International Exposition and Annual Meeting. SEG 28:851–855

  16. Dutta S, Pal SK, Mukhopadhyay S, Sen R (2013) Application of digital image processing in tool condition monitoring: a review. CIRP J Manuf Sci Technol 6:212–232

    Article  Google Scholar 

  17. Gadelmawla ES (2004) A vision system for surface roughness characterization using the gray level co-occurrence matrix. NDT E Int 37:577–588

    Article  Google Scholar 

  18. Huaian YI, Jian LIU, Enhui LU, Peng AO (2016) Measuring grinding surface roughness based on the sharpness evaluation of colour images. Meas Sci Technol 27:25404

    Article  Google Scholar 

  19. Sun H, Gao D, Zhao Z, Tang X (2017) An approach to in-process surface texture condition monitoring. Robot Comput Integr Manuf 48:254–262

    Article  Google Scholar 

  20. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3:610–621

  21. Bo H, Ao L (2006) Research on computation of GLCM of image texture. Acta Electron Sin 31:155–158+134

  22. Srivastava D, Rajitha B, Agarwal S, Singh S (2020) Pattern-based image retrieval using GLCM. Neural Comput Appl 32:10819–10832

    Article  Google Scholar 

  23. Chen ML, Dai SK (2012) Analysis on image texture based on gray-level co-occurrence matrix. Comp Technol 45(2):108–111

  24. Conners RW, Harlow CA (1980) A theoretical comparison of texture algorithms. IEEE Trans Pattern Anal Mach Intell PAMI-2:204–222

  25. Xu JT, Xu LK, Li YF, Sun YW (2020) Shape-adaptive CNC milling for complex contours on deformed thin-walled revolution surface parts. J Manuf Process 59:760–771

    Article  Google Scholar 

  26. Sawhney R, Crane K (2017) Boundary first flatening. ACM Trans Graph 37(1):5:1–5:14

  27. Neyrinck A, Verl A (2012) Optimale Maschinen und Anlagen durch Simulation von Varianten in der Konzeptionsphase. Automation 2012

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Funding

This study was supported in part by grants from the National Defense Basic Scientific Research Program of China (grant no. JCKY2020210C002) and the National Natural Science Foundation of China—Liaoning Provincial Joint Fund (grant no. U1908230).

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JiaXuan Li performed the algorithm design and code writing and was a major contributor in writing the manuscript. The above work was completed under the guidance of Bo Zhou and Lun Li. Guang Zhu and Cai Ming analyzed and interpreted the experimental data. All authors read and approved the final manuscript.

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Correspondence to Bo Zhou.

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Li, J.X., Zhou, B., Li, L. et al. A method for analyzing the texture features of free-form surface polishing paths based on co-occurrence matrix. Int J Adv Manuf Technol 124, 601–618 (2023). https://doi.org/10.1007/s00170-022-10401-1

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