Research PaperNo Access

Fractal-Based Analysis of the Relation Between Surface Finish and Machine Vibration in Milling Operation

Muhammad Owais Qadri

Department of Mechanical Engineering, School of Engineering, Monash University, Selangor, Malaysia

Search for more papers by this author

and

Hamidreza Namazi

http://orcid.org/0000-0001-7178-455X

Department of Mechanical Engineering, School of Engineering, Monash University, Selangor, Malaysia

E-mail Address: hrn_mechanic@yahoo.com

Search for more papers by this author

https://doi.org/10.1142/S0219477520500066Cited by:14 (Source: Crossref)

Abstract

Analysis of surface quality of machined workpiece is an important issue in machining of materials. For this purpose, scientists analyze how the texture of machined surface changes due to different conditions. Machine vibration is one of the factors that highly affects the surface quality of machined surface. In this research, we analyze the relation between machine vibration and surface quality of machined workpiece. For this purpose, we employ fractal theory and analyze how the complex structure of machined surface changes with the complex structure of machine vibration signal in case of variations of machining parameters, namely, depth of cut, feed rate and spindle speed, in milling operation. Based on the results, variations of surface quality of machined workpiece are related with the variations of complexity of machine vibration signal. The method of analysis employed in this research can be applied to other machining operations in order to find the relation between machine vibration and surface quality of machined workpiece.

Communicated by Robert Vajtai

Keywords:

References

1. H. Namazi , Genetic algorithm based optimization of cutting parameters in drilling of composite materials, ASME 2010 Int. Mechanical Engineering Congress and Exposition (Vancouver, British Columbia, Canada, 2010), pp. 173–178. Crossref, Google Scholar
2. G. Zhang et al., Prediction of surface roughness in end face milling based on Gaussian process regression and cause analysis considering tool vibration, Int. J. Adv. Manuf. Technol. 75 (2014) 1357, https://doi.org/10.1007/s00170-014-6232-6. Crossref, Web of Science, Google Scholar
3. S. S. Bhogal, C. Sindhu, S. S. Dhami and B. S. Pabla , Minimization of surface roughness and tool vibration in CNC milling operation, J. Optim. 2015 (2015) 192030, https://doi.org/10.1155/2015/192030. Web of Science, Google Scholar
4. U. C. Okonkwo, I. P. Okokpujie, J. E. Sinebe and C. A. K. Ezugwu , Comparative analysis of aluminium surface roughness in end-milling under dry and minimum quantity lubrication (MQL) conditions, Manuf. Rev. 2(30) (2015) 1–11, https://doi.org/10.1051/mfreview/2015033. Google Scholar
5. A. B. Abdullah, L. Y. Chia and Z. Samad , The effect of feed rate and cutting speed to surface roughness, Asian J. Sci. Res. 1(1) (2008) 12–21. Crossref, Google Scholar
6. N. Kumar and P. Kumar , Effect of milling parameters on surface roughness and dry friction: An experimental and modeling study, Am. J. Mech. Ind. Eng. 1(3) (2016) 64–69. Google Scholar
7. V. K. Mall, P. Kumar and B. Singh , A review of optimization of surface roughness of inconel 718 in end milling using Taguchi method, Int. J. Eng. Res. Appl. 4(12) (2014) 103–109. Google Scholar
8. J. V. Abellan-Nebot, G. M. Bruscas, J. Serrano and C. Vila , Portability study of surface roughness models in milling, Procedia Manuf. 13 (2017) 593–600. Crossref, Google Scholar
9. S. Shajari, M. H. Sadeghi and H. Hassanpour , The influence of tool path strategies on cutting force and surface texture during ball end milling of low curvature convex surfaces, Sci. World J. 2014 (2014) 374526, https://doi.org/10.1155/2014/374526. Crossref, Web of Science, Google Scholar
10. D. Constantine, S. Dimitrios, S. Evlampia, T. Christos and T. Ioannis , Experimental analysis of the effect of vibration phenomena on workpiece topomorphy due to cutter runout in end-milling process, Mach. 6(3) (2018) 27, https://doi.org/10.3390/machines6030027. Crossref, Web of Science, Google Scholar
11. N. M. Trivedi and J. R. Mevada , Improvement of surface finish by vibration control in machine tool using composite material, Int. J. Eng. Res. Appl. 4(4) (2014) 17–23. Google Scholar
12. V. Ostasevicius, R. Gaidys, R. Dauksevicius and S. Mikuckyte , Study of vibration milling for improving surface finish of difficult-to-cut materials, J. Mech. Eng. 59(6) (2013) 351–357. Crossref, Web of Science, Google Scholar
13. H. S. Jakeer and J. Srinivas , Optimal selection of operating parameters in end milling of Al-6061 work materials using multi-objective approach, Mech. Adv. Materi. Mod. Process. 3 (2017) 5, https://doi.org/10.1186/s40759-017-0020-6. Crossref, Google Scholar
14. T. Y. Wu and K. W. Lei , Prediction of surface roughness in milling process using vibration signal analysis and artificial neural network, Int. J. Adv. Manuf. Technol. 102 (2019) 305–314, https://doi.org/10.1007/s00170-018-3176-2. Crossref, Web of Science, Google Scholar
15. A. Logins, T. Torims, P. R. Castellano, S. Gutiérrez and R. Torres , Vibration analysis of high-speed end milling operations applied to injection mold materials, ASME 2017 Int. Mechanical Engineering Congress and Exposition. Tampa, Florida, USA, November 3–9, (2017), https://doi.org/10.1115/IMECE2017-72636. Crossref, Google Scholar
16. G. P. Babu, B. Murthy, K. Venkatarao and C. Ratnam , Multi-response optimization in orthogonal turn milling by analyzing tool vibration and surface roughness using response surface methodology, Proc. Inst. Mech. Eng., Part B: J. Eng. Manuf. 231(12) (2017) 2084–2093, https://doi.org/10.1177/0954405415624349. Crossref, Web of Science, Google Scholar
17. M. M. de Aguiar, A. E. Diniz and R. Pederiva , Correlating surface roughness, tool wear and tool vibration in the milling process of hardened steel using long slender tools, Int. J. Mach. Tools and Manuf. 68 (2013) 1–10, https://doi.org/10.1016/j.ijmachtools.2013.01.002. Crossref, Web of Science, Google Scholar
18. K. Bissoonauth and H. Namazi , Complexity based decoding of the effect of machining parameters on the machined surface in milling operation, Fractals (2019), https://doi.org/10.1142/S0218348X19500762. Link, Web of Science, Google Scholar
19. Y.-T. Sheen , A complex filter for vibration signal demodulation in bearing defect diagnosis, J. Sound Vib. 276(1–2) (2004) 105–119. Crossref, Web of Science, Google Scholar
20. R. H. Namazi , Fractal-based analysis of the influence of music on human respiration, Fractals 25 (2017) 1750059, https://doi.org/10.1142/S0218348X17500591. Link, Web of Science, Google Scholar
21. H. Namazi , Fractal based classification of Electromyography (EMG) signal in response to basic movements of the fingers, Fractals 27(3) (2019) 1950037, https://doi.org/10.1142/S0218348X19500373. Link, Web of Science, Google Scholar
22. H. Alipour, H. Namazi, H. Azarnoush and S. Jafari , Complexity-based analysis of the relation between moving visual stimuli and human eye movement, Fractals 27(3) (2019) 1950024, https://doi.org/10.1142/S0218348X19500245. Link, Web of Science, Google Scholar
23. H. Namazi , Decoding of hand gestures by fractal analysis of Electromyography (EMG) signal, Fractals 27(3) (2019) 1950022, https://doi.org/10.1142/S0218348X19500221. Link, Web of Science, Google Scholar
24. H. Namazi and S. Jafari , Estimating of brain development in newborns by fractal analysis of sleep Electroencephalographic (EEG) signal, Fractals 27(3) (2019) 1950021, https://doi.org/10.1142/S0218348X1950021X. Link, Web of Science, Google Scholar
25. H. Alipour, A. Menon, H. Namazi, F. Towhidkhah and S. Jafari , Complexity-based analysis of the relation between fractal visual stimuli and fractal eye movements, Fluct. Noise Lett. 18(3) (2019) 1950012 (14 pages), https://doi.org/10.1142/S0219477519500123. Link, Web of Science, Google Scholar
26. H. Namazi, E. Aghasian and T. S. Ala , Fractal based classification of Electroencephalography (EEG) signals from healthy adolescents and adolescents with symptoms of schizophrenia, Technol. Health Care (2019), https://doi.org/10.3233/THC-181497. Crossref, Web of Science, Google Scholar
27. H. Namazi and S. Jafari , Decoding of wrist movements’ direction by fractal analysis of Magnetoencephalography (MEG) signal, Fractals 27(2) (2019) 1950001, https://doi.org/10.1142/S0218348X19500014. Link, Web of Science, Google Scholar
28. H. Namazi et al., Analysis of the influence of memory content of auditory stimuli on the memory content of EEG signal, Oncotarget. 7 (2016) 56120–56128, https://doi.org/10.18632/oncotarget.11234. Crossref, Web of Science, Google Scholar
29. H. Namazi, T. S. Ala and H. Bakardjian , Decoding of steady-state visual evoked potentials by fractal analysis of the Electroencephalographic (EEG) signal, Fractals 26(6) (2018) 1850092, https://doi.org/10.1142/S0218348X18500925. Link, Web of Science, Google Scholar
30. J. M. Alipour, R. Khosrowabadi and H. Namazi , Fractal-based analysis of the influence of variations of rhythmic patterns of music on human brain response, Fractals (2018), https://doi.org/10.1142/S0218348X18500809. Link, Web of Science, Google Scholar
31. H. Namazi et al., The fractal based analysis of human face and DNA variations during aging, Biosci. Trends. 10 (2016), https://doi.org/10.5582/bst.2016.01182. Crossref, Web of Science, Google Scholar
32. H. Namazi, V. V. Kulish, F. Delaviz and A. Delaviz , Diagnosis of skin cancer by correlation and complexity analyses of damaged DNA, Oncotarget 6(40) (2015) 42623–31, https://doi.org/10.18632/oncotarget.6003. Crossref, Google Scholar
33. H. Alipour, H. Namazi, H. Azarnoush and S. Jafari , Complexity-based analysis of the influence of visual stimulus color on human eye movement, Fractals 27(2) (2019) 1950002, https://doi.org/10.1142/S0218348X19500026. Link, Web of Science, Google Scholar
34. H. Namazi et al., A signal processing based analysis and prediction of seizure onset in patients with epilepsy, Oncotarget 7 (2016) 342–350, https://doi.org/10.18632/oncotarget.6341. Crossref, Google Scholar
35. M. A. Ahmadi-Pajouh, T. S. Alaa, F. Zamanian, H. Namazi and S. Jafari , Fractal-based classification of human brain response to living and non-living visual stimuli, Fractals 26(5) (2018) 1850069, https://doi.org/10.1142/S0218348X1850069X. Link, Web of Science, Google Scholar
36. M. Mozaffarilegha, H. Namazi, A. A. Tahaei and S. Jafari , Complexity-based analysis of the difference between normal subjects and subjects with stuttering in speech evoked auditory brainstem response, J. Med. Biol. Eng. (2018), https://doi.org/10.1007/s40846-018-0430-x. Crossref, Web of Science, Google Scholar
37. H. Namazi and M. Kiminezhadmalaie , Diagnosis of lung cancer by fractal analysis of damaged DNA, Comput. Math. Methods Med. (2015), https://doi.org/10.1155/2015/242695. Crossref, Web of Science, Google Scholar
38. H. Namazi, A. Akrami, R. Haghighi, A. Delaviz and V. V. Kulish , Analysis of the influence of element’s entropy on the bulk metallic glass (BMG) entropy, complexity and strength, Metall. Mater. Trans. A (2016), https://doi.org/10.1007/s11661-016-3870-3. Web of Science, Google Scholar
39. H. Namazi and S. Jafari , Age-based variations of fractal structure of EEG signal in patients with epilepsy, Fractals 26(4) (2018) 1850051, https://doi.org/10.1142/S0218348X18500512. Link, Web of Science, Google Scholar
40. M. Mozaffarilegha, H. R. Namazi, M. Ahadi and S. Jafari , Complexity-based analysis of the difference in speech-evoked Auditory Brainstem Responses (s-ABRs) between binaural and monaural listening conditions, Fractals (2018), https://doi.org/10.1142/S0218348X18500524. Link, Web of Science, Google Scholar
41. H. Namazi, A. Daneshi, T. Shirzadian, H. Azarnoush and S. Jafari , Information based analysis of the relation between visual stimuli and human eye movements, Fluct. Noise Lett. (2018), https://doi.org/10.1142/S021947751950010X. Web of Science, Google Scholar
42. H. Namazi and S. Jafari , Decoding of simple hand movements by fractal analysis of Electromyography (EMG) signal, Fractals 27(4) (2019) 1950042 (9 pages), https://doi.org/10.1142/S0218348X19500427. Link, Web of Science, Google Scholar
43. H. Namazi and T. S. Ala , Decoding of simple and compound limb motor imagery movements by fractal analysis of Electroencephalogram (EEG) signal, Fractals 27(3) (2019) 1950041, https://doi.org/10.1142/S0218348X19500415. Link, Web of Science, Google Scholar
44. H. Namazi , Fractal-based classification of Electromyography (EMG) signal between fingers and hand’s basic movements, functional movements, and force patterns, Fractals 27(4) (2019) 1950050 (8 pages), https://doi.org/10.1142/S0218348X19500506. Link, Web of Science, Google Scholar
45. H. Namazi, A. Akhavan Farid and C. T. Seng , Fractal-based analysis of the variations of cutting forces along different axes in end milling operation, Fractals 26(6) (2018) 1850089, https://doi.org/10.1142/S0218348X18500895. Link, Web of Science, Google Scholar
46. H. Namazi, A. Akhavan Farid and C. T. Seng , Complexity-based analysis of the influence of tool geometry on cutting forces in rough end milling, Fractals 26(5) (2018) 1850078, https://doi.org/10.1142/S0218348X18500780. Link, Web of Science, Google Scholar
47. H. Namazi, A. Akhavan Farid and C. T. Seng , Fractal-based analysis of the influence of cutting depth on complex structure of cutting forces in rough end milling, Fractals 26(5) (2018) 1850068, https://doi.org/10.1142/S0218348X18500688. Link, Web of Science, Google Scholar
48. G. Jithmal Pathiranagama and H. Namazi , Fractal based analysis of the effect of machining parameters on surface finish of workpiece in turning operation, Fractals 27(4) (2019) 1950043 (8 pages), https://doi.org/10.1142/S0218348X19500439. Link, Web of Science, Google Scholar
49. H. Namazi, A. Akhavan Farid and C. T. Seng , Decoding of the relation between fractal structure of cutting force and surface roughness of machined workpiece in end milling operation, Fractals 27(4) (2019) 1950054 (9 pages). Link, Web of Science, Google Scholar
50. A. T. Thasthakeer, H. Namazi, A. Akhavan Farid and C. T. Seng , Analysis of the correlation between fractal structure of cutting force signal and surface roughness of machined workpiece in end milling operation, Fractals 27(2) (2019) 1950013, https://doi.org/10.1142/S0218348X19500130. Link, Web of Science, Google Scholar
51. A. Akhavan Farid, S. Sharif and H. Namazi , Effect of machining parameters and cutting edge geometry on surface integrity when drilling and hole making in inconel 718, SAE Int. J. Mater. Manuf. 2 (2009) 564–569, https://doi.org/10.4271/2009-01-1412. Crossref, Google Scholar
52. H. Namazi, T. S. Ala and V. Kulish , Decoding of upper limb movement by fractal analysis of Electroencephalogram (EEG) signal, Fractals 26(5) (2018) 1850081, https://doi.org/10.1142/S0218348X18500810. Link, Web of Science, Google Scholar
53. H. Namazi, A. Daneshi, H. Azarnoush, S. Jafari and F. Towhidkhah , Fractal-based analysis of the influence of auditory stimuli on eye movements, Fractals 26(3) (2018) 1850040, https://doi.org/10.1142/S0218348X18500408. Link, Web of Science, Google Scholar
54. H. Alipour, H. Namazi, H. Azarnoush and S. Jafari , Fractal-based analysis of the influence of color tonality on human eye movements, Fractals 27(3) (2019) 1950040, https://doi.org/10.1142/S0218348X19500403. Link, Web of Science, Google Scholar
55. H. Namazi and V. V. Kulish , Fractional diffusion based modelling and prediction of human brain response to external stimuli, Comput. Math. Methods Med. 2015 (2015) 148534, https://doi.org/10.1155/2015/148534. Crossref, Web of Science, Google Scholar
56. H. Namazi and V. V. Kulish , Mathematical modeling of human brain neuronal activity in the absence of external stimuli, J. Med. Imaging Health Info. 2(4) (2012) 400–407. Crossref, Web of Science, Google Scholar
57. K. Seetharaman, H. Namazi and V. V. Kulsih , Phase lagging model of brain response to external stimuli — modeling of single action potential, Comput. Biol. Med. 42 (2012) 857–862. Crossref, Web of Science, Google Scholar
58. H. Namazi and V. V. Kulish , A mathematical based definition of human consciousness, Mathematics in Engineering, Science & Aerospace (MESA) 3(2) (2012) 189–198. Google Scholar
59. H. Namazi and V. V. Kulish , A mathematical based calculation of a myelinated segment in axons, Comput. Biol. Med. 43(6) (2013) 693–8, https://doi.org/10.1016/j.compbiomed.2013.03.005. Crossref, Web of Science, Google Scholar
60. H. Namazi, V. V. Kulish, A. Wong and S. Nazeri , Mathematical based calculation of drug penetration depth in solid tumors, Biomed. Res. Int. 2016 (2016) 8437247, https://doi.org/10.1155/2016/8437247. Crossref, Web of Science, Google Scholar
61. U. S. Vevek, H. Namazi, R. Haghighi and V. V. Kulish , Analysis & validation of exact solutions to Navier-Stokes equation in connection with quantum fluid dynamics, Math. Eng. Science & Aerospace (MESA) 7(2) (2016) 389–417. Google Scholar