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Surface roughness prediction as a classification problem using support vector machine

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Abstract

In order to achieve a high level of product quality, it is imperative to gain a high degree of predictability especially in automated manufacturing setup. Surface finish is one of the most important measures for determining the quality of products in machining. Therefore, accurate predictive models for surface finish are needed. This paper utilizes vibration signals that are experimentally obtained during the end milling of aluminum plates at different cutting conditions. Several features are extracted by processing the acquired signals in both the time and frequency domains. The feature sets include statistical parameters, fast Fourier transforms (FFT) spectra, and the wavelet packets. This work introduces a classifier based on a support vector machine to analyze the set of features in order to predict the type of surface finish. Experiments are conducted for three different types of kernels and parameter configurations. One objective is to examine the effect of feature reduction on the performance of the proposed classifier using three different feature selection algorithms. Another objective is to compare the results with k-nearest neighbor, decision tree, and random forest classifiers. The results show the effectiveness of feature reduction and support vector machine in the success of the proposed classifier.

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References

  1. Marinescu ID (2002) Handbook of machine tool analysis. World Wide Web Internet Web Inf Syst. doi:10.1201/9780203909201

    Google Scholar 

  2. Abainia S, Ouelaa N (2015) Experimental study of the combined influence of the tool geometry parameters on the cutting forces and tool vibrations. Int J Adv Manuf Technol 79:1127–1138. doi:10.1007/s00170-015-6885-9

    Article  Google Scholar 

  3. Gadelmawla ES, Koura MM, Maksoud TMA et al (2002) Roughness parameters. J Mater Process Technol 123:133–145. doi:10.1016/S0924-0136(02)00060-2

    Article  Google Scholar 

  4. Wang W, Kweon SH, Yang SH (2005) A study on roughness of the micro-end-milled surface produced by a miniatured machine tool. J. Mater. Process. Technol, In, pp 702–708

    Google Scholar 

  5. Xi X, Ding W, Li Z, Xu J (2016) High speed grinding of particulate reinforced titanium matrix composites using a monolayer brazed cubic boron nitride wheel. Int J Adv Manuf Technol 1–10. doi:10.1007/s00170-016-9493-4

  6. Ozcelik B, Bayramoglu M (2006) The statistical modeling of surface roughness in high-speed flat end milling. Int J Mach Tools Manuf 46:1395–1402. doi:10.1016/j.ijmachtools.2005.10.005

    Article  Google Scholar 

  7. Zhang G, Li J, Chen Y et al (2014) Prediction of surface roughness in end face milling based on Gaussian process regression and cause analysis considering tool vibration. Int J Adv Manuf Technol 75:1357–1370. doi:10.1007/s00170-014-6232-6

    Article  Google Scholar 

  8. Lou MS, Chen JC, Li CM (1998) Surface roughness prediction technique for CNC end-milling. J Ind Technol 15:1–6

    Google Scholar 

  9. Verma AK, Holcomb SL, Blessner P et al (2003) Parametric study of surface finish in end milling using robust design techniques. Innov Appl Res Mech Eng Technol 2003. doi:10.1115/IMECE2003-42158

  10. Chen CC, Liu NM, Chiang KT, Chen HL (2012) Experimental investigation of tool vibration and surface roughness in the precision end-milling process using the singular spectrum analysis. Int J Adv Manuf Technol 63:797–815. doi:10.1007/s00170-012-3943-4

    Article  Google Scholar 

  11. Fu S, Muralikrishnan B, Raja J (2003) Engineering surface analysis with different wavelet bases. ASME. J. Manuf. Sci. Eng. American Society of Mechanical Engineers, In, pp 844–852

    Google Scholar 

  12. Yang J-Y, Yoon M-C (2011) Machined surface generation using wavelet filtering. J Mech Sci Technol 25:639–645. doi:10.1007/s12206-011-0113-9

    Article  Google Scholar 

  13. Gologlu C, Arslan Y (2009) Zigzag machining surface roughness modelling using evolutionary approach. J Intell Manuf 20:203–210. doi:10.1007/s10845-008-0222-1

    Article  Google Scholar 

  14. Prakasvudhisarn C, Kunnapapdeelert S, Yenradee P (2009) Optimal cutting condition determination for desired surface roughness in end milling. Int J Adv Manuf Technol 41:440–451. doi:10.1007/s00170-008-1491-8

    Article  Google Scholar 

  15. Ramesh R, Ravi Kumar KS, Anil G (2009) Automated intelligent manufacturing system for surface finish control in CNC milling using support vector machines. Int J Adv Manuf Technol 42:1103–1117. doi:10.1007/s00170-008-1676-1

    Article  Google Scholar 

  16. Salgado DR, Alonso FJ, Cambero I, Marcelo A (2009) In-process surface roughness prediction system using cutting vibrations in turning. Int J Adv Manuf Technol 43:40–51. doi:10.1007/s00170-008-1698-8

    Article  Google Scholar 

  17. Kalidass S, Palanisamy P (2014) Prediction of surface roughness for AISI 304 steel with solid carbide tools in end milling process using regression and ANN models. Arab J Sci Eng 39:8065–8075. doi:10.1007/s13369-014-1346-6

    Article  Google Scholar 

  18. Mahesh G, Muthu S, Devadasan SR (2015) Prediction of surface roughness of end milling operation using genetic algorithm. Int J Adv Manuf Technol 77:369–381. doi:10.1007/s00170-014-6425-z

    Article  Google Scholar 

  19. He K, Xu Q, Jia M (2014) Modeling and predicting surface roughness in hard turning using a Bayesian inference-based HMM-SVM model. IEEE Trans Autom Sci Eng 12:1092–1103. doi:10.1109/TASE.2014.2369478

    Article  Google Scholar 

  20. Benardos PG, Vosniakos GC (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43:833–844. doi:10.1016/S0890-6955(03)00059-2

    Article  Google Scholar 

  21. Samanta B (2009) Surface roughness prediction in machining using soft computing. Int J Comput Integr Manuf 22:257–266. doi:10.1080/09511920802287138

    Article  Google Scholar 

  22. Sharkawy AB, El-Sharief MA, Soliman M-ES (2013) Surface roughness prediction in end milling process using intelligent systems. Int J Mach Learn Cybern 5:135–150. doi:10.1007/s13042-013-0155-7

    Article  Google Scholar 

  23. MATLAB, The Math Works, Inc., Natick, MA

  24. Blatter C (1998) Wavelets : a primer. A.K. Peters

    MATH  Google Scholar 

  25. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proc. fifth Annu. Work. Comput. Learn. theory—COLT ‘92. ACM Press, New York, New York, USA, pp 144–152

  26. Informatik F, Joachims T (1998) Text Categorization with Suport Vector Machines: Learning with Many Relevant Features. In: Proc. 10th Eur. Conf. Mach. Learn. ECML ‘98. Springer Berlin Heidelberg, pp 137–142

  27. Pang B, Lee L, Rd H, Jose S (2002) Thumbs up? Sentiment classification using machine learning techniques. Language (Baltim) 79–86

  28. Furey TS, Cristianini N, Duffy N et al (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16:906–914. doi:10.1093/bioinformatics/16.10.906

    Article  Google Scholar 

  29. Jiang Z, Fu H, Li L (2005) Support vector machine for mechanical faults classification. J Zhejiang Univ Sci CN 6A:433–439. doi:10.1631/jzus.2005.A0433

    Article  Google Scholar 

  30. Sun J, Rahman M, Wong Y, Hong G (2004) Multiclassification of tool wear with support vector machine by manufacturing loss consideration. Int J Mach Tools Manuf 44:1179–1187. doi:10.1016/j.ijmachtools.2004.04.003

    Article  Google Scholar 

  31. Ding F, He Z, Zi Y, et al (2008) Application of support vector machine for equipment reliability forecasting. In: IEEE Int. Conf. Ind. Informatics. IEEE, pp 526–530

  32. Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6:37–66

    Google Scholar 

  33. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13:21–27. doi:10.1109/TIT.1967.1053964

    Article  MATH  Google Scholar 

  34. Dudani SA (1976) The distance-weighted k-nearest-neighbor rule. IEEE Trans Syst Man Cybern SMC-6:325–327. doi:10.1109/TSMC.1976.5408784

    Article  Google Scholar 

  35. Breiman L (1993) Classification and regression trees. Chapman & Hall

  36. Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106

    Google Scholar 

  37. Quinlan JR John R (1993) C4.5: Programs for machine learning. Morgan Kaufmann series in machine learning, Morgan Kaufmann Publishers Inc., San Mateo, CA, USA

  38. Tin Kam Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20:832–844. doi:10.1109/34.709601

    Article  Google Scholar 

  39. Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  40. Hall M, Frank E, Holmes G et al (2009) The WEKA data mining software: an update. SIGKDD Explor 11:10–18. doi:10.1145/1656274.1656278

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Science, Engineering and Technology, The Pennsylvania State University at Harrisburg, 777 West Harrisburg Pike, W239 Olmsted Bldg., Middletown, PA, 17057, USA

    Issam Abu-Mahfouz, Omar El Ariss, A. H. M. Esfakur Rahman & Amit Banerjee

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  1. Issam Abu-Mahfouz
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  2. Omar El Ariss
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  3. A. H. M. Esfakur Rahman
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  4. Amit Banerjee
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Correspondence to Issam Abu-Mahfouz.

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Abu-Mahfouz, I., El Ariss, O., Esfakur Rahman, A.H.M. et al. Surface roughness prediction as a classification problem using support vector machine. Int J Adv Manuf Technol 92, 803–815 (2017). https://doi.org/10.1007/s00170-017-0165-9

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