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Boosting Projections to improve surface roughness prediction in high-torque milling operations

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Abstract

Industrial solutions for surface roughness prediction are in great demand, especially in high-torque milling operations, owing to the exponential expansion of wind power energy generation over the past decade. In this paper, we use Boosting Projections to predict surface roughness in high-torque, high-power face milling operations. A data set is generated from experiments performed under industrial conditions, using a milling machine with a high working volume, to train and validate the new algorithm. The experimental data comprise a very extensive set of parameters that influence surface roughness: cutting tool properties, machining parameters and cutting phenomena. The proposed method is based on non-linear boosting projections (although it uses linear projections to speed up the training process). To the best of our knowledge this is the first time it has been used in an industrial context. It demonstrates a higher prediction accuracy when compared with single multilayer perceptrons, decision trees and classical ensemble methods.

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  1. http://www.cs.waikato.ac.nz/ml/weka/.

References

  • Agresti A (2010) Analysis of ordinal categorical data. Wiley series in probability and statistics. Wiley, New York

  • Arizmendi M, Fernández J, Gil A, Veiga F (2009) Effect of tool setting error on the topography of surfaces machined by peripheral milling. Int J Mach Tools Manuf 49(1):36–52

    Article  Google Scholar 

  • Bauer E, Kohavi R (1999) An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach Learn 36:105–139

    Article  Google Scholar 

  • Beggan C, Woulfe M, Young P, Byrne G (1999) Using acoustic emission to predict surface quality. Int J Adv Manuf Technol 15:737–742. 10.1007/s001700050126

    Google Scholar 

  • Benardos PG, Vosniakos GC (2002) Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Robot Comput-Integrated Manuf 18(5–6):343–354

    Article  Google Scholar 

  • Benardos PG, Vosniakos GC (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43(8):833–844

    Article  Google Scholar 

  • Binsaeid S, Asfour S, Cho S, Onar A (2009) Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion. J Mater Process Technol 209(10):4728–4738

    Article  Google Scholar 

  • Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

    MathSciNet  MATH  Google Scholar 

  • Brezocnik M, Kovacic M (2003) Integrated genetic programming and genetic algorithm approach to predict surface roughness. Mater Manuf Process 18(3):475–491

    Article  Google Scholar 

  • Brezocnik M, Kovacic M, Ficko M (2004) Prediction of surface roughness with genetic programming. J Mater Process Technol 157(158):28–36

    Article  Google Scholar 

  • Bustillo A, Ukar E, Rodriguez JJ, Lamikiz A (2011a) Modelling of process parameters in laser polishing of steel components using ensembles of regression trees. Int J Comput Integr Manuf 24(8):735–747

    Article  Google Scholar 

  • Bustillo A, Díez-Pastor JF, Quintana G, García-Osorio C (2011b) Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations. Int J Adv Manuf Technol 57:521–532. doi:10.1007/s00170-011-3300-z

    Google Scholar 

  • Chandrasekaran M, Muralidhar M, Krishna C, Dixit U (2010) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol 46(5):445–464

    Article  Google Scholar 

  • Cho S, Binsaeid S, Asfour S (2010) Design of multisensor fusion-based tool condition monitoring system in end milling. Int J Adv Manuf Technol 46(5):681–694

    Article  Google Scholar 

  • Choudhury SK, Bartarya G (2003) Role of temperature and surface finish in predicting tool wear using neural network and design of experiments. Int J Mach Tools Manuf 43(7):747–753

    Article  Google Scholar 

  • Correa M, Bielza C, de J. Ramirez M, Alique JR (2008) A Bayesian network model for surface roughness prediction in the machining process. Int J Syst Sci 39(12):1181–1192

    Article  MATH  Google Scholar 

  • Correa M, Bielza C, Pamies-Teixeira J (2009) Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process. Expert Syst Appl 36(3):7270–7279

    Article  Google Scholar 

  • Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10(7):1895–1923

    Article  Google Scholar 

  • Dietterich TG (2000a) Ensemble methods in machine learning. In: Kittler J, Roli F (eds) Multiple classifier systems. Lecture notes in computer science, vol 1857, pp 1–15

  • Dietterich TG (2000b) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40(2):139–157

    Article  Google Scholar 

  • Dhokia VG, Kumar S, Vichare P, Newman ST (2008) An intelligent approach for the prediction of surface roughness in ball-end machining of polypropylene. Robot Comput-Integrated Manuf 24(6):835–842. In: FAIM 2007, 17th international conference on flexible automation and intelligent manufacturing

    Google Scholar 

  • Dzeroski S, Zenko B (2004) Is combining classifiers with stacking better than selecting the best one? Mach Learn 54(3):255–273

    Article  MATH  Google Scholar 

  • Frank E, Hall M (2001) A simple approach to ordinal classification. In: ECML 2001, pp 145–156

  • Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: International conference on machine learning, pp 148–156

  • Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139

    Article  MathSciNet  MATH  Google Scholar 

  • Fukunaga K, Mantock J (1983) Nonparametric discriminant analysis. IEEE Trans Pattern Anal Mach Intell 6(5):671–678

    Article  Google Scholar 

  • García-Pedrajas N, García-Osorio C (2011) Constructing ensembles of classifiers using supervised projection methods based on misclassified instances. Expert Syst Appl 38(1):343–359

    Article  Google Scholar 

  • García-Pedrajas N, García-Osorio C, Fyfe C (2007) Nonlinear “boosting” projections for ensemble construction. J Mach Learn Res 8:1–33

    MathSciNet  MATH  Google Scholar 

  • García-Osorio C, García-Pedrajas N (2008) Constructing ensembles of classifiers using linear projections based on misclassified instances. In Verleysen M (ed) 16th European symposium on artificial neural networks (ESANN 2008), pp 283–288, Bruges, Belgium, April 2008. d-side publications

  • Groover MP (2006) Fundamentals of modern manufacturing: materials, processes, and systems, 3rd edn. Wiley, New York. ISBN:0471744859; ISBN-13:9780471744856, 978-0471744856

  • Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. SIGKDD Explor Newsl 11:10–18

    Article  Google Scholar 

  • Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20:832–844

    Article  Google Scholar 

  • Ho WH, Tsai JT, Lin BT, Chou JH (2009) Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid taguchi-genetic learning algorithm. Expert Syst Appl 36(2):3216–3222

    Article  Google Scholar 

  • International Organization for Standardization (1996) ISO-4288. Geometrical product specifications (GPS): rules and procedures for the assessment of surface texture

  • International Organization for Standardization (1997) ISO-4287. Geometrical product specifications (GPS)—surface texture: profile method—terms, definitions and surface texture parameters

  • Iqbal A, He N, Li L, Dar NU (2007) A fuzzy expert system for optimizing parameters and predicting performance measures in hard-milling process. Expert Syst Appl 32(4):1020–1027

    Article  Google Scholar 

  • Ismail F, Elbestawi MA, Du R, Urbasik K (1993) Generation of milled surfaces including tool dynamics and wear. J Eng Ind 115(3):245–252

    Google Scholar 

  • Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley-Interscience, New York. ISBN-10: 0471210781; ISBN-13: 978-0471210788

  • Kuncheva LI (2005) Diversity in multiple classifier systems. Inf Fusion 6(1):3–4

    Article  MathSciNet  Google Scholar 

  • Kuncheva LI (2001) Combining classifiers: soft computing solutions. In: Pal SK (ed) Pattern recognition: from classical to modern approaches. World Scientific, Singapore, pp 427–452

  • Lee HS, Park MS, Kim MT, Chu CN (2006) Systematic finishing of dies and moulds. Int J Mach Tools Manuf 46(9):1027–1034

    Article  Google Scholar 

  • Lo SP (2003) An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. J Mater Process Technol 142(3):665–675

    Article  Google Scholar 

  • Maimon O, Rokach L (eds) (2010) Data mining and knowledge discovery handbook, 2nd edn. Springer, Berlin

  • Martellotti ME (1941) An analysis of the milling process. Trans ASME 63:667–700

    Google Scholar 

  • Montgomery D, Altintas Y (1991) Mechanism of cutting force and surface generation in dynamic milling. J Eng Ind 113(2):160–168

    Article  Google Scholar 

  • Oza N, Tumer K (2008) Classifier ensembles: select real-world applications. Inf Fusion 9(1):4–20

    Article  Google Scholar 

  • Prakasvudhisarn C, Kunnapapdeelert S, Yenradee P (2009) Optimal cutting condition determination for desired surface roughness in end milling. Int J Adv Manuf Technol 41(5):440–451

    Article  Google Scholar 

  • Quintana G, Garcia-Romeu M, Ciurana J (2009) Surface roughness monitoring application based on artificial neural networks for ball-end milling operations. J Intell Manuf. doi:10.1007/s10845-009-0323-5

  • Quintana G, de Ciurana J, Ribatallada J (2010) Surface roughness generation and material removal rate in ball end milling operations. Mater Manuf Process 25(6):386–398

    Article  Google Scholar 

  • Samanta B, Erevelles W, Omurtag Y (2008) Prediction of workpiece surface roughness using soft computing. Proc Inst Mech Eng B: J Eng Manuf 222(10):1221–1232

    Article  Google Scholar 

  • Tian Q, Yu J, Huang TS (2005) Boosting multiple classifiers constructed by hybrid discriminantanalysis. In: Oza NC, Polikar R, Kittler J, Roli F (eds) Multiple classifier systems. Lecture notes in computer science, vol 3541, pp 42–52. Springer, Berlin

  • Vivancos J, Luis CJ, Ortiz JA, González HA (2005) Analysis of factors affecting the high-speed side milling of hardened die steels. J Mater Process Technol 162–163:696–701

    Google Scholar 

  • Webb GI (2000) MultiBoosting: a technique for combining boosting and wagging. Mach Learn 40(2):159

    Google Scholar 

Download references

Acknowledgments

This investigation has been partially supported by the Projects CENIT-2008-1028, TIN2011-24046 and IPT-2011-1265-020000 of the Spanish Ministry of Economy and Competitiveness. The authors would especially like to thank Mr. Desiderio Sutil from Nicolas Correa S.A. for his kind-spirited and useful advice.

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Authors and Affiliations

  1. Department of Civil Engineering, University of Burgos, Burgos, Spain

    José-Francisco Díez-Pastor, Andres Bustillo & César García-Osorio

  2. ASCAMM Technology Centre, Parc Tecnológic del Vallés, Barcelona, Spain

    Guillem Quintana

Authors
  1. José-Francisco Díez-Pastor
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  2. Andres Bustillo
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  3. Guillem Quintana
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  4. César García-Osorio
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Corresponding author

Correspondence to Andres Bustillo.

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Díez-Pastor, JF., Bustillo, A., Quintana, G. et al. Boosting Projections to improve surface roughness prediction in high-torque milling operations. Soft Comput 16, 1427–1437 (2012). https://doi.org/10.1007/s00500-012-0846-0

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