Journal of Computational Design and Engineering

Society for Computational Design and Engineering (한국CDE학회)
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Data-mining modeling for the prediction of wear on forming-taps in the threading of steel components

  • Bustillo, Andres (Department of Civil Engineering, University of Burgos) ;
  • Lopez de Lacalle, Luis N. (Department of Mechanical Engineering, University of the Basque Country UPV/EHU) ;
  • Fernandez-Valdivielso, Asier (Department of Mechanical Engineering, University of the Basque Country UPV/EHU) ;
  • Santos, Pedro (Department of Civil Engineering, University of Burgos)
  • Received : 2016.03.04
  • Accepted : 2016.06.26
  • Published : 2016.10.01
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Abstract

An experimental approach is presented for the measurement of wear that is common in the threading of cold-forged steel. In this work, the first objective is to measure wear on various types of roll taps manufactured to tapping holes in microalloyed HR45 steel. Different geometries and levels of wear are tested and measured. Taking their geometry as the critical factor, the types of forming tap with the least wear and the best performance are identified. Abrasive wear was observed on the forming lobes. A higher number of lobes in the chamber zone and around the nominal diameter meant a more uniform load distribution and a more gradual forming process. A second objective is to identify the most accurate data-mining technique for the prediction of form-tap wear. Different data-mining techniques are tested to select the most accurate one: from standard versions such as Multilayer Perceptrons, Support Vector Machines and Regression Trees to the most recent ones such as Rotation Forest ensembles and Iterated Bagging ensembles. The best results were obtained with ensembles of Rotation Forest with unpruned Regression Trees as base regressors that reduced the RMS error of the best-tested baseline technique for the lower length output by 33%, and Additive Regression with unpruned M5P as base regressors that reduced the RMS errors of the linear fit for the upper and total lengths by 25% and 39%, respectively. However, the lower length was statistically more difficult to model in Additive Regression than in Rotation Forest. Rotation Forest with unpruned Regression Trees as base regressors therefore appeared to be the most suitable regressor for the modeling of this industrial problem.

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References

  1. Henderer WE, von Turkovich BF. Theory of the cold forming taps. Ann. CIRP 1974;23:51-2.
  2. de Carvalho A Olinda, Brandao LC, Panzera TH, Lauro CH. Analysis of form threads using fluteless taps in cast magnesium alloy (AM60). J. Mater. Process. Technol. 2012;212(8)1753-60. https://doi.org/10.1016/j.jmatprotec.2012.03.018
  3. Ivanov V, Kirov V. Rolling of internal threads: Part 1. J. Mater. Process. Technol. 1997;72:214-20. https://doi.org/10.1016/S0924-0136(97)00171-4
  4. Agapiou JS. Evaluation of the effect of high speed machining on tapping. J. Manuf. Sci. Eng. Technol., ASME 1994;116:457-62.
  5. Chandra R, Das SC. Roll taps and their influence on production. J. India. Eng. 1975;55:244-9.
  6. Chowdhary S, Kapoor SG, DeVor RE. Modelling forces including elastic recovery for internal thread forming. J Manuf Sci Eng, ASME 2003;125: 681-8. https://doi.org/10.1115/1.1619178
  7. Stephan P, Mathurin F, Guillot J. Experimental study of forming and tightening processes with thread forming screws. J. Mater. Process. Technol. 2012;212(4)766-75. https://doi.org/10.1016/j.jmatprotec.2011.10.029
  8. Fromentin G, Poulachon G, Moisan A. Precision and surface integrity of threads obtained by form tapping. CIRP Ann. -Manuf. Technol. 2005;54(1)519-22. https://doi.org/10.1016/S0007-8506(07)60159-0
  9. Stephan F, Guillot J, Stephan P, Daidie A. 3D finite element modelling of an assembly process with thread forming screw. J. Manuf. Sci. Eng 2009;131/041015:1-8.
  10. Zunkler B. Thread rolling torques with through rolling die heads and their relationship to the thread geometry and the properties of the workpiece material. Wire 1985;35:114-7.
  11. Domblesky JP. Computer simulation of thread rolling processes. Fasten Technol. Int. 1999(8):38-40.
  12. Domblesky JP, Feng F. A parametric study of process parameters in external thread rolling. J. Mater. Process. Technol. 2002;121:341-9. https://doi.org/10.1016/S0924-0136(01)01223-7
  13. Fernandez Landeta Javier, Fernandez Valdivielso Asier, Lopez de Lacalle LN, Girot Franck, Perez Perez JM,. Wear of form taps in threading of steel cold forged parts. J .Manuf. Sci. Eng. Trans. ASME 2015;137.
  14. Yu J, Xi L, Zhou X. Identifying source(s) of out-of-control signals in multivariate manufacturing processes using selective neural network ensemble. Eng. Appl. Artif. Intell. 2009;22:141-52. https://doi.org/10.1016/j.engappai.2008.05.009
  15. Liao T, Tang F, Qu J, Blau P. Grinding wheel condition monitoring with boosted minimum distance classifiers. Mech Syst Signal Process 2008;22: 217-32. https://doi.org/10.1016/j.ymssp.2007.06.005
  16. Bustillo A, Rodriguez JJ. Online breakage detection of multitooth tools using classifier ensembles for imbalanced data. Int. J. Syst. Sci. 2014;45(12)2590-602. https://doi.org/10.1080/00207721.2013.775378
  17. Cho S, Binsaeid S, Asfour S. Design of multisensor fusion-based tool condition monitoring system in end milling. Int. J. Adv. Manuf. Technol. 2010;46:681-94. https://doi.org/10.1007/s00170-009-2110-z
  18. Binsaeid S, Asfour S, Cho S, Onar A. Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion. J Mater. Process. Technol. 2009;209(10)4728-38. https://doi.org/10.1016/j.jmatprotec.2008.11.038
  19. Bustillo A, Diez-Pastor JF, Quintana G, Garcia-Osorio C. Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations. Int. J. Adv. Manuf. Technol. 2011;57(5)521-32. https://doi.org/10.1007/s00170-011-3300-z
  20. Rodriguez J, Kuncheva L, Alonso C. Rotation forest: a new classifier ensemble method. Pattern Anal. Mach. Intell., IEEE Trans. 2006;28(10)1619-30. https://doi.org/10.1109/TPAMI.2006.211
  21. Santos P, Maudes J, Bustillo A. Identifying maximum imbalance in datasets for fault diagnosis of gearboxes. J. Intell. Manuf. 2015 http://dx. doi.org/10.1007/s10845-015-1110-0.
  22. Shichang Du, Jun Lv, Xi Lifeng. A robust approach for root causes identification in machining processes using hybrid learning algorithm and engineering knowledge. J. Intell. Manuf. 2012;23(5)1833-47. https://doi.org/10.1007/s10845-010-0498-9
  23. Mazahery Ali, Shabani Mohsen Ostad. The accuracy of various training algorithms in tribological behavior modeling of A356-B4C composites. Russ. Metall. (Met) 2011(7):699-707.
  24. Mazahery Ali, Shabani Mohsen Ostad. Assistance of novel artificial intelligence in optimization of aluminum matrix nanocomposite by genetic algorithm. Metall. Mater. Trans. A 2012;43(13)5279-85. https://doi.org/10.1007/s11661-012-1339-6
  25. Shabani Mohsen Ostad, Rahimipour Mohammad Reza, Tofigh Ali Asghar, Davami Parviz. Refined microstructure of compo cast nanocom-posites: the performance of combined neuro-computing, fuzzy logic and particle swarm techniques. Neural Comput. Appl. 2015.
  26. Kuncheva L. Combining classifiers: soft computing solutions. Pattern Recognit.: Class Mod. Approaches 2001:427-51.
  27. Bustillo A, Ukar E, Rodriguez JJ, Lamikiz A. Modelling of process parameters in laser polishing of steel components using ensembles of regression trees. Int. J. Comput. Integr. Manuf. 2011;24(8)735-47. https://doi.org/10.1080/0951192X.2011.574155
  28. Beale R, Jackson T. Neural Computing: An Introduction. Bristol, UK: IOP Publishing Ltd; 1990.
  29. B., Boser, I., Guyon, V., Vapnik, A training algorithm for optimal margin classifiers, in: Proceedings of the fifth annual workshop on Computational learning theory. ACM, 1992. pp. 144-152.
  30. Azadeh A, Ghaderi S, Sohrabkhani S. Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Convers. Manag. 2008;49(8)2272-8. https://doi.org/10.1016/j.enconman.2008.01.035
  31. Palanisamy P, Rajendran I, Shanmugasundaram S. Prediction of tool wear using regression and ANN models in end-milling operation. Int. J. Adv. Manuf. Technol. 2008;37(1-2)29-41. https://doi.org/10.1007/s00170-007-0948-5
  32. Quintana G, Bustillo A, Ciurana J. Prediction, monitoring and control of surface roughness in high-torque milling machine operations. Int. J. Comput. Integr. Manuf. 2012;25(12)1129-38. https://doi.org/10.1080/0951192X.2012.684717
  33. Tosun N, Ozler L. A study of tool life in hot machining using artificial neural networks and regression analysis method. J Mater Process Technol 2002;124(1)99-104. https://doi.org/10.1016/S0924-0136(02)00086-9
  34. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Philip, SY, et al. Top 10 algorithms in data mining. Knowl. Inf. Syst. 2008;14(1)1-37. https://doi.org/10.1007/s10115-007-0114-2
  35. Vijayakumar S, Schaal S. Approximate nearest neighbor regression in very high dimensions. In: Nearest-Neighbor Methods in Learning and Vision: Theory and Practice. Cambridge, MA, USA: MIT Press; 2006. p. 103-42.
  36. J. R. Quinlan, 1992. Learning with continuous classes, in: Proceedings of the 5th Australian joint Conference on Artificial Intelligence, Vol. 92. Singapore, pp. 343-348.
  37. Khoshgoftaar TM, Allen EB, Deng J. Using regression trees to classify fault-prone software modules. Reliab., IEEE Trans. 2002;51(4)455-62. https://doi.org/10.1109/TR.2002.804488
  38. Witten IH, Frank E. Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed., Morgan Kaufmann; 2005. >.
  39. Ho TK. The random subspace method for constructing decision forests. Pattern Anal. Mach. Intell., IEEE Trans. 1998;20(8)832-44. https://doi.org/10.1109/34.709601
  40. Dietterichl TG. Ensemble learning. In: Arbib MA, editor. Handbook of Brain Theory and Neural Networks, 2nd edition, p. 405-8.
  41. Breiman L. Bagging predictors. Mach. Learn. 1996;24(2)123-40. https://doi.org/10.1023/A:1018054314350
  42. Freund Y, Schapire, RE, et al. Experiments with a new boosting algorithm. ICML 1996;96:148-56.
  43. Breiman L. Using iterated bagging to debias regressions. Mach. Learn. 2001;45(3)261-77. https://doi.org/10.1023/A:1017934522171
  44. Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. In: Computational Learning Theory. Springer; 1995. p. 23-37.
  45. Drucker H. Improving regressors using boosting techniques. ICML 1997;97:107-15.
  46. Friedman JH. Stochastic gradient boosting. Comput. Stat. Data Anal. 2002;38(4)367-78. https://doi.org/10.1016/S0167-9473(01)00065-2
  47. Dayhoff JE, Leo JM De. Artificial neural networks. Cancer 2001;91(S8) S1615-35. https://doi.org/10.1002/1097-0142(20010415)91:8+<1615::AID-CNCR1175>3.0.CO;2-L
  48. Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Netw. 1989;2(5)359-66. https://doi.org/10.1016/0893-6080(89)90020-8
  49. W.H. Delashmit, M.T. Manry, Recent developments in multilayer perceptron neural networks, in: Proceedings of the Seventh Annual Memphis Area Engineering and Science Conference, MAESC, 2005.
  50. Smola AJ, Scholkopf B. A tutorial on support vector regression. Stat. Comput. 2004;14(3)199-222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
  51. Cortes C, Vapnik V. Support-vector networks. Mach. Learn. 1995;20(3)273-97. https://doi.org/10.1007/BF00994018
  52. Aha DW, Kibler D, Albert MK. Instance-based learning algorithms. Mach. Learn. 1991;6(1)37-66.

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