Random Forest ensemble prediction of stent dimensions in microfabrication processes

Abstract

The recent development of new laser machine tools for the manufacture of micro-scale metallic components has boosted demand in the field of medical applications. However, the optimization of this process encounters a major problem: a knowledge gap concerning the relation between the controllable parameters of these machine tools and the quality of the machined components. Our research proposes a two-step strategy to approach this problem for the manufacture of stents. First, a screening test identifies good and bad performance conditions for the laser process and generates useful information on cutting performance; then, a stent is manufactured under different cutting conditions and the most accurate machine learning technique to model this process is identified. This strategy is validated with the performance of experiments that vary pulse duration, laser power, and cutting speed, and measure two geometrical characteristics of the stent geometry. The results showed that linear Support Vector Machines can identify good and bad cutting conditions, while Random Forest ensembles of regression trees can predict with high accuracy the two characteristics of the stent geometry under study. Besides, this technique can extract useful information from the screening test that improves its final accuracy. In view of the small dataset size, an alternative based on the leave-one-out technique was used, instead of standard cross validation, so as to assure the generalization capability of the models.

This is a preview of subscription content, log in to check access.

References

  1. 1.

    Tawari G, Sarin Sundar JK, Sundararajan G, Joshi SV (2005) Influence of process parameters during pulsed Nd:YAG laser cutting of nickel-base superalloys. J Mater Process Technol 170:229–239

    Article  Google Scholar 

  2. 2.

    K.F. Kleine, B. Whitney, K.G. Watkins (2002) Use of fiber lasers for micro cutting applications in medical device industry. 21st International Congress on Applications of Lasers and Electro-Optics

  3. 3.

    Muhammad N, Whitehead D, Boor A, Li L (2010) Precision machine design. Comparison of dry and wet fibre laser profile cutting of thin 316 L stainless steel tubes for medical device applications. J Mater Process Technol 210:2261–2267

    Article  Google Scholar 

  4. 4.

    Meng H, Liao J, Zhou Y, Zhang Q (2009) Laser micro-processing of cardiovascular stent with fiber laser cutting system. Optics Laser Technol 41:300–302

    Article  Google Scholar 

  5. 5.

    Baumeister M, Dickman K, Hoult T (2006) Fiber laser micro-cutting of stainless steel sheets. J Appl Phys A 85:121–124

    Article  Google Scholar 

  6. 6.

    Yan Y, Li L, Sezer K, Whitehead D, Ji L, Bao Y, Jiang Y (2011) Experimental and theoretical investigation of fibre laser crack-free cutting of thick-section alumina. Int J Mach Tools Manuf 51:859–870

    Article  Google Scholar 

  7. 7.

    Kathuria YP (2005) Laser microprocessing of metallic stent for medical therapy. J Mater Process Technol 170:545–550

    Article  Google Scholar 

  8. 8.

    Pfeifer R, Herzog D, Hustedt M, Barcikowski S (2010) Pulsed Nd:YAG laser cutting of NiTi shape memory alloys—influence of process parameters. J Mater Process Technol 210:1918–1925

    Article  Google Scholar 

  9. 9.

    Shanjin L, Yang W (2006) An investigation of pulsed laser cutting of titanium alloy sheet. Opt Lasers Eng 44:1067–1077

    Article  Google Scholar 

  10. 10.

    Muhammad N, Whitehead D, Boor A, Oppenlander W, Liu Z, Li L (2012) Picosecond laser micromachining of nitinol and platinum-iridium alloy for coronary stent applications. Appl Phys A 106:607–617

    Article  Google Scholar 

  11. 11.

    Lia C, Nikumbb S, Wong F (2006) An optimal process of femtosecond laser cutting of NiTi shape memory alloy for fabrication of miniature devices. Opt Lasers Eng 44:1078–1087

    Article  Google Scholar 

  12. 12.

    Huang H, Zheng HY, Lim GC (2004) Femtosecond laser machining characteristics of nitinol. Appl Surf Sci 228:201–206

    Article  Google Scholar 

  13. 13.

    Raval A, Choubey A, Engineer C, Kothwala D (2004) Development and assessment of 316LVM cardiovascular stents. Mater Sci Eng A 386:331–343

    Article  Google Scholar 

  14. 14.

    Scintilla LD, Tricarico L (2013) Experimental investigation on fiber and CO2 inert gas fusion cutting of AZ31 magnesium alloy sheets. Optics Laser Technol 46:42–52

    Article  Google Scholar 

  15. 15.

    Desai CK, Shaikh A (2012) Prediction of depth of cut for single-pass laser micro-milling process using semi-analytical, ANN and GP approaches. Int J Adv Manuf Technol 60(9–12):865–882

    Article  Google Scholar 

  16. 16.

    Goyal R, Dubey AK, Upadhyay BN (2016) An intelligent approach to quality improvement in laser trepan drilling of inconel 718 superalloy. Lasers Eng 34(1–3):15–41

    Google Scholar 

  17. 17.

    Karazi SM, Issa A, Brabazon D (2009) Comparison of ANN and DoE for the prediction of laser–machined micro-channel dimensions. Opt Lasers Eng 47:956–964

    Article  Google Scholar 

  18. 18.

    Yousef BF, Knopf GK, Bordatchev EV, Nikumb SK (2003) Neural network modeling and analysis of the material removal process during laser machining. Int J Adv Manuf Technol 22(1–2):41–53

    Article  Google Scholar 

  19. 19.

    Teixidor D, Grzenda M, Bustillo A, Ciurana J (2015) Modeling pulsed laser micromachining of micro geometries using machine-learning techniques. J Intell Manuf 26(4):801–814. doi:10.1007/s10845-013-0835-x

    Article  Google Scholar 

  20. 20.

    Chandrasekaran M, Muralidhar M, Krishna C (2010) Dixit U “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 

  21. 21.

    Bustillo A, Correa M (2012) Using artificial intelligence to predict surface roughness in deep drilling of steel components. J Intell Manuf 23(5):1893–1902

    Article  Google Scholar 

  22. 22.

    Bustillo A, Díez-Pastor JF, Quintana G, García-Osorio C (2011) Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations. Int J Adv Manuf Technol 57(5):521–532

    Article  Google Scholar 

  23. 23.

    Grzenda M, Bustillo A, Zawistowski P (2012) A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling. J Intell Manuf 23(5):1733–1743

    Article  Google Scholar 

  24. 24.

    Grzenda M, Bustillo A, Quintana G, Ciurana J (2012) Improvement of surface roughness models for face milling operations through dimensionality reduction. Integr Comput-Aided Eng 19(2):179–197

    Google Scholar 

  25. 25.

    Beruvides G, Castaño F, Quiza R, Haber RE (2016) Surface roughness modeling and optimization of tungsten-copper alloys in micro-milling processes. Meas: J Int Meas Confederation 86:246–252

    Article  Google Scholar 

  26. 26.

    Bustillo A, Ukar E, Rodriguez JJ, Lamikiz A (2011) 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 

  27. 27.

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

    Article  Google Scholar 

  28. 28.

    Cicală E, Soveja A, Sallamand P, Grevey D, Jouvard JM (2008) The application of the random balance method in laser machining of metals. J Mater Process Technol 196(1–3):393–401

    Article  Google Scholar 

  29. 29.

    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  30. 30.

    Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185

    MathSciNet  Google Scholar 

  31. 31.

    Drucker H, Surges CJC, Kaufman L, Smola A, Vapnik V (1996) Support vector regression machines, in advances in neural information processing systems, NIPS. MIT Press, USA, pp. 155–161

    Google Scholar 

  32. 32.

    Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, New York

    Google Scholar 

  33. 33.

    Mendes-Moreira J, Soares C, Jorge AM, De Sousa JF (2012) Ensemble approaches for regression: a survey. ACM Comput Surv 45(1) art. no. 10. doi:10.1145/2379776.2379786

  34. 34.

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

    MATH  Google Scholar 

  35. 35.

    Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  36. 36.

    Pardo C, Diez-Pasor JF, Garcia-Osorio C, Rodriguez JJ (2013) Rotation forest for regression. Appl Math Comput 219(19):9914–9924

  37. 37.

    Breiman L (2001) Using iterated bagging to debias regressions. Mach Learn 45(3):261–277

    Article  MATH  Google Scholar 

  38. 38.

    Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367–378

    MathSciNet  Article  MATH  Google Scholar 

  39. 39.

    Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 2 (12). Morgan Kaufmann, San Mateo, pp 1137–1143

  40. 40.

    Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explorations 11(1):10–18

    Article  Google Scholar 

  41. 41.

    Witten I, Frank E, Hall M (2005) Data mining: practical machine learning tools and techniques, Morgan Kaufmann, 3rd edn

    Google Scholar 

  42. 42.

    Elomaa T, Kääriäinen M (2001) An analysis of reduced error pruning. J Artif Intell Res 15:163–187

    MathSciNet  MATH  Google Scholar 

  43. 43.

    Nadeau C, Bengio Y (2003) Inference for the generalization error. Mach Learn 52(3):239–281

    Article  MATH  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Joaquim Ciurana.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Maudes, J., Bustillo, A., Guerra, A.J. et al. Random Forest ensemble prediction of stent dimensions in microfabrication processes. Int J Adv Manuf Technol 91, 879–893 (2017). https://doi.org/10.1007/s00170-016-9695-9

Download citation

Keywords

  • Data mining
  • Random Forest
  • Ensembles of regressors
  • Regression trees
  • Stents
  • Laser machining