Random Forest ensemble prediction of stent dimensions in microfabrication processes
- 145 Downloads
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.
KeywordsData mining Random Forest Ensembles of regressors Regression trees Stents Laser machining
Unable to display preview. Download preview PDF.
- 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-OpticsGoogle Scholar
- 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):15–41Google Scholar
- 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):179–197Google Scholar
- 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. 155–161Google Scholar
- 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
- 36.Pardo C, Diez-Pasor JF, Garcia-Osorio C, Rodriguez JJ (2013) Rotation forest for regression. Appl Math Comput 219(19):9914–9924Google Scholar
- 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 1137–1143Google Scholar
- 41.Witten I, Frank E, Hall M (2005) Data mining: practical machine learning tools and techniques, Morgan Kaufmann, 3rd ednGoogle Scholar