Modeling of machined depth in laser surface texturing of medical needles

Xingsheng Wang*, Peidong Han, Marco Giovannini, Kornel Ehmann

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

The present paper addresses the experimental modeling of process parameters in laser surface texturing (LST) of medical needles. First, experiments were carried out based on Taguchi methodology. The laser process parameters considered during LST were the circumferential overlap, axial overlap and the overscan number. A second-order regression model of the machined depth for LST was developed based on the experimental results. Second, a predictive model for the machined depth based on least squares support vector machines (LS-SVM) with radial basis functions was constructed using the same experimental swatches. Grid search and leave-one-out cross-validation were used to determine the optimal parameters of the LS-SVM model. The comparison between the second-order regression model and the LS-SVM model was carried out. The experiments indicated that the LS-SVM model is capable of better predictions of the machined depth than the second-order regression model. The validity of the LS-SVM model has been checked through the creation of micro-channels with blended edges. It was found that the predicted profile was in a good agreement with the experimental profiles. The LS-SVM model can be used to predict machined geometry of the micro-channels on medical needles.

Original languageEnglish (US)
Pages (from-to)10-18
Number of pages9
JournalPrecision Engineering
Volume47
DOIs
StatePublished - Jan 1 2017

Keywords

  • LS-SVM
  • Laser surface texturing
  • Medical needles
  • Predictive model of machined depth

ASJC Scopus subject areas

  • Engineering(all)

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