Predicting the seam strength of notched webbings for parachute assemblies using the Taguchi's design of experiment and artificial neural networks

L Onal, M Zeydan, M Korkmaz, Sheik Meeran

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Webbings are used in parachute assemblies as reinforcing units for the strength they provide. The strength of these seams is an important characteristic which has a substantial influence on the mechanical property of the parachute assemblies. It is well established that factors such as fabric width, folding length of joint, seam design and seam type will all have an impact on seam strength. In this work, the effect of these factors on seam strength was studied using both Taguchi's design of experiment (TDOE) as well as an artificial neural network (ANN). In TDOE, two levels were chosen for the factors mentioned above. An L8 design was adopted and an orthogonal array was generated. The contribution of each factor to seam strength was analyzed using analysis of variance (ANOVA) and signal to noise ratio methods. From the analysis it was found that the fabric width, folding length of joint and interaction between the folding length of joint and the seam design affected seam strength significantly. Further, using TDOE, an optimal configuration of levels of factors was found. In order to contrast and compare the results from TDOE, an ANN was also used to predict seam strength using the above mentioned factors as inputs. The prediction from TDOE and ANN methodologies were compared with physical seam strength. It was established from these comparisons, in which the root mean square error was used as an accuracy measure, that the predictions by ANN were better in accuracy than those predicted by TDOE.
Original languageEnglish
Pages (from-to)468-478
Number of pages11
JournalTextile Research Journal
Volume79
Issue number5
DOIs
Publication statusPublished - Mar 2009

Keywords

  • webbing
  • Taguchi's design of experiment
  • seam strength
  • artificial neural network
  • parachute

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