Predictive modelling of surface roughness and kerf widths in abrasive water jet cutting of Kevlar composites using neural network

M. Shukla, P.B. Tambe

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

Abrasive water jet cutting (AWJC) is one of the important non-traditional machining processes used for cutting of difficult-to-cut materials and intricate profiles. Cutting of Kevlar fibre reinforced polymer composites is a complex process, making it difficult to model, predict and improve the cut surface quality. This paper presents a detailed approach of the usage and effectiveness of a back-propagation neural network (NN) for modelling and prediction of three cut surface characteristics namely top kerf width, bottom kerf width and surface roughness (Ra) in AWJC of aerospace grade Kevlar-epoxy composites. Statistically designed full factorial experiments based on three process parameters [water jet pressure (WJP), abrasive flow rate (AFR) and quality level (QL)] at three levels each were conducted to generate the NN training database. The results demonstrate that the NN model was able to successfully model and predict the two kerf widths and surface roughness closely matching the experimental results. Copyright © 2010 Inderscience Enterprises Ltd.
Original languageEnglish
Pages (from-to)226-246
Number of pages21
JournalInternational Journal of Machining and Machinability of Materials
Volume8
Issue number1-2
DOIs
Publication statusPublished - 2010
Externally publishedYes

Bibliographical note

cited By 5

Keywords

  • Abrasive waterjet cutting
  • ANN
  • Artificial Neural Network
  • Epoxy composite
  • Kerf width, Abrasive cutting
  • Abrasives
  • Design of experiments
  • Forecasting
  • Jets
  • Machining
  • Metal analysis
  • Surface properties
  • Surface roughness, Neural networks

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