Optimization of injection molding parameters for HDPE/TiO2 nanocomposites fabrication with multiple performance characteristics using the Taguchi method and grey relational analysis

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

Abstract

The current study presents an investigation on the optimization of injection molding parameters of HDPE/TiO2 nanocomposites using grey relational analysis with the Taguchi method. Four control factors, including filler concentration (i.e., TiO2), barrel temperature, residence time and holding time, were chosen at three different levels of each. Mechanical properties, such as yield strength, Young's modulus and elongation, were selected as the performance targets. Nine experimental runs were carried out based on the Taguchi L9 orthogonal array, and the data were processed according to the grey relational steps. The optimal process parameters were found based on the average responses of the grey relational grades, and the ideal operating conditions were found to be a filler concentration of 5 wt % TiO2, a barrel temperature of 225 °C, a residence time of 30 min and a holding time of 20 s. Moreover, analysis of variance (ANOVA) has also been applied to identify the most significant factor, and the percentage of TiO2 nanoparticles was found to have the most significant effect on the properties of the HDPE/TiO2 nanocomposites fabricated through the injection molding process.

Original languageEnglish
Article number710
JournalMaterials
Volume9
Issue number8
DOIs
Publication statusPublished - Aug 22 2016

Keywords

  • Grey relational analysis
  • HDPE/TiO nanocomposites
  • Injection molding parameters
  • Optimization
  • Taguchi method

ASJC Scopus subject areas

  • Materials Science(all)
  • Condensed Matter Physics

Fingerprint

Dive into the research topics of 'Optimization of injection molding parameters for HDPE/TiO2 nanocomposites fabrication with multiple performance characteristics using the Taguchi method and grey relational analysis'. Together they form a unique fingerprint.

Cite this