Determination and prediction of surface and kerf properties in abrasive water jet machining of Fe-Cr-C based hardfacing wear plates


ARMAĞAN M., ARICI A. A.

Journal of Manufacturing Processes, cilt.117, ss.329-345, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 117
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.jmapro.2024.03.016
  • Dergi Adı: Journal of Manufacturing Processes
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Compendex, INSPEC
  • Sayfa Sayıları: ss.329-345
  • Anahtar Kelimeler: Abrasive water jet, ANOVA, General linear model, Hardfacing, Kerf, Surface roughness
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Improving and maintaining the sustainability, profitability and efficiency of sectors such as manufacturing, mining and agriculture are the most important goals for industries. It is therefore quite common to apply hardfacing method to products and equipment created to achieve these goals. However, machining of the workpiece is necessary to ensure dimensional accuracy as a result of hardfacing production. In the processing of hardfacing materials with traditional machining methods, negative factors such as tool wear and thermal effects occur. For this reason, the cutting performance of hardfacing wear plate with abrasive water jet, which is one of the unconventional manufacturing processes, was investigated in this study. Tests were realized in regard to the full factorial experimental design, which includes the changes in the levels of material alignment direction, abrasive mass flow rate and traverse speed parameters. The influences of the test parameters on the surface roughness and kerf taper angle constituting the cutting performance were determined by analysis of variance (ANOVA). In addition, the effects of changes in parameter levels were investigated. Material alignment direction was found to be the most effective test parameter for surface roughness and kerf taper angle. The influences of parameter and parameter levels on the formation of surface conditions were determined with surface images. Thus, machining mechanisms and surface morphologies were explained by scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). Optimal levels of test parameters were achieved by performing multiple-response optimization with equal importance. Finally, the estimated results of cutting performances were found by general linear model, and all results were confirmed by regression analysis.