Additive manufacturing of polypropylene: A screening design of experiment using laser-based powder bed fusion

Research output: Contribution to journalArticleScientificpeer-review

Researchers

  • Iñigo Flores Ituarte
  • Olli Wiikinkoski
  • Anton Jansson

Research units

  • Aalborg University
  • Örebro University

Abstract

The use of commodity polymers such as polypropylene (PP) is key to open new market segments and applications for the additive manufacturing industry. Technologies such as powder-bed fusion (PBF) can process PP powder; however, much is still to learn concerning process parameters for reliable manufacturing. This study focusses in the process-property relationships of PP using laser-based PBF. The research presents an overview of the intrinsic and the extrinsic characteristic of a commercial PP powder as well as fabrication of tensile specimens with varying process parameters to characterize tensile, elongation at break, and porosity properties. The impact of key process parameters, such as power and scanning speed, are systematically modified in a controlled design of experiment. The results were compared to the existing body of knowledge; the outcome is to present a process window and optimal process parameters for industrial use of PP. The computer tomography data revealed a highly porous structure inside specimens ranging between 8.46% and 10.08%, with porosity concentrated in the interlayer planes in the build direction. The results of the design of experiment for this commercial material show a narrow window of 0.122 ≥ Ev ≥ 0.138 J/mm3 led to increased mechanical properties while maintaining geometrical stability.

Details

Original languageEnglish
Article number1293
JournalPolymers
Volume10
Issue number12
Publication statusPublished - 22 Nov 2018
MoE publication typeA1 Journal article-refereed

    Research areas

  • Additive manufacturing, Computer tomography, Laser sintering, Mechanical properties, Polypropylene, Powder-bed fusion, Process parameter optimization

Download statistics

No data available

ID: 30313340