journal article·2020-10

Sandblasting parameter variation effect on galvanized steel surface chemical composition, roughness and free energy

A. Bechikh, O. Klinkova, Y. Maalej, I. Tawfiq, R. Nasri

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Abstract

Abstract The aim of this paper is to investigate the effect of variant sandblasting parameters such as pressure, angle and sand granulometry on galvanized steel surface chemical composition, roughness and free energy for the purpose of increasing its adhesion properties for further use in structural bonding applications. Two sandblasting pressures (5 × 10 5 Pa, 6 × 10 5 Pa), two sandblasting angles (75°, 90°), and three glace sand granulometries (200/300, 90/150, 70/110) were tested. Surface chemical composition was evaluated using a scanning electron microscope, surface roughness was measured using a mechanical surfscan and surface free energy was determined using the contact angle method along with the two component Owens-Wendt model. Trials revealed that the sandblasting method has an effect on the surface free energy, wettability, chemical composition and roughness. As the sandblasting pressure and glace sand granulometry increased, the surface average roughness (Ra) increased. Specimens treated with the lowest glace sand granulometry had the highest surface free energy ( γ s t ). Glace sand granulometry had a more significant effect on the galvanized steel surface properties than that of the sandblasting pressure and angle. Sandblasting angle variation affected the surface chemical composition and wettability rather than the roughness. Sand residue presented at the level of the treated surface varied according to the sandblasting setting which might have affected the surface free energy and wettability along with the roughness.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Adhesion, Friction, and Surface Interactions · Surface Modification and Superhydrophobicity · Metal Alloys Wear and Properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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