4.5 Article

Abrasive Machining Characteristics and Prediction Model for Sisal/Polyester Sandwich Composite

期刊

JOURNAL OF NATURAL FIBERS
卷 19, 期 14, 页码 7956-7972

出版社

TAYLOR & FRANCIS INC
DOI: 10.1080/15440478.2021.1958427

关键词

Sisal fiber; composite; pvc foam core; awjm; optimization; prediction model

资金

  1. U.S. Department of Biomaterials Research [2005-1234]

向作者/读者索取更多资源

The study focuses on optimizing the abrasive machining parameters of natural fiber reinforced sandwich composite. By comparing the experimental results and observing surface damage through scanning electron microscopy, the feasibility of the research was validated.
This work focuses on optimization of abrasive machining parameters of the natural fiber reinforced sandwich composite, which is rarely reported in the literature. A sandwich made of vegetable fiber composite skins and polyvinyl chloride (PVC) foam of 80 gsm was machined for optimal conditions. The design of experiment and analysis were adopted to confirm the influence of machining parameters. The machining characters of bio-sandwich were compared with synthetic and hybrid sandwich panels to optimize the machinability of the target. The panels were manufactured through vacuum infusion bagging. The machining studies were done using the abrasive water jet cutting machine. The machining characteristics were optimized for the parameters and L18 Taguchi technique was employed in parameter optimization. Three controlled levels of machining parameters were chosen to be optimized: standoff distance (SOD), abrasive water jet pressure (JP), and nozzle traverse rate (TR). The response of kerf taper (KT), surface roughness (SR), and material removal rate (MRR) were investigated. It is observed that highest levels of these parameters gave minimum kerf taper and lowest levels produce lower surface roughness. The surface roughness and damage on the surface was observed using scanning electron microscopy (SEM). It Shows that flowing abrasive particle's directional distortion noted at the foam regions due to their higher damping nature. The prediction model shows a good agreement with the experimental value.

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