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Application of Response Surface Method to Optimize Waterjet Cutting Process Parameter of Glass Fiber Reinforced Polymer Matrix Laminates
ISSN: 0148-7191, e-ISSN: 2688-3627
Published October 11, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Event: International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility
Waterjet machining is a widely used advanced machining technique because of its versatility in removal of material for a wider range of materials. Waterjet machining is particularly advantageous in the precise cutting of advanced materials like Fiber Reinforced Polymers (FRPs) comparative to conventional machining methods. The conventional machining methods result in the release of high amount of glass fiber dust which leaves the work environment unsafe for the workers. The material dust if inhaled can lead to acute respiratory diseases. In this work an analysis was done on the cutting performance of Waterjet machining and is presented based on an experimental investigation on fabricated fiberglass reinforced laminates. It is shown that with a good combination of cutting parameters such as nozzle traverse speed, waterjet pressure, and Stand-off distance a cutting performance can be achieved. Plausible trends of kerf quality and machining time with respect to the waterjet pressure, nozzle traverse speed and nozzle stand-off distance are analysed. The surface roughness and machining time has been optimized using DOE techniques for achieving proper machining characteristics and faster completion of the work. An experimental study of WJM of Glass epoxy composite to improve kerf properties is presented. The RSM a well-renowned technique is used to optimize the process parameters.
CitationAulakh, S., Patil, D., Elsen, R., and Aggarwal, S., "Application of Response Surface Method to Optimize Waterjet Cutting Process Parameter of Glass Fiber Reinforced Polymer Matrix Laminates," SAE Technical Paper 2019-28-0153, 2019, https://doi.org/10.4271/2019-28-0153.
Data Sets - Support Documents
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