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• W4293101322 endingPage "2175" @default.
• W4293101322 startingPage "2164" @default.
• W4293101322 abstract "The secondary operations of composite parts are performed following thermal cure processes, which generate the final dimensions with desired tolerance and quality specifications. High-strength composites, on the other hand, especially aramid fiber-reinforced polymers (AFRP), are not suitable for conventional machining operations due in part to high operational costs and limited surface quality characterized by fuzziness and delamination. Abrasive Water Jet (AWJ) has been recently shown promising results in obtaining improved surface quality while ensuring significant cost advantages. This study investigates the AWJ processing of AFRP by implementing the analysis of variance and response surface methods. The effects of the control parameters (sand ratio, pressure, stand-off-distance, and feed rate) on the surface quality metrics (surface roughness, kerf angle, and dimensional error) are identified and comparatively evaluated. The surface quality of the AWJ processed AFRP specimens are investigated using Scanning Electron Microscopy (SEM). The trade-offs between the measured tolerances and surface roughness values are identified via a new genetic algorithm approach: Non-dominated Sorting Genetic Algorithm (NSGA-III). Also, operation regions are determined using the generated Pareto curves while improving the quality of various features of an AFRP component, critical to its functional performance during extended service life. As a result, the lowest Ra values obtained were 4.135 µm for trimming, 5.962 µm for pocketing, and 4.696 µm for the hole-making operation. The maximum error in the accuracy of operating regions yields to 7% with independent measurements for validation." @default.
• W4293101322 created "2022-08-26" @default.
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• W4293101322 date "2022-03-21" @default.
• W4293101322 modified "2023-09-25" @default.
• W4293101322 title "Secondary Processing of Aramid with AWJ and Optimization with NSGA-III" @default.
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• W4293101322 doi "https://doi.org/10.1177/09544089221085325" @default.
• W4293101322 hasPublicationYear "2022" @default.
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