Multi-Objective Optimization of Drilling GFRP Composites Using ANN Enhanced by Particle Swarm Algorithm
نویسندگان
چکیده
This paper aims to optimize the quality characteristics of drilling process in glass fiber-reinforced polymer (GFRP) composites. It focuses on optimizing parameters with drill point angles concerning delamination damage and energy consumption, simultaneously. The effects machinability were analyzed by evaluating characteristics. cutting power was modeled through (speed feed), angle, laminate thickness. response surface analysis artificial neural networks enhanced particle swarm optimization algorithm applied for modeling effect process. most influential properties determined variance (ANOVA). A multi-response performed sustainable obtained models predict characteristics, exhibited an excellent harmony experiment results. optimal factors highest spindle speed lowest feed, a angle 118° 4.75 mm
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ژورنال
عنوان ژورنال: Processes
سال: 2023
ISSN: ['2227-9717']
DOI: https://doi.org/10.3390/pr11082418