Journal
MEASUREMENT
Volume 134, Issue -, Pages 359-374Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2018.10.066
Keywords
Injection molding process; Bi-aspheric lens; Artificial neural networks (ANNs); Particle swarm optimization (PSO); Surface quality; SPDT machining
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Injection molding of bi-aspheric lens using polycarbonate material with minimum variation in volumetric shrinkage is crucial for optical quality and is more challenging task among the researchers. In this paper, a hybrid artificial neural networks (ANN) and particle swarm optimization (PSO) technique is used to predict the optimal process parameters of injection molding process of the bi-aspheric lens. The developed ANN network (7-13-6) was trained as well as tested with experimental data sampled from statistical methods. The well trained and tested ANN network was coupled with improved PSO algorithm as a hybrid ANN-PSO to optimize the injection molding process parameters. The optimized injection molding process parameters obtained from hybrid ANN-PSO algorithm are validated with experiments using J. S. Winjection molding machine. It is observed from the lens quality parameters that the proposed hybrid ANN-PSO method optimized the injection molding process of the bi-aspheric lens with an optical power of 27.73 Diopter and the lens posses seventh order spherical aberrations. (C) 2018 Elsevier Ltd. All rights reserved.
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