Abstract
In this study, a procedure for optimization of an LED lens module design based on 3 LED light sources was divided into two phases. For preliminary optimization of the dimensions of the LED lens module in Stage I, an optical analysis with orthogonal arrays and TracePro (an optical design package) combined with analysis of variance was conducted to investigate relationships between the multiple optical quality characteristics (viewing angle and average illuminance) and dimension parameters and find the initial optimal parameter combination of the LED lens module. In Stage II, the initial optimal parameter combination determined in Stage I was employed to develop an orthogonal array L25(56) for optical simulation. The experimental data of the orthogonal array were used to train and test the back-propagation neural network to develop an optical quality predictor, which was integrated into the genetic algorithms and the particle swarm optimization in order to find the optimal parameter combination that conformed to optical quality. From the experimental results, the proposed optimization procedure contributes to a precise viewing angle to achieve the goal of optical quality and improved the average illuminance in development of the product. The procedure to optimize the optical design developed in this study can be applied to design all types of LED lens modules and improve the optical design and technology of the LED lens industry.
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References
Parkyn WA, Pelka DG (2005) Illuminance-mapping linear lenses for LEDs. Int Soc Opt Eng Conf SPIE 5942:1–12
Moreno I, Tzonchev RI (2004) Effects on illumination uniformity due to dilution on arrays of LEDs. Int Soc Opt Eng Conf SPIE 5529:268–275
Fang YC, Tzeng YF, Li SX (2008) Multi-objective design and extended optimization for developing a miniature light emitting diode pocket-sized projection display. Opt Rev 15(5):241–250
Chen WC, Wang MW, Chen CT, Fu GL (2009) An integrated parameter optimization system for MISO plastic injection molding. Int J Adv Manuf Technol 44(5–6):501–511
Smrelar J, Pandit D, Fast M, Assadi M, De S (2010) Prediction of power output of a coal-fired power plant by artificial neural network. Neural Comput Appl 19(5):725–740
Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Comput Appl 21(1):171–187
Su CT, Wong JT (2007) Designing MIMO controller by neuro-traveling particle swarm optimizer approach. Exp Syst Appl 32(3):848–855
Li CJ, Fang YC, Cheng MC (2009) Study of optimization of an LCD light guide plate with neural network and genetic algorithm. Opt Exp 17(12):10177–10188
Chen WC, Lai TT, Wang MW, Hung HW (2011) An optimization system for LED lens design. Exp Syst Appl 38:11976–11983
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Chen, WC., Liu, KP., Liu, B. et al. Optimization of optical design for developing an LED lens module. Neural Comput & Applic 22, 811–823 (2013). https://doi.org/10.1007/s00521-012-0990-6
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DOI: https://doi.org/10.1007/s00521-012-0990-6