PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Bi-objective robust design optimization for LED lens design

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Dwuobiektywowa, solidna optymalizacja konstrukcji soczewki LED
Języki publikacji
EN
Abstrakty
EN
The primary objective of this paper is to propose bi-objective robust design optimization models for LED lens design. The viewing angle and the luminance uniformity are used as the two optical quality objectives. Based on the experimental designs, the response surface methodology is applied to identify the functional relationships between input factors (i.e., design parameters) and their two output responses. Then, the dual response model is applied to determine the optimal solutions. In addition, the -constrained method is developed to identify the optimal design parameter settings. Moreover, the weighted sum model based on the quality loss function is proposed to consider the trade-off between two conflicting objectives. The final results show that the proposed models are far more effective than the existing method in LED lens design.
PL
Głównym celem tego artykułu jest zaproponowanie dwóch dwuobiektywnych, solidnych modeli optymalizacji konstrukcji dla konstrukcji soczewek LED. Kąt widzenia i jednorodność luminancji są używane jako dwa optyczne cele jakości. W oparciu o projekty eksperymentalne zastosowano metodologię powierzchni odpowiedzi w celu zidentyfikowania funkcjonalnych zależności między czynnikami wejściowymi (tj. parametrami projektowymi) a ich dwiema odpowiedziami wyjściowymi. Następnie opracowywany jest model podwójnej odpowiedzi w celu określenia optymalnych rozwiązań. Ponadto zaproponowano metodę z ograniczeniami  w celu identyfikacji optymalnych ustawień parametrów projektowych. Kompromis między dwoma sprzecznymi celami może być zagrożony przy użyciu proponowanych modeli. Ostateczne wyniki pokazują, że proponowane modele są znacznie bardziej efektywne niż dotychczas stosowane metody projektowania soczewek LED.
Rocznik
Strony
55--59
Opis fizyczny
Bibliogr. 30 poz., rys., tab.
Twórcy
autor
  • Faculty of Engineering and Technology, Quy Nhon University, Vietnam
  • Electrical and Electronics Engineering Faculty, Ho chi Minh University of Technology and Education, Vietnam
Bibliografia
  • [1] Zhao S., Wang K., Chen F., Wu D., & Liu S., Lens design of LED searchlight of high brightness and distant spot, JOSA A, 28 (5), (2011), 815-820.
  • [2] Yip W. S., To S., & Wang W. K., Design of an optical lens for LED lighting using a hybrid principal components analysis– Taguchi method, Lighting Research & Technology, 51(5), (2019), 788-802.
  • [3] Ding Y., Liu X., Zheng Z. R., & Gu P. F., Freeform LED lens for uniform illumination, Optics express, 16(17), (2008), 12958- 12966.
  • [4] Wang K., Chen F., Liu Z., Luo X., & Liu S., Design of compact freeform lens for application specific light-emitting diode packaging, Optics Express, 18(2), (2010), 413-425.
  • [5] Babadi S., Ramirez-Iniguez R., Boutaleb T., & Mallick T., An optimisation of a freeform lens design for LED street lighting, in 2016 International Conference for Students on Applied Engineering (ICSAE), Oct. 2016, pp. 89–92. doi: 10.1109/ICSAE.2016.7810167.
  • [6] Chen E., & Yu F., Design of LED-based reflector-array module for specific illuminance distribution, Optics Communications, 289, (2013), 19-27.
  • [7] Mendes-Lopes J., Benítez P., Miñano J. C., & Santamaría A., Simultaneous multiple surface design method for diffractive surfaces, Optics Express, 24(5), (2016), 5584-5590.
  • [8] Zhu X., Zhu Q., Wu H., & Chen C., Optical design of LED-based automotive headlamps, Opt. Laser Technol., vol. 45, pp. 262–266, Feb. 2013, doi: 10.1016/j.optlastec.2012.05.040.
  • [9] Liu P. et al., Uniform illumination design by configuration of LEDs and optimization of LED lens for large-scale color-mixing applications, Appl. Opt., vol. 52, no. 17, pp. 3998–4005, Jun. 2013, doi: 10.1364/AO.52.003998.
  • [10] Lin C. J. & Liu Y. C., Image backlight compensation using neuro-fuzzy networks with immune particle swarm optimization, Expert Syst. Appl., vol. 36, no. 3, Part 1, pp. 5212–5220, Apr. 2009, doi: 10.1016/j.eswa.2008.06.109.
  • [11] Kwak T. S., Suzuki T., Bae W. B., Uehara Y., & Ohmori H., Application of neural network and computer simulation to improve surface profile of injection molding optic lens, J. Mater. Process. Technol., vol. 170, no. 1, pp. 24–31, Dec. 2005, doi: 10.1016/j.jmatprotec.2005.04.099.
  • [12] Yang T., & Lu J. C., A hybrid dynamic pre-emptive and competitive neural-network approach in solving the multi-objective dispatching problem for TFT-LCD manufacturing, International Journal of Production Research, 48(16), (2010), 4807-4828.
  • [13] Lokesh J., Padmasali A. N., Mahesha M. G., & Kini S. G., Comparison and validation of neural network models to estimate LED spectral power distribution. Lighting Research & Technology, 55(3), (2023), 281-299.
  • [14] Feng Q., Li Q., Wang Y., Wu C., & Lv G., The design and optimization of lens array for LED backlight in LCD imaging engine of helmet-mounted display, Journal of the Society for Information Display, 25(5), (2017), 312-319.
  • [15] Su Z., Xue D., & Ji Z., Designing LED array for uniform illumination distribution by simulated annealing algorithm, Optics express, 20(106), (2012), A843-A855.
  • [16] Wu R., Zheng Z., Li H., & Liu X., Optimization design of irradiance array for LED uniform rectangular illumination, Appl. Opt., vol. 51, no. 13, pp. 2257–2263, May 2012, doi: 10.1364/AO.51.002257.
  • [17] P. P. Banik, R. Saha, and K.-D. Kim, LED color prediction using a boosting neural network model for a visual-MIMO system, Opt. Commun., vol. 437, pp. 139–147, Apr. 2019, doi: 10.1016/j.optcom.2018.12.027.
  • [18] Brüning R., Verhoek M., & Lippmann U., Neural Network for Optical Performance Estimation and Advanced Lens Combination, EPJ Web Conf., vol. 266, p. 03005, 2022, doi: 10.1051/epjconf/202226603005.
  • [19] Fang Y. C., Tzeng Y. F., & Li S. X., Multi-objective design and extended optimization for developing a miniature light emitting diode pocket-sized projection display, Optical review, 15, (2008), 241-250.
  • [20] Yi-Chin F., Yih-Fong T., & Si-Xiang L., A Taguchi PCA fuzzy-based approach for the multi-objective extended optimization of a miniature optical engine, J. Phys. Appl. Phys., vol. 41, no. 17, p. 175108, Aug. 2008, doi: 10.1088/0022-3727/41/17/175108.
  • [21] Chen W. C., Lai T. T., Wang M. W., & Hung H. W., An optimization system for LED lens design, Expert Syst. Appl., vol. 38, no. 9, pp. 11976–11983, Sep. 2011, doi: 10.1016/j.eswa.2011.03.092.
  • [22] Chen W. C., Liu K. P., Liu B., & Lai T. T., Optimization of optical design for developing an LED lens module, Neural Comput. Appl., vol. 22, no. 3, pp. 811–823, Mar. 2013, doi: 10.1007/s00521-012-0990-6.
  • [23] Wang J. C., Fan Y. C., Fang T. H., Tran A. S., & Cheng Y.-T., Optimization of optical uniformity factors of backlight module using robust design method, Opt. Appl., vol. Vol. 52, no. nr 1, 2022, doi: 10.37190/oa220101.
  • [24] León R. V., Shoemaker A. C., & Kacker R. N., Performance Measures Independent of Adjustment: An Explanation and Extension of Taguchi’s Signal-to-Noise Ratios, Technometrics, vol. 29, no. 3, pp. 253–265, 1987, doi: 10.2307/1269331.
  • [25] Box G., Signal-to-Noise Ratios, Performance Criteria, and Transformations, Technometrics, vol. 30, no. 1, pp. 1–17, 1988, doi: 10.2307/1270311.
  • [26] Box G., Bisgaard S., & Fung C., An explanation and critique of taguchi’s contributions to quality engineering, Qual. Reliab. Eng. Int., vol. 4, no. 2, pp. 123–131, 1988, doi: 10.1002/qre.4680040207.
  • [27] Nair V. N. et al., Taguchi’s Parameter Design: A Panel Discussion, Technometrics, vol. 34, no. 2, pp. 127–161, 1992, doi: 10.2307/1269231.
  • [28] Vining G. G. & Myers R. H., Combining Taguchi and Response Surface Philosophies: A Dual Response Approach, J. Qual. Technol., vol. 22, no. 1, pp. 38–45, Jan. 1990, doi: 10.1080/00224065.1990.11979204.
  • [29] Box G. E. P. & Wilson K. B., On the Experimental Attainment of Optimum Conditions, J. R. Stat. Soc. Ser. B Methodol., vol. 13, no. 1, pp. 1–45, 1951.
  • [30] Myers R. H., Response surface methodology—current status and future directions, Journal of Quality Technology, 31(1), (1999), 30-44.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d3b0ff30-ed58-4953-b04f-8e16ed3b1dc0
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.