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江南大学理学院 江苏省轻工光电工程技术研究中心,江苏 无锡,214122
Received:03 February 2016,
Revised:14 March 2016,
Published:05 June 2016
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黄逸峰, 李潇, 高培丽等. 侧入式导光板网点全自动优化设计研究[J]. 发光学报, 2016,37(6): 751-757
HUANG Yi-feng, LI Xiao, GAO Pei-li etc. Automatic Optimization Design of Dot Patterns for Edge-lit LGP[J]. Chinese Journal of Luminescence, 2016,37(6): 751-757
黄逸峰, 李潇, 高培丽等. 侧入式导光板网点全自动优化设计研究[J]. 发光学报, 2016,37(6): 751-757 DOI: 10.3788/fgxb20163706.0751.
HUANG Yi-feng, LI Xiao, GAO Pei-li etc. Automatic Optimization Design of Dot Patterns for Edge-lit LGP[J]. Chinese Journal of Luminescence, 2016,37(6): 751-757 DOI: 10.3788/fgxb20163706.0751.
为解决目前侧入式导光板网点优化设计中存在经验式手动优化繁琐、且很难达到一个高均匀效果的问题
提出了一种基于模糊优化理论的网点自动优化设计方法。网点形状采用锥形结构
为使目标面上各处的光能分布主要由其正下方网点的散射光贡献
分析了其半顶角与网点位置的关系
并使得网点半顶角仅由其位置决定。以网点半径为主要优化参数
通过隶属度函数将网点结构模糊化
并自定义评价函数来解模糊化实现均匀照明。进而
采用动态数据交换(DDE)技术将Matlab与TracePro进行联立
通过Matlab语言与Scheme语言混编控制TracePro自动进行数据交换、光线追迹与模糊优化。导光板网点优化设计实例仿真结果表明
优化后均匀度达到92.17%
光能利用率达到70.37%
全程实现自动优化
无需任何手动调节。
In order to solve the problems including the complication of the empirical manual optimization and the difficulty of achieving an ideal effect in edge-lit LGP
an automatic optimization design method based on fuzzy optimization theory is proposed. The shape of the dot is considered to be conical
and the relationship between the semi-vertical angle and the position of the dot is analyzed. It is found that the correlation degree between the light distribution in target face and the dot distribution under it can be increased
and the semi-vertical angle is merely determined by the position of the dot. The radius of the dot is adopted as the main optimization parameter. A membership function is used to fuzz the dot structure
and a self-defined evaluation function is used in the defuzzification to realize uniform illumination. Furthermore
the data exchange
ray tracing and fuzzy optimization are then automatically performed by TracePro
via
dynamic data exchange (DDE) between Matlab and TracePro. It is demonstrated that the uniformity and the light utilization efficiency after fuzzy optimization can reach 92.17% and 70.37%
respectively. The advantage of our scheme is that the whole fuzzy optimization is purely automatic without any manual adjustment.
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