Author Affiliations
1School of Science, Jiangnan University, Wuxi 214122, Jiangsu , China2Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Wuxi 214122, Jiangsu , Chinashow less
Fig. 1. Diagram of LED illumination system
Fig. 2. Schematic diagram of constructing free-form surface by weighted superposition algorithm
Fig. 3. Center coordinate points of surface after translation
Fig. 4. Diagram of intersection of ray and triangular mesh. (a) Triangle mesh surface; (b) intersection of line and triangle surface
Fig. 5. Diagram of intersection of rays and five free-form surfaces. (a) Single ray intersecting with five free-form surfaces; (b) k rays intersecting with five free-form surfaces
Fig. 6. Free-form surface data points after superposition
Fig. 7. Flow chart of optimization algorithm
Fig. 8. Schematic diagram of LED illumination system with key parameters
Fig. 9. Free-form surface after optimization
Fig. 10. Distributions of target surface illuminance before and after optimization. (a) Distribution of illuminance before optimization; (b) contour distribution of illuminance before optimization; (c) distribution of initial weight illuminance; (d) contour distribution of initial weight illuminance; (e) distribution of illuminance after optimization; (f) contour distribution of illuminance after optimization
Parameter | Value |
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LED size | 5 mm×5 mm | Inner sphere radius /mm | 10 | Lens material | PMMA | Distance of LED from target plane /mm | 8000 | Target plane size | 30000 mm×10000 mm | Particle number of population N | 50 | Optimization cycles T | 200 | Range of weight factors | [0.1,0.4] | Number of sample points on LED | 5 | Distribution of sample points on LED | |
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Table 1. Design parameters
Parameter | Initial weight factor | Optimized weight factor |
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| 0.2500000 | 0.2462467 | | 0.2500000 | 0.2024465 | | 0.2500000 | 0.1262445 | | 0.2500000 | 0.1915454 | | 0.2500000 | 0.2337455 |
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Table 2. Weight factors before and after optimization
Initial weighting factor | Weight factor after optimization | Uniformity | Number of iterations |
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(0.2500,0.2500,0.2500,0.2500,0.2500) | (0.2462,0.2024,0.1262,0.1915,0.2337) | 75.0% | 200 | (0.2630,0.1365,0.2207,0.1626,0.2170) | (0.2458,0.2054,0.1202,0.1895,0.2391) | 75.1% | 230 | (0.4412,0.0459,0.0643,0.2385,0.2102) | (0.2461,0.2025,0.1260,0.1917,0.2334) | 74.8% | 186 | (0.3319,0.3635,0.0617,0.2359,0.0051) | (0.2441,0.2015,0.1273,0.1955,0.2316) | 74.8% | 176 | (0.3027,0.2041,0.0732,0.2106,0.2094) | (0.2443,0.2032,0.1243,0.1926,0.2356) | 75.2% | 275 | (0.2902,0.1236,0.2229,0.1095,0.2538) | (0.2451,0.2015,0.1233,0.1896,0.2375) | 74.9% | 196 | (0.1255,0.2120,0.2509,0.1604,0.2512) | (0.2476,0.2066,0.1242,0.1902,0.2314) | 75.1% | 234 | (0.2799,0.0994,0.1129,0.2980,0.2099) | (0.2422,0.2017,0.1263,0.1936,0.2362) | 75.0% | 178 | (0.3146,0.2003,0.1961,0.0758,0.2132) | (0.2453,0.2085,0.1257,0.1899,0.2306) | 74.9% | 168 | (0.3246,0.1902,0.1763,0.0918,0.2143) | (0.2442,0.2032,0.1241,0.1901,0.2384) | 74.8% | 180 |
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Table 3. Optimization results of different initial weight factors