Optimization of optical design for developing an LED lens module

Chen, Wen‐Chin; Liu, Kai-Ping; Bing-hui, Liu; Lai, Tung-Tsan

doi:10.1007/s00521-012-0990-6

Cited by 13 publications

(8 citation statements)

References 7 publications

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“…Taguchi method [49] is well-functioning in the discontinuous region but poor for the use of an optimal optical design because of immoderate time consumption during the analyses. Angle and average illuminance were observed and indicated that the evolved system is applicable to the optical design of different LED lenses [50].…”

Section: First Studies Of Ga In Lens Designmentioning

confidence: 97%

“…• Systematic method used to optimize an optical design for a LED lens module Chen et al [50] Continued on next page 2014 • GAs used to optimize optical systems Neda HesamMah-moudiNezhad [17] 2015 • Investigation of the influence of aberrations on contact lenses and a hybrid neural-GA was used to optimize the aspherical lens design of myopic and astigmatic eyes Yen and Ye [10] • GA used to design a multifocal IOL design for the human eye Yen and Jin [11] • Hybrid genetic algorithm used to improve field curvature aberration in optical lenses Tsai and Fang [12]…”

Section: Reading Light Systemmentioning

confidence: 99%

Section: B Historical Perspectivementioning

confidence: 99%

See 2 more Smart Citations

Genetic algorithms for lens design: a review

Höschel

Lakshminarayanan

2018

J Opt

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Genetic algorithms (GAs) have a long history of over four decades. GAs are adaptive heuristic search algorithms that provide solutions for optimization and search problems. The GA derives expression from the biological terminology of natural selection, crossover, and mutation. In fact, GAs simulate the processes of natural evolution. Due to their unique simplicity, GAs are applied to the search space to find optimal solutions to various problems in science and engineering.Using GAs for lens design was investigated mostly in the 1990s, but were not fully exploited. But in the past few years there have been a number of newer studies exploring the application of GAs or hybrid GAs in optical design. In this paper we discuss the basic ideas behind GAs and demonstrate their application in optical lens design.

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Section: First Studies Of Ga In Lens Designmentioning

confidence: 97%

Section: Reading Light Systemmentioning

confidence: 99%

Section: B Historical Perspectivementioning

confidence: 99%

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Genetic algorithms for lens design: a review

Höschel

Lakshminarayanan

2018

J Opt

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“…Firstly, we used TracePro [29]- [31] to simulate the defect imaging system, and then gradually changed the depth value of the defect to obtain a series of defect spot images, finally we explored the relationship between the grayscale of the bright spot image center and the physical depth value of the defect. Fig.…”

Section: Optical Model Simulationmentioning

confidence: 99%

Vison-Based 3D Shape Measurement System for Transparent Microdefect Characterization

et al. 2019

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The main task of vision-based industrial defect inspection is to implement efficient non-contact visual quality control, i.e., to detect if there is a defect and to achieve an accurate 3D shape measurement of such a defect, and this kind of vision defect inspection system has been widely applied in various industrial application. However, it is still not the case in the inspection of transparent microdefect on the polarizer (which is the most important part of an LCD screen). Optical measurement devices (such as confocal microscopy) are often utilized to fulfil this task. To solve problems lied in the current confocal microscopy inspection system, such as expensive and non-real-time processing, this research aims to develop a novel vision-based 3D shape measurement system for polarizer transparent microdefect characterization. The innovation of this system, which has been verified by our optical model simulation, is that the 3D sizes of microdefect have a monotonically relation to the grayscale of the microdefect image. Hence, a microdefect imaging system, which could acquire defect image accurately, is first well designed and implemented. Then, a support vector regression (SVR) algorithm is derived by the trained data, i.e., 100 acquired defect images and its corresponding 3D shape value by confocal microscopy. Characterized 3D measurement of microdefect is thereby obtained by this SVR algorithm. 30 polarizer microdefect samples have been imaged and measured by our proposed system, and several important performance indicators, including processing speed, accuracy and system reproducibility, have been elaborately tested. The experimental results show that the proposed system could achieve a high-accuracy measurement but in a much faster and more efficient way than the confocal microscopy. Besides, this developed imaging system has been evaluated in real applications, and over 300 samples have been detected, which also validate the effectiveness of the proposed system.

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“…Moreover, Chen et al (2012) employed experimental design, response surface method and genetic algorithm in the plastic injection molding process optimization experiments, in which CAE simulation software and DOE experiment data were first used to conduct ANOVA analysis to select more important parameters, and then the regression model combined with genetic algorithm was utilized to obtain the best combination of process parameters. Furthermore, Chen et al (2013) applied the Moldflow analysis software to conduct LED lens module optical design development optimization experiments. However, the CAE simulation has neglected the internal and external noise interference control factors and the actual injection error still exists.…”

Section: Introductionmentioning

confidence: 99%

Study on the traditional method for communication between vlans of IoT based on education simulation technology

2015

Management, Information and Educational Engineering

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this paper proposes an optimization model of process parameters in Plastic Injection Molding (PIM). Firstly, the taguchi method is employed for experiment and data analysis in which the quality characteristics for the plastic injection product are length and warpage. Besides, the control factors for the process are melt temperature, injection velocity, packing pressure, packing time, and cooling time. Moreover, the signalto-noise (S/N) ratio and analysis of variance (ANOVA) are used to obtain a combination of parameter settings. Experimental data are set for the Response Surface Methodology (RSM) to analyze and create two quality predictors. two quality predictors, length and warpage, are associated with Genetic Algorithm (GA) to search for an optimal combination of process parameters that meets multiple-objective quality characteristics. the confirmation results show that the approach not only enhances the stability in the injection molding process including the quality in length and warpage but also saves the cost and time spent from the mold design to manufacturing.

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Optimization of optical design for developing an LED lens module

Cited by 13 publications

References 7 publications

Genetic algorithms for lens design: a review

Genetic algorithms for lens design: a review

Vison-Based 3D Shape Measurement System for Transparent Microdefect Characterization

Study on the traditional method for communication between vlans of IoT based on education simulation technology

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