A study of the effects of process parameters for injection molding on surface quality of optical lenses

Tsai, Kuo-Ming; Hsieh, Chung-Yu; Lo, Wei-Chun

doi:10.1016/j.jmatprotec.2008.08.006

Cited by 103 publications

(40 citation statements)

References 19 publications

(18 reference statements)

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“…Most applications are in the field of microoptics (such as CDs and DVDs) and microfluidic devices. Production of other molded microoptical components including optical gratings, optical switches, and waveguides [3][4][5] as well as a variety of molded microfluidic devices including pumps, capillary analysis systems, and lab-on-a-chip applications [6,7] is ongoing.…”

Section: Introductionmentioning

confidence: 99%

Gas-Assisted Heating Technology for High Aspect Ratio Microstructure Injection Molding

Chen

Lin

Chang

et al. 2013

Advances in Mechanical Engineering

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A hot gas is used for heating the cavity surface of a mold. Different mold gap sizes were designed. The mold surface temperature was heated to above the glass transition temperature of the plastic material, and the mold then closed for melt filling. The cavity surface can be heated to 130 ∘ C to assist the melt filling of the microfeatures. Results show that hot gas heating can improve the filling process and achieve 91% of the high aspect ratio microgrooves (about 640.38 m of the maximum of 700 m). The mold gap size strongly affects the heating speed and heating uniformity. Without surface preheating, the center rib is the highest. When the heating target temperature is 90 ∘ C or 100 ∘ C, the three microribs have a good uniformity of height. However, when the target temperature exceeds 100 ∘ C, the left side rib is higher than the other ribs.

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Section: Introductionmentioning

confidence: 99%

Gas-Assisted Heating Technology for High Aspect Ratio Microstructure Injection Molding

Chen

Lin

Chang

et al. 2013

Advances in Mechanical Engineering

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“…When the packing pressure was increased, the shrinkage and warpage were reduced drastically. In contrast, improper setting of processing parameters may induce devastating defects on the products, such as warpage, shrinkage, sink mark, and residual stress [17]. Therefore, determining the optimal processing parameters is performed routinely in the plastic injection moulding industry as it has a direct and dramatic influence on product quality and costs.…”

Section: Optimization Of Injection Moulding Processing Parametersmentioning

confidence: 99%

Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review

Fei

Mehat²,

Kamaruddin

2013

ISRN Industrial Engineering

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Determining the optimal processing parameter is routinely performed in the plastic injection moulding industry as it has a direct and dramatic influence on product quality and costs. In this volatile and fiercely competitive market, traditional trial-and-error is no longer sufficient to meet the challenges of globalization. This paper aims to review the research of the practical use of Taguchi method in the optimization of processing parameters for injection moulding. Taguchi method has been employed with great success in experimental designs for problems with multiple parameters due to its practicality and robustness. However, it is realized that there is no single technique that appears to be superior in solving different kinds of problem. Improvements are to be expected by integrating the practical use of the Taguchi method into other optimization approaches to enhance the efficiency of the optimization process. The review will shed light on the standalone Taguchi method and integration of Taguchi method with various approaches including numerical simulation, grey relational analysis (GRA), principal component analysis (PCA), artificial neural network (ANN), and genetic algorithm (GA). All the features, advantages, and connection of the Taguchi-based optimization approaches are discussed.

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“…Lu and Khim [6] and Spina et al [7] investigated some effects of the molding conditions on the surface contours and birefringence patterns of the lens, using a statistical experimental analysis method and grey relational component analysis coupled to the Taguchi design. Taking light transmission, surface waviness, and surface finish as the optimization objectives, Tsai et al [8] established linear, exponential, and nonlinear regression models for the objectives and predicted the optimal IMPP; they concluded that the nonlinear regression model had higher prediction accuracy. They established an inverse model of injection molding for form accuracy of optical lens by combining the artificial neural network (ANN) with a genetic algorithm (GA) [9].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of Injection Molding Process Parameters for the Precision Manufacturing of Plastic Optical Lens

Liu

Chen

Lin

et al. 2017

Mathematical Problems in Engineering

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Injection molding process parameters (IMPP) have a significant effect on the optical performance and surface waviness of precision plastic optical lens. This paper presents a set of procedures for the optimization of IMPP, with haze ratio (HR) reflecting the optical performance and peak-to-valley 20 (PV 20 ) reflecting the surface waviness as the optimization objectives. First, the orthogonal experiment was carried out with the Taguchi method, and the results were analyzed by ANOVA to screen out the IMPP having a significant effect on the objectives. Then, the 3 4 full-factor experiment was conducted on the key IMPP, and the experimental results were used as the training and testing samples. The BPNN algorithm and the M-SVR algorithm were applied to establish the mapping relationships between the IMPP and objectives. Finally, the multiple-objective optimization was performed by applying the nondominated sorting genetic algorithm (NSGA-II), with the built M-SVR models as the fitness function of the objectives, to obtain a Pareto-optimal set, which improved the quality of plastic optical lens comprehensively. Through the experimental verification on the optimization results, the mean prediction error (MPE) of HR and PV 20 is 7.16% and 9.78%, respectively, indicating that the optimization method has high accuracy.

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A study of the effects of process parameters for injection molding on surface quality of optical lenses

Cited by 103 publications

References 19 publications

Gas-Assisted Heating Technology for High Aspect Ratio Microstructure Injection Molding

Gas-Assisted Heating Technology for High Aspect Ratio Microstructure Injection Molding

Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review

Multiobjective Optimization of Injection Molding Process Parameters for the Precision Manufacturing of Plastic Optical Lens

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