臺灣博碩士論文加值系統

藍光物讀取頭物鏡是一種電子系統元件，可以微量射出成形技術來生產。本研究利用Zemax光學分析設計一型藍光物讀取頭物鏡，採用多層射出技術，以田口實驗方法結合BPNN-PSO和BPPN-GA類神經最佳化技術結合，尋找最佳成形參數，研究中並探討有無模具補償，對透鏡成形品質之影響。
由實驗結果可以看出，不管有無模具補償多層射出成形透鏡品質，都比一次射出透鏡好。模具補償後，多層噴射表面I的最佳形狀精度為3.97μm（BPNN-GA）和3.59μm（BPNN-PSO），而單次射出表面I的最佳形狀精度為6.26μm（BPNN-GA）和6.50 μm（BPNN-GA），顯示多層射出比單層射出在形狀精度上改善了35％(BPNN-GA最佳化)及45％(BPNN-PSO最佳化)。在表面II上多層射出的最佳形狀精度為3.40μm（BPNN-GA）和3.49μm（BPNN-PSO），單層射出表面II的最佳形狀精度為6.35μm（BPNN-GA）和6.22μm（BPNN-PSO），BPNN-GA和BPNN-PSO最佳化在形狀精度上分別提高約45％及46％。整合光學分析、多層射出、模具補償及類神經最佳化，本研究所開發的藍光讀取頭表面I及表面II之P-V分別為0.5117μm和0.7478μm，可以完全滿足產業要求。因此，相對於單次射出，多層射出成形技術是較有利的藍光讀取頭物鏡生產方式。

Blu-ray objective lens is one of the electronic parts, it can be produced by employing micro-injection molding technique. In this study, Multi-shot molding technology has been proposed and implemented in the molding of the Blu-ray objective lens designed with Zemax optical analysis software, Taguchi method combined with BPNN-PSO and BPPN-GA was applied to find the optimum processing parameters, the lens molding quality without/with mold compensation technology was investigated as well.
Based on the results of the experiments, it can be seen that the multi-shot injection molding process compared to the single-shot injection molding without/with mold compensation have better molding quality. After mold compensation, the optimal shape accuracy of Type I surface I was 3.97 μm (BPNN-GA) and 3.59 μm (BPNN-PSO), while the optimal shape accuracy of Type II surface I was 6.26 μm (BPNN-GA) and 6.50 μm (BPNN-GA). So, it means to improve by about 35 % for BPNN-GA and 45% for BPNN-PSO. The optimal shape accuracy of Type I surface II was 3.40 μm (BPNN-GA) and 3.49 μm (BPNN-PSO), while the optimal shape accuracy of Type II surface II was 6.35 μm (BPNN-GA) and 6.22 (BPNN-PSO) μm, which improve of about 46% for BPNN-GA and 45% for BPNN-PSO. Integrating optical analysis, multi-shot molding, mold compensation, neural-network optimization, the objective lens developed in this study has achieved P-V values of 0.5117 μm (surface I) and 0.7478 μm (surface II), respectively. That means P-V value of the multi-shot lens is less than 1 μm and can full fill the industrial needed. The results of this study indicate multi-shot molding is favorable for the production of Blu-ray pick up head objective lens.

CHAPTER
ABSTRACT iii
PREFACE vii
ACKNOWLEDGEMENTS viii
CONTENT ix
FIGURES xii
TABLES xv

I INTRODUCTION 1
1.1 Introduction 1
1.2 Research Purpose 2
1.3 Research Methods 6
1.4 Thesis Organization 6

II BASIC THEORY AND LITERATURE REVIEW 7
2.1 Multi-shot Optical Lens Injection Molding 7
2.2 Micro-Injection Molding Process 8
2.3 Defect in Injection Molding 11
2.4 Taguchi Experimental Method
13
2.4.1 Three stages of quality engineering 14
2.4.2 Quality characteristics of three factors 15
2.4.3 Loss function 16
2.5 Neural Network 19
2.5.1 Perception 20
2.5.2 Backpropagation 22
2.5.3 Radial-based function 23
2.6 Genetic Algorithm 24
2.6.1 The history of genetic algorithm 25
2.6.2 Stage of genetic algorithm process 25
2.7 Particle Swarm Optimization 27
2.8 Mold Compensation 29
2.9 Related Research Documents 29

III OPTICAL DESIGN, MOLD DESIGN AND MOLD FLOW ANALYSIS 33
3.1 Optical Design 33
3.1.1 Aspherical optical elements 33
3.1.2 The definition of aspherical lens 35
3.1.3 Blu-ray objective lens design 36
3.2 Mold Design 43
3.2.1 Mold gating system design 43
3.2.2 Mold system design 43
3.3 Mold Filling Analysis 46
3.3.1 Product model 47
3.3.2 Material and processing machine 48
3.4 Taguchi Experimental Plan 49
3.4.1 Design parameters process 49
3.4.2 Type I processing parameters 51
3.5 Mold Flow Analysis Results 61

IV INJECTION MOLDING EXPERIMENT 74
4.1 Experiment Procedure 74
4.2 Material Selection 75
4.3 Experiment Equipment 76
4.3.1 Micro-injection molding machine 76
4.3.2 Drying machine 78
4.3.3 Dimension measurement equipment 80
4.3.4 Shape precision measurement machine
80
4.4 Processing Window 81
4.5 Taguchi Experiment Method 87
4.5.1 Process parameters selection 87
4.5.2 Injection molding results 90
4.6 Back Propagation Neural Network (BPNN), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Experiments 96
4.7 Shape Accuracy 103
4.8 Mold Compensation 105
4.9 Taguchi Experiment with Mold Compensation Results 108
4.10 P-V Value Measurement 126

V DISCUSSION 128
5.1 Mold Flow Analysis Simulation Results 128
5.2 Injection Molding Results 129

VI CONCLUSION 134

REFERENCES 135

[1]Chen, D., Chen, D., Wang, B., The advantages of Blu-ray disc, proceeding of SPIE vol. 5966, (2005) doi: 10.117/12.649801.
[2]Nobuyoshi, M., Tohru, K., Kyu, T., Junji, H., Yuichiro, O., Nobuo, M., Plastic Objective Lens for Blu LD Optical Pick-up, Konica Minolta technology report 2 (2005) pp. 153-156.
[3]Mamat, A., Trochu, T. F., Sanschagrin, B., Analysis of Shrinkage by Dual Kriging for Filled and Unfilled Polypropylene Moulded Part, Polymer Engineering, and science, 35, (19) (1995), pp. 1511-1520.
[4]Tsai, K. M., Hsieh, C. Y., Lo, W. C., A Study of The Effect of Process Parameter for Injection Molding on The Surface Quality of Optical Lenses, Journal material processing Technology, 209, (2009) pp. 3469-3477, doi:10.1016/j. jmatprotec.2008.08.006.
[5]Kitayama, S., Natsume, S., Multi-Objective Optimization of Volume Shrinkage and Clamping Force for Plastic Injection Molding Via Approximate Sequential Optimization, Journal simulation Modelling Practice, and Theory, 48, (2014) pp. 35-44, http;//dx.do.org/10.1016/j.simpat.2014. 07.004.
[6]Wang, M. W., Micro-ceramic Injection Molding of a Multi-shot Micro Patterned Micro-part, International Journal Advance Manufacture Technology, 51 (2010) pp. 145-153.
[7]Mathivanan, D., Naouby, M., Vidhya, R., Minimization of Sink Mark Defects Injection Molding Process Taguchi Approach, International Journal of engineering science and technology, vol.2, no.2, (2010) pp. 13-22
[8]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, Journal of material Processing Technology, vol 170 (2005) pp. 24-31, doi: 10.1016/j.jmatprotec.2005.04.099
[9]Young, W. B., Effect of Process Parameters on Injection Compression Molding of Pick-up Lens, Journal Applied Mathematical Modelling, vol. 29, (2005) pp. 955-971, doi: 10.1016/j.apm.2005.02.004
[10]Wu, C. H., Chen, W. S., Injection Molding and Injection Compression Molding of Three-beam Grating of DVD Pick-up Lens, Journal Sensor and Actuator A Physical vol. 125 (2005) pp. 367-375, doi:10.1016/j.sna.2005.07.025
[11]Lu, X., Khim, L. S., A Statistical Experimental Study of Injection Molding on Optical Lenses, Journal of material Processing Technology 113, (2001) pp. 189-193
[12]Peter, G. W. M. P., Velden, J. L.V.D., Meijer, H. E. H., Multilayer Injection Molding Part2: Particle Tracking in Reactive Molding. International Polymer Process 9, (1994) pp. 258-265
[13]Maier, C., Giessauf, J., Steinbichler, G., Efficient Production of Thick-walled Parts, Kunststoffe International 9 (2013) pp. 15-19, www.kunststoffeinternational.com, Document Number. PE111447.
[14]Lin, J. C., Lee, K. S., Molding Analysis of Multi-cavity Aspheric Lens and Mold Designing, Advances in Materials and Processing Technologies, ISSN: 1662-8985, 83-86, (2009) pp. 77-87, http:// doi:10.4028/ www.scientific.net/AMR. 83-86.77, Trans Tech Publications, Switzerland
[15]Pundir, R., Chary, G. H. V. C., Dastidar, M. G. Application of Taguchi Method for Optimizing the Process Parameters for the Removal of Copper and Nickel by Growing Aspergillus sp, Water Resources and Industry, (2016) pp. 1-10, http://dx.doi.org/10.1016/j.wri. 2016.05.001.
[16]Oktem, H., Erzurumlu, T., Uzman, I., Application of Taguchi Optimization Technique in Determining Plastic Injection Molding Process Parameters for a Thin-shell Part, Materials & Design, 28, 4, (2017) pp. 1271-1278.
[17]Lee, K. S. Lin, J. C. Optimization of Injection Molding Parameters for LED Lampshade, Transactions of the Canadian Society for Mechanical Engineering, 37, (3) (2013) pp. 313-323.
[18]Erzurumlu, T., Ozcelik, B., Minimization of Warpage and Sink Index in Injection-Molded Thermoplastic Parts Using Taguchi Optimization Method, Materials & Design, 27, (10) (2006) pp. 853-861.
[19]Altan M., Reducing Shrinkage in Injection Moldings Via the Taguchi, ANOVA and Neural Network Methods. Materials and Design. 31(1) (2010) pp. 599-604.
[20]Hakimian, E., Sulong, A.B., Analysis of Warpage and Shrinkage Properties of Injection-molded Micro Gears Polymer Composites Using Numerical Simulations Assisted by the Taguchi method. Materials and Design. 42 (2012) pp.62-71.
[21]Hopman, C. Neuss, A., Weber, M., Walack, P. Multi Injection Molding of Thick-walled Optical Plastic Parts, AIP Conference Proceeding; 1593, (2014) pp. 146.
[22]ENGEL, Efficient Production of Thick-walled Part, Special Reprint Form Kunstoffe International, 2013.
[23]ARBURG, Eigth Stations, One Lens, The ARBURG Magazine, I 55, pp.24-25.
[24]Menges, G., Mohren, P., How to Make Injection Molds. Second Edition, New York: Hanser, (2013) pp. 89. ISBN 3-446-16305-0.
[25]Crisan, N., Descartes, S., Berhier, Y., Cavoret, J., Baud, D., Montalbano, F., Tribogical Assessment of the Interface Injection Mold/Plastic Part, Journal Tribology International, 100 (2016) pp. 388-399, http://dx.doi.org/10.1016/ j.triboint.2016.04.015
[26]Usama, M., Marson, A., S., Alcock, J., R., 2009, Micro-Injection Moulding of Polymer Microfluidic Devices, Journal microfluidics and nanofluidics, vol 7, no. 1, pp 1-28
[27]Surace, R., Trotta, G., Bellantone V., Fassi, I., The Micro-injection Moulding Process for Polymeric Components Manufacturing, New Technologies – Trends, Innovations and Research
[28]Cunha, A. M., Pouzada, A. S., Injection Moulding Process, Guimaraes: Universidade do Minho.
[29]Johannaber, F., Injection Molding Machines: a user’s guide, Third Edition, Munich: Hanser (1994), pp. 8-9. ISBN 3-446-17733-7.
[30]Plastic Technology, Available at: https://www.ptonline.com/articles/micromolding-sizing-up-the-challenges, Accessed, September 13, 2018.
[31]Zhao, J., Mayes, R. H., Chen, G., Xie, H., Chan, P.S., Effect of Process Parameters on the Micro Molding Process, Polymer Engineering and Science, 43, (2003) pp. 1542-1554.
[32]Yu, M. C., Young, W. B., Hsu, P. M., Micro-injection Molding with the Infrared Assisted Mold Heating System, Material Science and Engineering, 460-461 (2007) pp. 288-295.
[33]Ong, N. S., Koh, Y. H., Experimental Investigation Into Micro-injection Molding of Plastic Parts, Material Manufacturing Process, 20 (2005) pp. 245-253.
[34]Understanding Dimensional consistency, Available at:
https://www.pitfallsinmolding.com/dimncon.html, Accessed, Maret 13, 2019.
[35]Close, B. W., Hall, T. A., Disposable Needle Assembly Having Sensors Formed There in Permitting the Simultaneous Drawing and Administering of Fluids and Method of Forming the Tame, patent of US 6758835 B2.
[36]Gheorghe, O. C., Florin, T. D., Vlad, G. T., Gabriel, D. T., Optimization of Micro-injection Molding of Polymeric Medical Devices Using Software Tools, Procedia Engineering, 69 (2014) pp. 5690-5701.
[37]Dayhoff, J. E., Neural Network Architectures (An Introduction), USA, Van Nostrand Reinhold, 1990.
[38]Fausett, L., Fundamental of Neural Network (Architectures, Algorithms, and Applications) New Jersey: Prentice-Hall, 1994.
[39]Haykin, S., Neural Networks (A Comprehensive Foundation). New York: Macmillan Coolege Publishing Company.
[40]Erdei, G. Szarvas, G., Lorincz, E., Tolerancing Surface Accuracy of Aspheric Lenses Used for Imaging Purposes, Optical Design and Engineering, 5249 (2004) pp. 718-728.
[41]Rao, S. S., Engineering Optimization Theory and Practice, 4th edition,. Ed.: John Wiley and Sons, 2009.
[42]Spina, R., Walach, P., Schild, J., Hopmann, C., Analysis of Lens with Injection Molding, International Journal of Precision Engineering and Manufacturing, 13(11) (2012) pp. 2087-2095.
[43]Wang, C. Y., Wang, P. J., Analysis of Optical Properties in Injection Molded and Compression Molded Optical Lens, Applied Optics, 53(11) (2014) pp. 2523-2531.
[44]Bensingh, R.J., Boopathy, S.R., Jebaraj, C., Minimization of Variation in Volumetric Shrinkage and Deflection on Injection Molding of Bi-aspheric Lens Using Numerical Simulation, Journal of Mechanical Science and Technology 30 (11) (2016) pp. 5143-5152.
[45]Wang, M. W., Arifin, F., Huyah, T. T. N., Optimization of Molding Parameters fro Gear with Taguchi Method, IOP Conf.Series: Journal of Physics: Confr. Series 1167(2019)012001 doi: 10.1088/1742-6596/1167/1/012001.
[46]Chen, W. C, Tai, P. H., Wang, M. W., Deng, W. J, Chen, C. T., A Neural Network-based Approach for Dynamic Quality Prediction in a Plastic Injection Molding Process. Expert System with Applications, 35 (2008) pp. 843-849.
[47]Yin, F., Mao, H. Hua, L., Guo, W., Shu, M., Back Propagation Neural Network Modeling for Warpage Prediction and Optimization of Plastic Product During Injection Molding. Material and Design, 32(2011) pp. 1844-1850.
[48]Tsai, K.M., Luo, H-J., An Inverse Model for Injection Molding of Optical Lens Using Artificial Neural Network Couple with Genetic Algorithm, Journal International Manufacture, (2014) DOI 10.1007/s10845-014-0999-z.
[49]Tsai, C. Y., Gear Module Adjustment Method for Shrinkage Compensation of Injection Molded Plastic Gears Using Single Dedicated Hob, Mechanism and Machine Theory, 140 (2019) pp. 233-244.
[50]Chen, A. C. C., Vu, L. T., Qiu, T. T., Study on Injection Molding of Shell Mold for Aspheric Contact Lens Fabrication, Procedia Engineering, 184 (2017), pp. 344-349.
[51]Wang, Y., Livshits, I. L., Potemin, I. S., Zhdanom, D. D., Voloboy, A., Peculiarities of optical element manufacturing in the Chinese optical industry, Proc. SPIE 10815, Optical Design and Testing VIII, 108151P (5 November 2018); doi: 10.1117/12.2504244
[52]Differences Between Plastic and Glass lenses, Available at: http://www.differencebetween.net/object/difference-between-plastic-and-glass-lenses/ Accessed, December 13, 2018.
[53]Spherical aberration, Available at:
https://en.wikipedia.org/wiki/Spherical_aberration, Accessed, December 13, 2018.
[54]Aspherical lens, Available at: https://en.wikipedia.org/wiki/Aspheric_lens, Accessed, December 13, 2018.
[55]Kweon, G. I. Aspherical Lens Design by Using a Numerical Analysis, Journal of the Korean Physical Society, Vol. 51, No. 1, (2007) pp. 93 -103.
[56]Greynolds, A. W., 2002, Superconic and Subconic Surface Descriptions in Optical Design, Proc. SPIE., 4832, (2002) pp. 1-9
[57]Lin, J. J., Optimal Multi-Layer Molding of Blu-ray Pick-up Head Lens with Neural Network Technology, Master’s Thesis, National Kaohsiung University of Science and Technology, 2017.
[58]Technical Note: Optical Surface, https://www.newport.com/n/optical-surfaces, Accessed, March 30, 2019

[59]How dose Peak to Valley (P-V) and Root Mean Square (RMS) Affects the Quality of Your Optic?, https://www.shanghai-optics.com/about-us/resources/how-does-peak-to-valley-and-root-mean-square-affects-the-quality-of-your-optic/, Accessed, March 30, 2019.