• Vol 10, No 2 (2019)
  • Mechanical Engineering

Optimization of the Micro Molding of a Biconcave Structure

Min-Wen Wang, Fatahul Arifin, Jyun-Yan Huang

Corresponding email: farifinus@yahoo.com


Cite this article as:
Wang, M., Arifin, F., Huang, J., 2019. Optimization of the Micro Molding of a Biconcave Structure. International Journal of Technology. Volume 10(2), pp. 269-279
133
Downloads
Min-Wen Wang Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Fatahul Arifin -Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan -Department of Mechanical Engineering, Politeknik Negeri Sriwijaya, Palembang
Jyun-Yan Huang Department of Mechanical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan
Email to Corresponding Author

Abstract
image

Micro molding is a rapid and cost-effective micro manufacturing technology. However, during the manufacturing process, different kinds of products usually face problems with uneven thicknesses or particular structure designs, resulting in uneven products shrinkage. This research focuses on exploring the problems of molding a micro part with biconcave structure and sharp edges. Using the Taguchi Experimental Method, it aims to find the critical molding parameters which influence the moldability of the micro part, as well as to minimize the shrinking of part. This study used polypropylene (PP) for the injection molding and successfully resolved the void defect which appeared in the center of this biconcave structure. Melt temperature contributed the highest percentage (41.235%) to the shrinkage, followed by injection speed (31.947%) and mold temperature (12.887%). The optimal process parameters for injection molding obtained from the experiment which are melt temperature 240oC mold temperature 90oC, and injection speed of 30 mm/s and the shrinkage rate is 1.641%.

Biconcave structure; Injection molding; Sharp edges; Shrinkage

Introduction

The development of technology and science has progressed rapidly, as can be seen in the development of computer hardware products, digital cameras, mobile phones and other electronic goods. Many electronic devices are made as thin and small as possible, which contributes to their popularity. Consequently, this poses some difficulties for manufacturers to achieve such small sizes with high levels of accuracy if they still apply traditional manufacturing processes.

One example of a small electronic devices is the LED (Light emitting diode). A plastic lens is usually mounted on the LED chip to obtain the desired light distribution patterns. Chen et al. (2011) and our own lab (Molding Technology Synergy Lab./Kaohsiung) have developed a LED lens with biconcave structure (Figure 1), giving both high luminous uniformity (93.35%) and high viewing angle (135o).

One of the popular technologies for making plastic lenses is injection molding. This manufacturing method has advantages over other processes, in that it can mass produce parts rapidly and effectively (Hasnan et al., 2017). In recent years, injection molding  technology  has evolved   to   micro-injection   molding,  which  can   produce  parts   with   small  volume   and microfeatures with high precision and at low cost. The appearance, weight, and size of the machines are evidence of the difference between conventional injection molding and micro-injection molding machines. From their appearance, conventional injection machine are more prominent than a micro injection ones, but typically micro-injection machines require higher stability and controllability to make high precision micro parts.

The Taguchi method is a popular method used in the molding industry to obtain the best part quality. It simply uses the response table and graph from the experiment data to be analyzed, and then directly finds the combination of levels that gives the optimal response (Hadiyat & Wahyudi, 2013; Pavani et al., 2017). The parameter design is an optimized part of the Taguchi method and is often used to improve the product and process design. Its intention is to find a set of optimal parameter level combinations, so that the average target value can be reached with smallest variance, which means that the loss of the quality characteristic is minimized.

The micro-injection technique has recently been used, especially for making small plastic parts with high precision requirement. In addition to the Taguchi experimental method, Lin et al., (2004) worked on a plastic micro-injection part technique to manufacture high aspect-ratio optical fiber ferrules. They used Taguchi’s design experiment to evaluate the effect of parameters on the final properties, finding that mold temperature, holding pressure, and holding time could improve the alignment accuracy and lifetime of the microcore pin and minimize volumetric shrinkage. This new concept can be applied in the molding of microtubes. Pal et al. (2012) studied the micro-injection molding process of polypropylene dog bone shaped bars using Taguchi method; the results of their research showed the key micro-injection molding process parameters were injection pressure, holding time, and melt temperature. Lee and Lin (2013) selected melt temperature, mold temperature, packing pressure, holding time and cooling time as control factors, then employed the Taguchi method and finite element analysis to explore the LED lens molding and to find the relationship between shrinkage and the injection molding process parameters. Their results show that the effect of melt temperature on the shrinking of the LED lens has the most significant contribution, while cooling time made the smallest contribution. Skrlec and Klemenc, (2016) show that micro-injection molding parts quality are influenced by processing parameters such as injection speed, holding pressure, holding time, injection pressure, mold temperature, and melt temperature. Jiang et al. (2018) investigated the relationship between the morphlogical evolution and mechanical properties of PC/PET blends prepared by micro injection molding by applying Taguchi method; their results showed that the molecular of PC/PET first increases, then decreases in line with increased injection speed. Masato et al. (2018) used micro injection molding to produce medical and aerospace parts, and also applied Taguchi experiment on their study. They selected injection speed, melt and mold temperature, and holding pressure as the main processing parameters and found that the mold temperature and injection speed are the most effective parameters in the improvement in the dimension and the quality of molded part.

Computer-aided-engineering is a time saving tool for both molding part design and mold design. Marhöfer et al. (2013) employed Moldflow injection molding simulation software in micro-injection molding in order to obtain the optimum injection molding process parameter combination before doing the actual micro-injection molding process. Wang et al. (2018) employed Moldex3D in the micro injection molding to produce Blu-ray pick up lens in order to establish the optimum injection molding process co mbination. They demonstrated the possibility of using mold filling simulation software assisted in micro molding.

Chen et al. (2011) and our own lab have integrated optical simulation software and neural networks to design biconcave structured LED lens, but have not actually made such as a lens. It has non-uniform thickness distribution and with sharp edges that could causes defects in the molding process. The aim of this research is to study the moldability of this lens structure. Moldex3d molding simulation software will be implemented in the mold design and the Taguchi Method will also be applied to investigate the effects of molding parameters on the part’s quality, and furtherly to find the optimal parameter combination for micro injection molding of the part.

Conclusion

This study has investigated the effect of micro-injection molding parameters on the size of shrinkage of double-concave spherical structures. Appropriate molding parameters should be set to successfully mold this micro part without defects. Melt temperature is the dominant factor in the molding of the part, because this affects the melt viscosity and the cooling rate, and subsequently influences the packing efficiency.

Successful molding using PP material was achieved in this study without consideration of a draft angle, which is not the same when molding more rigid PC and PMMA materials. A draft angle should be considered when molding micro parts with rigid and brittle materials, as it will be much easier for parts to be released cleanly from the mold.

Acknowledgement

This work was supported by the Ministry of Science and Technology (MOST) Taiwan under the grant of MOST 103-2221-E-151-061. The financial support is gratefully acknowledged.

References

Chen, W.C., Lai, T.T., Wang, M.-W., Huang, H.-W., 2011. An Optimization System for LED Lens Design. Expert Systems with Applications, Volume 38(9), pp. 11976–11983

Hadiyat, M.A., Wahyudi, R.D., 2013. Integrating Steepest Ascent for the Taguchi Experiment: A Simulation Study. International Journal of Technology, Volume 4(3), pp. 280–287

Hasnan, A., Putra, N., Septiadi, W.N., Ariantara, B., Abdullah N.A., 2017. Vapor Chamber Utilization for Rapid Cooling in the Conventional Plastic Injection Molding Process. International Journal of Technology, Volume 8(4), pp. 690–697

Helleloid, G.T., 2001. On the Computation of Viscosity-shear Rate Temperature Master Curves for Polymeric Liquids. Journal of Applicable Mathematics, Volume 1, pp. 1–11

Jiang, J., Wang, S., Hou, J., Zhang, K., Wang, X., Li, Q., Liu, G., 2018. Effect of Injection Velocity on the Structure and Mechanical Properties of Micro Injection Molded Polycarbonate/polyethylene Terephthalate Blends. Journal Material and Design, Volume 141, pp. 132–141

Lee, K., Lin, J.C., 2013. Optimization of Injection Molding Parameters for LED Lampshade. Transactions of the Canadian Society for Mechanical Engineering, Volume 37(3), pp. 313–323

Lin, Z.G., Tseng, S.C., Wang, J., Su, Y.C., 2004. A Study of Micro Injection Molding for High-Aspect-Ratio Optical Fiber Ferrules. Material Processing and Design: Modeling, Simulation and Application, Volume 712(1), pp. 1547–1551

Marhöfer, D.M., Tosello, G., Hansen H.N., Islam, A., 2013. Advancements on the Simulation of the Micro Injection Molding Process. In: Proceedings of the 10th International Conference on Multi-Material Micro Manufacture 2013, San Sebastian, Spain, 8-10 October

Masato, D., Sorgato, M., Babenko, M., Whiteside, B., Lucchetta, G., 2018. Thin-wall Injection Molding of Polystyrene Parts with Coated and Uncoated Cavities. Material & Design, Volume 141, pp. 286–295

Pal, R., Mukhopadhyay, S., Das, D., 2012. Optimization of Micro-injection Molding Process to Tensile Properties of Polypropylene. Indian Journal of Fibre & Textile Research, Volume 37(1), pp. 11–15

Pavani, P.N.L., Rao, R.P., Prasad, C.L.V.R.S.V., 2017. Synthesis and Experimental Investigation of Tribological Performance of a Blended (Palm and Mahua) Bio-lubricant using the Taguchi Design of Experiment (DOE). International Journal of Technology, Volume 8(3), pp. 418–427

Sedighi, R, Meiabadi, M.S., Sedighi, M., 2017. Optimisation of Gate Location based on Weld Line in Plastic Injection Moulding using Computer-aided Engineering, Artificial Neural Network, and Genetic Algorithm. International Journal of Automotive and Mechanical Engineering, Volume 14(3), pp. 4419–4431

Skrlec, A., Klemenc, J., 2016. Estimating the Strain-Rate-Dependent Parameters of the Cowper-Symonds and Johnson-Cook Material Models using Taguchi Arrays. Journal of Mechanical Engineering, Volume 62(4), pp. 220–230

Wang, M.-W., Chen, C.-H., Arifin, F., Lin, J.-J., 2018. Modeling and Analysis of Multi-shot Injection Molding of Blu-ray Objective Lens. Journal of Mechanical Science and Technology, Volume 32(10), pp. 4839–4849