Robust parameter search for IC tray injection molding using regrind resin
-
Ming-Shyan Huang
and
Shih-Chih Nian
Abstract
Quality consistency is essential in maximizing the productivity rate of the injection molding process and minimizing the production cost. The quality consistency problem is particularly acute in the case of injection molding processes performed using regrind resin, for which the rheological properties are less uniform and more unpredictable than those of virgin material. Accordingly, the present study proposes a two-stage approach for optimizing the injection molding process parameters in such a way as to achieve a consistent molding quality over repeated injection molding cycles. In the first stage, the values of the injection speed/pressure, velocity-to-pressure (V/P) switchover point, and packing pressure are individually determined based on an inspection of the cavity pressure profile and machine parameters provided by the injection molding machine controller. In the second stage, a robust parametric search method based on a first-order regression model is employed to determine the optimal combination of the process parameter settings. Using an Integrated Circuit (IC) tray fabricated from regrind resin for illustration purposes, the results confirm that the proposed method overcomes the problem of small variations in the melt quality and therefore provides an effective technique for improving the yield rate and quality of the continuous mass production.
Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Nian, S. C., Wu, C. Y., Huang, M. S. Int. Commun. Heat Mass Transf. 2015, 61, 102–110. https://doi.org/10.1016/j.icheatmasstransfer.2014.12.008.Search in Google Scholar
2. Nian, S. C., Li, M. H., Huang, M. S. Int. J. Heat Mass Transf. 2015, 86, 358–368. https://doi.org/10.1016/j.ijheatmasstransfer.2015.03.027.Search in Google Scholar
3. Kazmer, D., Barkan, P. Polym. Eng. Sci. 1997, 37, 1865–1879. https://doi.org/10.1002/pen.11837.Search in Google Scholar
4. Nian, S. C., Fang, Y. C., Huang, M. S. Polymers 2019, 11, 1348–1362. https://doi.org/10.3390/polym11081348.Search in Google Scholar PubMed PubMed Central
5. Hamad, K., Kaseem, M., Deri, F. Polym. Degrad. Stabil. 2013, 98, 2801–2812. https://doi.org/10.1016/j.polymdegradstab.2013.09.025.Search in Google Scholar
6. Yoshida, T., Ishiaku, U. S., Okumura, H., Baba, S., Hamada, H. Compos. Part A Appl. Sci. Manuf. 2006, 37, 2300–2306. https://doi.org/10.1016/j.compositesa.2006.02.019.Search in Google Scholar
7. Gu, F., Hall, P., Miles, N. J. J. Clean. Prod. 2016, 115, 343–353. https://doi.org/10.1016/j.jclepro.2015.12.062.Search in Google Scholar
8. Gu, F., Hall, P., Miles, N. J. J. Clean. Prod. 2016, 137, 632–643. https://doi.org/10.1016/j.jclepro.2016.07.028.Search in Google Scholar
9. Khademi, F., Ma, Y., Ayranci, C., Choi, K., Duke, K. Polym. Eng. Sci. 2016, 56, 1283–1290. https://doi.org/10.1002/pen.24363.Search in Google Scholar
10. Boronat, J., Segui, V. J., Peydro, M. A., Reig, M. J. J. Mater. Process. Technol. 2009, 209, 2735–2745. https://doi.org/10.1016/j.jmatprotec.2008.06.013.Search in Google Scholar
11. Zuidema, H., Peters, G. W. M., Meijer, H. E. H. J. Appl. Polym. Sci. 2001, 82, 1170–1186. https://doi.org/10.1002/app.1951.Search in Google Scholar
12. van der Beek, M. H. E., Peters, G. W. M., Meijer, H. E. H. Int. Polym. Process. 2005, 20, 111–120. https://doi.org/10.3139/217.1872.Search in Google Scholar
13. van der Beek, M. H. E., Peters, G. W. M., Meijer, H. E. H. Macromol. Mater. Eng. 2005, 290, 443–455. https://doi.org/10.1002/mame.200500027.Search in Google Scholar
14. Wang, J., Mao, Q. Adv. Polym. Technol. 2013, 32, E474−E485. https://doi.org/10.1002/adv.21294.Search in Google Scholar
15. Min, B. H. J. Mater. Process. Technol. 2003, 136, 1–6. https://doi.org/10.1016/S0924-0136(02)00445-4.Search in Google Scholar
16. Chen, Z., Turng, L. S. Polym. Eng. Sci. 2007, 47, 852–862. https://doi.org/10.1002/pen.20769.Search in Google Scholar
17. Michaeli, W., Schreiber, A. Adv. Polym. Technol. 2009, 28, 65–76. https://doi.org/10.1002/adv.20153.Search in Google Scholar
18. Hopmann, C., Reßmann, A. Self-optimizing in injection molding and the problem at compensating viscosity fluctuations. in Proceedings of the SPE/ANTEC 2014, Las Vegas, NV, USA, 2014, 2, 1706–1710.Search in Google Scholar
19. Zhang, J. F., Zhao, P., Zhao, Y., Huang, J. Y., Xia, N., Fu, J. Z. Sens. Actuator A Phys. 2019, 285, 118–126. https://doi.org/10.1016/j.sna.2018.11.009.Search in Google Scholar
20. Gornik, C. Mater. Sci. Forum 2008, 591–593, 174–178. https://doi.org/10.4028/www.scientific.net/msf.591-593.174.Search in Google Scholar
21. Kurt, M., Saban, K. O., Kaynak, Y., Atakok, G., Girit, O. Mater. Des. 2009, 30, 3217–3224. https://doi.org/10.1016/j.matdes.2009.01.004.Search in Google Scholar
22. Wang, J., Xie, P., Ding, Y., Yang, W. Polym. Test. 2009, 28, 228–234. https://doi.org/10.1016/j.polymertesting.2008.09.003.Search in Google Scholar
23. Xie, P. C., Wang, X. H., Wu, T., Ding, Y. M., Yang, W. M. Int. Polym. Process. 2014, 29, 184–190. https://doi.org/10.3139/217.2683.Search in Google Scholar
24. Gao, R. X., Tang, X., Gordon, G., Kazmer, D. O. CIRP Ann. Manuf. Technol. 2014, 63, 493–496. https://doi.org/10.1016/j.cirp.2014.03.041.Search in Google Scholar
25. Gordon, G., Kazmer, D. O., Tang, X., Fan, Z., Gao, R. X. Int. J. Adv. Manuf. Technol. 2015, 78, 1381–1391. https://doi.org/10.1007/s00170-014-6706-6.Search in Google Scholar
26. Lin, C. C., Wang, W. T., Kuo, C. C., Wuet, C. L. Int. J. Mech. Mechatron. Eng. 2014, 8, 687–691.10.1023/A:1026086712672Search in Google Scholar
27. Zhao, P., Zhou, H., He, Y., Cai, K., Fu, J. Int. J. Adv. Manuf. Technol. 2014, 72, 765–777. https://doi.org/10.1007/s00170-014-5711-0.Search in Google Scholar
28. Huang, M. S., Nian, S. C., Chen, J. Y., Lin, C. Y. Precis. Eng. 2018, 51, 647–658. https://doi.org/10.1016/j.precisioneng.2017.11.007.Search in Google Scholar
29. Zhao, P., Zhang, J., Dong, Z., Huang, J., Fu, J., Turng, L. S. Adv. Polym. Technol. 2020, 2020, 1–22. https://doi.org/10.1155/2020/7023616.Search in Google Scholar
30. Huang, M. S. J. Mater. Process. Technol. 2007, 183, 419–424. https://doi.org/10.1016/j.jmatprotec.2006.10.037.Search in Google Scholar
31. Huang, M. S., Lin, T. Y. J. Mater. Process. Technol. 2008, 198, 436–444. https://doi.org/10.1016/j.jmatprotec.2007.07.022.Search in Google Scholar
32. Huang, M. S., Lin, T. Y. Int. J. Heat Mass Transf. 2008, 51, 5828–5837. https://doi.org/10.1016/j.ijheatmasstransfer.2008.05.016.Search in Google Scholar
33. Govaerts, B., Noël, J. Qual. Reliab. Eng. Int. 2005, 21, 511–512. https://doi.org/10.1002/qre.737.Search in Google Scholar
© 2020 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Material properties
- Compatibility of energetic plasticizers with the triblock copolymer of polypropylene glycol-glycidyl azide polymer-polypropylene glycol (PPG-GAP-PPG)
- Simultaneous improvement of mechanical and conductive properties of poly(amide-imide) composites using carbon nano-materials with different morphologies
- Thermal and mechanical behavior of SBR/devulcanized waste tire rubber blends using mechano–chemical and microwave methods
- Preparation and assembly
- Development and characterization of ethyl cellulose nanosponges for sustained release of brigatinib for the treatment of non-small cell lung cancer
- Polysulfone nanofiltration membranes enriched with functionalized graphene oxide for dye removal from wastewater
- Zn(II)-selective poly (vinyl chloride) (PVC) membrane electrode based on Schiff base ligand 2-benzoylpyridine semicarbazone as an ionophore
- Effectiveness assessment of TiO2-Al2O3 nano-mixture as a filler material for improvement of packaging performance of PLA nanocomposite films
- Boron nitride nanoplatelets as two-dimensional thermal fillers in epoxy composites: new scenarios at very low filler loadings
- Engineering and processing
- Study on bubble morphology at interface of laser direct joint between carbon fiber reinforced thermoplastic (CFRTP) and titanium alloy
- Robust parameter search for IC tray injection molding using regrind resin
Articles in the same Issue
- Frontmatter
- Material properties
- Compatibility of energetic plasticizers with the triblock copolymer of polypropylene glycol-glycidyl azide polymer-polypropylene glycol (PPG-GAP-PPG)
- Simultaneous improvement of mechanical and conductive properties of poly(amide-imide) composites using carbon nano-materials with different morphologies
- Thermal and mechanical behavior of SBR/devulcanized waste tire rubber blends using mechano–chemical and microwave methods
- Preparation and assembly
- Development and characterization of ethyl cellulose nanosponges for sustained release of brigatinib for the treatment of non-small cell lung cancer
- Polysulfone nanofiltration membranes enriched with functionalized graphene oxide for dye removal from wastewater
- Zn(II)-selective poly (vinyl chloride) (PVC) membrane electrode based on Schiff base ligand 2-benzoylpyridine semicarbazone as an ionophore
- Effectiveness assessment of TiO2-Al2O3 nano-mixture as a filler material for improvement of packaging performance of PLA nanocomposite films
- Boron nitride nanoplatelets as two-dimensional thermal fillers in epoxy composites: new scenarios at very low filler loadings
- Engineering and processing
- Study on bubble morphology at interface of laser direct joint between carbon fiber reinforced thermoplastic (CFRTP) and titanium alloy
- Robust parameter search for IC tray injection molding using regrind resin
