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RSM ve Grey Wolf Optimizasyonu Kullanılarak Enjeksiyon Kalıplanmış PA66+PA6I/6T Kompozitinin Mekanik Özelliklerinin Deneysel Olarak İncelenmesi

Year 2020, Volume: 7 Issue: 2, 835 - 847, 31.05.2020
https://doi.org/10.31202/ecjse.705212

Abstract

Bu çalışmada, takviyeli poliamid 66+PA6I/6T polimerinin enjeksiyon kalıpçılık proses parametreleri temel alınarak, mekanik özellik ve üretim koşullarının iyileştirilmesi için multi-objective optimizasyon metodu kullanılmıştır. Bu amaçla, polimerin kalite parametreleri olan çarpılma, hacimsel büzülme ve çevrim süresini minimize edip modellemek için bütünleşik RSM ve GWO optimizasyon yaklaşımı önerilmiştir. Çalışmada, tasarım parametreleri olan lif oranı, kalıp sıcaklığı, eriyik sıcaklığı, enjeksiyon basıncı ve enjeksiyon süreci gözönüne alınarak nümerik çıktı sonuçlarının elde edilmesi ve simülasyon işleminde Moldflow Insight yazılımı kullanılmıştır. Optimizasyonu yapılmış tasarım parametreleri gözönüne alınarak çekme test sonuçlarının elde edilmesi ve karşılaştırılması için plastik enjeksiyon makinesinde bir test numunesi hazırlanmıştır. Sayısal analiz için Box-Behnken deneysel tasarım metodu ve kalıplama işlemindeki tasarım parametrelerinin kaliteye olan etkilerini incelemek için ANOVA metodu uygulanmıştır. Elde edilen sayısal sonuçlara göre RSM ve GWO yöntemlerinin her ikisinin de, tavsiye edilen proses değerleriyle elde edilmiş kalite sonuçlarına göre daha iyi sonuçları verdikleri görülmüştür, aynı zamanda bu sonuçlar ANOVA sonuçlarıyla da uyumludur. Yürütülen bu deneysel tasarım için RSM’ nin GWO metodundan daha etkili olduğu gözlenmiştir. Ayrıca yürütülen deneysel çekme test sonuçlarına göre, % 60 lif takviyesinin en iyi çekme test sonuçları verdiğini ve optimizasyonu yapılmış olan bu proses parametreleri ile % 60’lık bu lif katkı oranı için elastik modül değerinde % 39,4 lük bir artış olduğu gözlenmiştir.

References

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  • Referans2 Ihesiulor O.K., Shankar K., Zhang Z., Ray T., Validation of algorithms for delamination detection in composite structures using experimental data, J Compos Mater, 2014, 48(8), 969–983.
  • Referans3 Shen C., Wang L., Li Q., Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method, J Mater Process Technol, 2007, 183(2), 412–418.
  • Referans4 Barghash M.A., Alkaabneh F.A., Shrinkage and warpage detailed analysis and optimization for the injection molding process using multistage experimental design, Qual Eng, 2014, 26(3), 319–34.
  • Referans5 Guo W., Mao HJ., Xu Q., Warpage and structural analysis of automotive trim based on FFD and CAE in plastic ijection molding, Adv Mater Res, 2011, 1282(6), 314– 316.
  • Referans6 Kuram E., Tasci E., Altan A.I., Medar M.M., Yilmaz F., Ozcelik B., Investigating the effects of recycling number and injection parameters on the mechanical properties of glass-fibre reinforced nylon 6 using Taguchi method, Mater Des, 2013, 49(5), 139–150.
  • Referans7 Pei J.W., Multi-objective optimization scheme for quality control in injection molding, Intell. Transp Syst J, 2002, 6(4), 331–342.
  • Referans8 Myers R.H., Montgomery D.C., Anderson-Cook C.M., Response Surface Methodology: Process and Product Optimization Using Designed Experiments, John Wiley & Sons, 2016.
  • Referans9 Zhang X., Yu S., Gong Y., Li Y., Optimization design for turbodrill blades based on response surface method, Adv Mech Eng, 2016, 8(2), 1–12.
  • Referans10 Mirjalili S., Lewis A., Grey wolf optimizer, Adv Eng Software, 2014, 69, 46–61.
  • Referans11 Haj Ali RKH., Nonlinear constitutive models for FRP composites using artificial neural networks, Mech Mater, 2007, 39(12), 1035–42.
  • Referans12 Megat-Yusoff PSM., Abdul Latif MR., Ramli MS., Optimizing injection molding processing parameters for enhanced mechanical performance of oil palm empty fruit bunch high density polyethylene composites, J Appl Sci, 2011, 11(9), 1618–1623.
  • Referans13 Imihezri S., Sapuan S.M., Sulaiman S. et al., Mould flow and component design analysis of polymeric based composite automotive clutch pedals, J of Mater Process Technol, 2006, 171, 358–365.
  • Referans14 Kurtaran H., Erzurumlu T., Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm, Int J Adv Manuf Technol, 2006, 27(5), 468–472.
  • Referans15 Chen W.C., Nguyen M.H., Chiu W.H., Chen T.N.,. Tai P.H., Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GAPSO, Int J Adv Manuf Technol, 2016, 83(9), 1–14.
  • Referans16 Yin F., Mao H., Hua L., A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters, Mater Des, 2011, 32(6), 3457–3464.
  • Referans17 Sadabadi H., Ghasemi M., Effects of some injection molding process parameters on fiber orientation tensor of short glass fiber polystyrene composites (SGF/PS), Journal of Reinforced Plastics and Composites, 2007, 26(17), 1729–1741.
  • Referans18 Nagahanumaiah BR., Effects of injection molding parameters on shrinkage and weight of plastic part produced by DMLS mold, Rapid Prototyp J, 2009, 15(3), 179–86.
  • Referans19 Kang GJ., Process factor optimization for reducing warpage and shrinkage in injection molding using design of experiments, Appl Mech Mater, 2014, 359(62), 541-542.
  • Referans20 Xu G., Yang Z.T., Long G.D., Multi-objective optimization of MIMO plastic injection molding process conditions based on particle swarm optimization, Int J Adv Manuf Technol, 2012, 58(5), 521–531.
  • Referans21 Rong Y., Zhou Q., Huang Y., Chang Y., Zhang G., Shao X., Multi-objective optimization of laser brazing with the crimping joint using ANN and NSGA-II, Int J Adv Manuf Technol, 2016, 85(5), 1239–1247.

Experimental Investigation of Mechanical Properties for Injection Molded PA66+PA6I/6T Composite Using RSM and Grey Wolf Optimization

Year 2020, Volume: 7 Issue: 2, 835 - 847, 31.05.2020
https://doi.org/10.31202/ecjse.705212

Abstract

In this study, a multi-objective optimization method was used to improve the mechanical properties and manufacturing conditions of reinforced polyamide 66+PA6I/6T polymer based on injection molding process parameters. For that purpose, the combined approach of response surface methodology (RSM) and Grey Wolf Optimization (GWO) was proposed to minimize and model the quality parameters such as warpage, volumetric shrinkage, and cycle time of the polymer. In the study, Moldflow Insight software was used to simulate and obtain the numerical objective results based on design parameters including fiber ratio, mold temperature, melt temperature, injection pressure, and injection time. Based on optimized design parameters, a test specimen was produced in an injection molding machine to obtain and compare the tensile test results. The Box-Behnken method was applied for the experimental design of the numerical analysis, and the analysis of variance (ANOVA) method was used to investigate the effect of design parameters on the objective parameters in molding. According to the numerical results, it was found that both RSM and GWO methods gave better results than the quality results obtained by the recommended process parameter results as well as these results were consistent with the ANOVA results. It was determined that the RSM was more effective than the GWO method for this experimental design. Also, it was concluded that according to the experimental tensile test results, the best tensile test result was obtained by 60% fiber reinforcement, and the tensile module value increased by 39,4% for this addition ratio based on the optimized process parameters.

References

  • Referans1 Zhang X., Yu S., Gong Y., Li Y., Optimization design for turbodrill blades based on response surface method, Advanced Mechanical Engineering, 2016, 8(2), 1–12.
  • Referans2 Ihesiulor O.K., Shankar K., Zhang Z., Ray T., Validation of algorithms for delamination detection in composite structures using experimental data, J Compos Mater, 2014, 48(8), 969–983.
  • Referans3 Shen C., Wang L., Li Q., Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method, J Mater Process Technol, 2007, 183(2), 412–418.
  • Referans4 Barghash M.A., Alkaabneh F.A., Shrinkage and warpage detailed analysis and optimization for the injection molding process using multistage experimental design, Qual Eng, 2014, 26(3), 319–34.
  • Referans5 Guo W., Mao HJ., Xu Q., Warpage and structural analysis of automotive trim based on FFD and CAE in plastic ijection molding, Adv Mater Res, 2011, 1282(6), 314– 316.
  • Referans6 Kuram E., Tasci E., Altan A.I., Medar M.M., Yilmaz F., Ozcelik B., Investigating the effects of recycling number and injection parameters on the mechanical properties of glass-fibre reinforced nylon 6 using Taguchi method, Mater Des, 2013, 49(5), 139–150.
  • Referans7 Pei J.W., Multi-objective optimization scheme for quality control in injection molding, Intell. Transp Syst J, 2002, 6(4), 331–342.
  • Referans8 Myers R.H., Montgomery D.C., Anderson-Cook C.M., Response Surface Methodology: Process and Product Optimization Using Designed Experiments, John Wiley & Sons, 2016.
  • Referans9 Zhang X., Yu S., Gong Y., Li Y., Optimization design for turbodrill blades based on response surface method, Adv Mech Eng, 2016, 8(2), 1–12.
  • Referans10 Mirjalili S., Lewis A., Grey wolf optimizer, Adv Eng Software, 2014, 69, 46–61.
  • Referans11 Haj Ali RKH., Nonlinear constitutive models for FRP composites using artificial neural networks, Mech Mater, 2007, 39(12), 1035–42.
  • Referans12 Megat-Yusoff PSM., Abdul Latif MR., Ramli MS., Optimizing injection molding processing parameters for enhanced mechanical performance of oil palm empty fruit bunch high density polyethylene composites, J Appl Sci, 2011, 11(9), 1618–1623.
  • Referans13 Imihezri S., Sapuan S.M., Sulaiman S. et al., Mould flow and component design analysis of polymeric based composite automotive clutch pedals, J of Mater Process Technol, 2006, 171, 358–365.
  • Referans14 Kurtaran H., Erzurumlu T., Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm, Int J Adv Manuf Technol, 2006, 27(5), 468–472.
  • Referans15 Chen W.C., Nguyen M.H., Chiu W.H., Chen T.N.,. Tai P.H., Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GAPSO, Int J Adv Manuf Technol, 2016, 83(9), 1–14.
  • Referans16 Yin F., Mao H., Hua L., A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters, Mater Des, 2011, 32(6), 3457–3464.
  • Referans17 Sadabadi H., Ghasemi M., Effects of some injection molding process parameters on fiber orientation tensor of short glass fiber polystyrene composites (SGF/PS), Journal of Reinforced Plastics and Composites, 2007, 26(17), 1729–1741.
  • Referans18 Nagahanumaiah BR., Effects of injection molding parameters on shrinkage and weight of plastic part produced by DMLS mold, Rapid Prototyp J, 2009, 15(3), 179–86.
  • Referans19 Kang GJ., Process factor optimization for reducing warpage and shrinkage in injection molding using design of experiments, Appl Mech Mater, 2014, 359(62), 541-542.
  • Referans20 Xu G., Yang Z.T., Long G.D., Multi-objective optimization of MIMO plastic injection molding process conditions based on particle swarm optimization, Int J Adv Manuf Technol, 2012, 58(5), 521–531.
  • Referans21 Rong Y., Zhou Q., Huang Y., Chang Y., Zhang G., Shao X., Multi-objective optimization of laser brazing with the crimping joint using ANN and NSGA-II, Int J Adv Manuf Technol, 2016, 85(5), 1239–1247.
There are 21 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Fuat Tan 0000-0002-4194-5591

Publication Date May 31, 2020
Submission Date March 17, 2020
Acceptance Date May 10, 2020
Published in Issue Year 2020 Volume: 7 Issue: 2

Cite

IEEE F. Tan, “Experimental Investigation of Mechanical Properties for Injection Molded PA66+PA6I/6T Composite Using RSM and Grey Wolf Optimization”, El-Cezeri Journal of Science and Engineering, vol. 7, no. 2, pp. 835–847, 2020, doi: 10.31202/ecjse.705212.
Creative Commons License El-Cezeri is licensed to the public under a Creative Commons Attribution 4.0 license.
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