Optimizing performance of rigid polyurethane foam using FGP models

Original Research

Abstract

This research proposes and implements fuzzy goal programming (FGP) models to optimize the performance of rigid polyurethane foam (RPF) for four quality characteristics. At initial process factor settings, the RPF process was found incapable for density, shrinkage ratio, and expanding ratio. However, it was found highly capable for compressive strength at 10% deformation. As a result, costly nonconforming products were produced and delivered. For this reason, fuzzy goal programming models were proposed and implemented to determine the combination of optimal factors settings followed by confirmation experiments. The results showed that: (1) for density the capability index is enhanced from −1.72 to (0.66, 0.62, and 0.85) at lower, middle and upper optimal factor setting levels, respectively, (2) for compressive stress at 10% deformation, the capability index is improved from 5.32, to (6.98, 5.45, and 6.02), (3) for shrinkage ratio the process capability is highly capable; \({{\widehat{C}}_{pu}}\) becomes (4.56, 4.62, and 5.91), and (4) for expanding ratio the capability index becomes highly capable; \({{\widehat{C}}_{pu}}\), equals to (6.37, 6.30, and 6.88). Such improvements in process capability result in significant savings in quality, maintenance, and production costs. In conclusions, implementing fuzzy goal programming model is found an efficient technique to optimal RPF performance for four quality responses.

Keywords

Rigid polyurethane foam Fuzzy goal programming Process capability Optimization 

References

  1. Al-Refaie A (2013) A proposed weighted additive model to optimize multiple quality responses in the Taguchi method with applications. J Process Mech Eng 229(3):168–178CrossRefGoogle Scholar
  2. Al-Refaie A (2014a) A proposed satisfaction model to optimize process performance with multiple quality responses in the Taguchi method. J Eng Manuf 228(2):291–301CrossRefGoogle Scholar
  3. Al-Refaie A, Li M-H (2011) Optimizing the performance of plastic injection molding using weighted additive model in goal programming. Int J Fuzzy Syst Appl 22(07):676–689Google Scholar
  4. Al-Refaie A, Diabat A, Li MH (2014) Optimizing tablets’ quality with multiple responses using fuzzy goal programming. J Process Mech Eng 228(2):115–126CrossRefGoogle Scholar
  5. Bashiri M, Hosseininezhad S (2009) A Fuzzy Programming for Optimizing Multi Response Surface in Robust Designs. J Uncert Syst 3(3):163–173Google Scholar
  6. Biedermann A, Kudoke C, Merten A, Minogue E, Rotermund U, Siefert H, Ebert HP, Heinemann U, Fricke J (2001) Heat transfer mechanisms in polyurethane rigid foam. High Temp High Pressures 33(6):699–706.CrossRefGoogle Scholar
  7. Briody C, Duignan B, Jerrams S, Ronan S (2012) Prediction of compressive creep behavior in flexible polyurethane foam over long time scales and at elevated temperatures. Polymer Test 31:1019–1025CrossRefGoogle Scholar
  8. Derringer G, Suich R (1980) Simultaneous optimization of several response variables. J Qual Technol 12(4):214–219CrossRefGoogle Scholar
  9. Jachovich D, O’toole BJ, Hawkins MC, Sapochak L (2005) Temperature and mold size effect on physical and mechanical properties of a polyurethane foam. J Cell Plast 41(2):153–168CrossRefGoogle Scholar
  10. Kasparek E, Zencker U, Scheidemann R, VÕlzke H, Mũler K (2011) Numerical and experimental studies of polyurethane foam under impact loading. Comput Mater Sci 50(4):1353–1358CrossRefGoogle Scholar
  11. Lisiecki J, Klysz S, Blazejewicz T, Gmurczyk G, Reymer P (2014) Tomographic examination of auxetic polyurethane foam structure. Phys Status Solidi B Basic Solid State Phys 251(2):314–320.CrossRefGoogle Scholar
  12. Maji A, Schreyer H, Donald S, Zuo Q, Satpathi D (1995) Mechanical- properties of polyurethane-foam impact limiters. J Eng Mech 121(4):528–540CrossRefGoogle Scholar
  13. Marsavina L, Linul E, Voiconi T, Sadowski T (2013) A comparison between dynamic and static fracture toughness of polyurethane foams. Polymer Test 32(4):673–680.CrossRefGoogle Scholar
  14. Mohan RB, O’toole BJ, Malpica J, Hatchett DW, Kodippili G, Kinyanjui JM (2008) Effect of processing temperature on ReCrete polyurethane foam. J Cell Plast 44(4):327–345CrossRefGoogle Scholar
  15. Nasirzadeh R, Saber AR (2014) Study of foam density variation in composite sandwich panels under high velocity impact loading. Int J Impact Eng 63:129–139CrossRefGoogle Scholar
  16. Padmanabhan K (2014) Strength-based design optimization studies on rigid polyurethane foam core-glass and carbon-glass fabric face sheet/epoxy matrix sandwich composites. Mech Adv Mater Struct 21(3):191–196CrossRefGoogle Scholar
  17. Pan X, Saddler JN (2013) Effect of replacing Polyol by Organosolv and kraft lignin on the property and structure of rigid polyurethane foam. Biotechnol Biofuels 6(1):1CrossRefGoogle Scholar
  18. Saha MC, Kabir Md.E, Jelani S (2008) Enhancement on thermal and mechanical properties of polyurethane foam infused with nanoparticles. Mater Sci Eng A 479(1–2):213–222CrossRefGoogle Scholar
  19. Stirna U, Beverte I, Yakushin V, Cabulis U (2011) Mechanical properties of rigid polyurethane foams at room and cryogenic temperatures. J Cell Plast 47(4):337–355CrossRefGoogle Scholar
  20. Tate PCM, Talal S (1999) Compressive properties of rigid polyurethane foams. Polymers Polymer Compos 7(2):117–124Google Scholar
  21. Thirumal M, Khastgir D, Singha N, Manjunath B, Naik Y (2008) Effect of foam density on the properties of water blown rigid polyurethane foam. J Appl Polymer Sci 108(3):1810–1817CrossRefGoogle Scholar
  22. Tinti A, Tarzia A, Passaro A, Angiuli R (2014) Thermo graphic analysis of polyurethane foams integrated with phase change materials designed for dynamic thermal insulation in refrigerated transport. Appl Therm Eng 70(1):201–210CrossRefGoogle Scholar
  23. Tu ZH, Shim VPW, Lim CT (2001) Plastic deformation modes in rigid polyurethane foam under static loading. Int J Solids Struct 38(50–51):9267–9279CrossRefGoogle Scholar
  24. Yacoub F, Macgregor JF (2003) Analysis and optimization of a polyurethane reaction injection molding (RIM) process using multivariate projection method. Chemo Metrics Intell Lab Syst 65(1):17–33CrossRefGoogle Scholar
  25. Yaghoobi MA, Jones DF, Tamiz M (2008) Weighted additive models for solving fuzzy goal programming problems. Asia-Pac J Oper Res 25(5):715–733MathSciNetCrossRefMATHGoogle Scholar
  26. Yang T, Ignizio J (1991) Fuzzy programming with nonlinear membership functions: piecewise linear approximation. Fuzzy Sets Syst 41:39–53MathSciNetCrossRefMATHGoogle Scholar
  27. Yu-Hallada LC, Kuczynski ET, Weierstll M (1998) polyurethane the material of choice for occupant protection and energy management. J Cell Plast 34(3):272–278CrossRefGoogle Scholar
  28. Zhang C, Li J, Zhen H, Fenglei Z, Huang Y (2012) Correlation between the acoustic and porous cell morphology of polyurethane foam: Effect of interconnected porosity. Mater Des 41:319–325CrossRefGoogle Scholar
  29. Zhu P, Cao ZB, Chen Y, Zhang XJ, Qian GR, Chu YL, Zhou M (2014) Glycolysis recycling of rigid waste polyurethane foam from refrigerators. Environ Technol 35(21):2676–2684CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Industrial EngineeringUniversity of JordanAmmanJordan
  2. 2.Department of Industrial Engineering and Systems ManagementFeng Chia UniversityTaichungTaiwan, Republic of China

Personalised recommendations