Skip to main content
SpringerLink
Log in

Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

In this study, statistical and soft computing techniques were developed to investigate effect of process parameters on diameter of extruded filament made of polypropylene in hot extrusion. A multi-factors experiment was designed with process parameters of screw speed, roller speed and die temperature. According to the design matrix, twenty four experiments were conducted. The diameter of the extruded plastic filament was measured in each experiment. Subsequently, statistical analysis was used to identify significant factors on diameter of extruded filament. Predictive models of response surface methodology (RSM) and radial basis function neural network (RBFNN) were applied to predict the diameter of extruded filament. The optimal process parameters to maintain the diameter of the filament closest to the target value were identified using the cuckoo search algorithm (CSA), and particle swarm optimization (PSO). Performance analysis demonstrated the superior predictive ability of both models, in which the prediction errors of 0.0245 and 0.0029 (in terms of mean squared error) were obtained by RSM and RBFNN, respectively. Considering the optimization methods, the optimization approaches of using CSA and PSO were promising, in which average relative error of 1.28% was obtained in confirmation tests.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Agapitova, O. Y., & Zalazinskii, A. G. (2014). Modeling and optimization of hydromechanical extrusion of tough-to-machine metals. Russian Journal of Non-Ferrous Metals, 55(6), 571–576. doi:10.3103/s1067821214060029.

    Article  Google Scholar 

  • Ameer, K., Bae, S. W., Jo, Y., Lee, H. G., Ameer, A., & Kwon, J. H. (2017). Optimization of microwave-assisted extraction of total extract, stevioside and rebaudioside-A from Stevia rebaudiana (Bertoni) leaves, using response surface methodology (RSM) and artificial neural network (ANN) modelling. Food Chemistry, 229, 198–207. doi:10.1016/j.foodchem.2017.01.121.

    Article  Google Scholar 

  • Arnaiz-González, Á., Fernández-Valdivielso, A., Bustillo, A., & López de Lacalle, L. N. (2016). Using artificial neural networks for the prediction of dimensional error on inclined surfaces manufactured by ball-end milling. The International Journal of Advanced Manufacturing Technology, 83(5), 847–859. doi:10.1007/s00170-015-7543-y.

    Article  Google Scholar 

  • Ashhab, M. S., Breitsprecher, T., & Wartzack, S. (2014). Neural network based modeling and optimization of deep drawing-extrusion combined process. Journal of Intelligent Manufacturing, 25(1), 77–84.

    Article  Google Scholar 

  • Bäck, T., & Schwefel, H. P. (1993). An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1), 1–23. doi:10.1162/evco.1993.1.1.1.

    Article  Google Scholar 

  • Bakhtiari, H., Karimi, M., & Rezazadeh, S. (2016). Modeling, analysis and multi-objective optimization of twist extrusion process using predictive models and meta-heuristic approaches, based on finite element results. Journal of Intelligent Manufacturing, 27(2), 463–473. doi:10.1007/s10845-014-0879-6.

    Article  Google Scholar 

  • Bingöl, S., & Kılıçgedik, H. Y. (2016). Application of gene expression programming in hot metal forming for intelligent manufacturing. Neural Computing and Applications,. doi:10.1007/s00521-016-2718-5.

    Article  Google Scholar 

  • Bustillo, A., López de Lacalle, L. N., Fernández-Valdivielso, A., & Santos, P. (2016). Data-mining modeling for the prediction of wear on forming-taps in the threading of steel components. Journal of Computational Design and Engineering, 3(4), 337–348. doi:10.1016/j.jcde.2016.06.002.

    Article  Google Scholar 

  • Chen, G., Chen, L., Zhao, G., Zhang, C., & Cui, W. (2017). Microstructure analysis of an Al–Zn–Mg alloy during porthole die extrusion based on modeling of constitutive equation and dynamic recrystallization. Journal of Alloys and Compounds, 710(Supplement C), 80–91. doi:10.1016/j.jallcom.2017.03.240.

    Article  Google Scholar 

  • Chen, W. J., Su, W. C., Nian, F. L., Lin, J. R., & Chen, D. C. (2013). Application of ANOVA and Taguchi-based mutation particle swarm algorithm for parameters design of multi-hole extrusion process. Research Journal of Applied Sciences, Engineering and Technology, 6(13), 2316–2325.

    Article  Google Scholar 

  • D’Addona, D. M., Ullah, A. M. M. S., & Matarazzo, D. (2017). Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing. Journal of Intelligent Manufacturing, 28(6), 1285–1301. doi:10.1007/s10845-015-1155-0.

    Article  Google Scholar 

  • Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28–39.

    Article  Google Scholar 

  • Farzad, H., & Ebrahimi, R. (2017). Die profile optimization of rectangular cross section extrusion in plane strain condition using upper bound analysis method and simulated annealing algorithm. Journal of Manufacturing Science and Engineering, Transactions of the ASME.,. doi:10.1115/1.4034336.

    Article  Google Scholar 

  • Fereshteh-Saniee, F., Sepahi-Boroujeni, A., & Sepahi-Boroujeni, S. (2016). Optimized tool design for expansion equal channel angular extrusion (Exp-ECAE) process using FE-based neural network and genetic algorithm. International Journal of Advanced Manufacturing Technology, 86(9–12), 3471–3482. doi:10.1007/s00170-016-8487-6.

    Article  Google Scholar 

  • Gattmah, J., Ozturk, F., & Orhan, S. (2017). Effects of process parameters on hot extrusion of hollow tube. Arabian Journal for Science and Engineering, 42(5), 2021–2030. doi:10.1007/s13369-017-2434-1.

    Article  Google Scholar 

  • Groover, M. P. (2010). Fundamentals of modern manufacturing: Materials and processes and systems. New York: Wiley.

    Google Scholar 

  • Hamlaoui, N., Azzouz, S., Chaoui, K., Azari, Z., & Yallese, M. A. (2017). Machining of tough polyethylene pipe material: Surface roughness and cutting temperature optimization. International Journal of Advanced Manufacturing Technology, 92(5–8), 2231–2245. doi:10.1007/s00170-017-0275-4.

    Article  Google Scholar 

  • Harnnarongchai, W., Intawong, N., & Sombatsompop, N. (2011). Effects of roller speed, die temperature, volumetric flow rate, and multiple extrusions on mechanical strength of molten and solidified LDPE under tensile deformation. Journal of Macromolecular Science, Part B, 50(6), 1074–1086. doi:10.1080/00222348.2010.497465.

    Article  Google Scholar 

  • Holland, J. H. (1992). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. Cambridge: MIT press.

    Book  Google Scholar 

  • Hsiang, S. H., Lin, Y. W., & Lai, J. W. (2012). Application of fuzzy-based Taguchi method to the optimization of extrusion of magnesium alloy bicycle carriers. Journal of Intelligent Manufacturing, 23(3), 629–638. doi:10.1007/s10845-010-0405-4.

    Article  Google Scholar 

  • Hussein, A. W., & Kadhim, A. J. (2017). Mathematical analyses and numerical simulations for forward extrusion of circular, square, and rhomboidal sections from round billets through streamlined dies. Journal of Manufacturing Science and Engineering, Transactions of the ASME,. doi:10.1115/1.4035123.

    Article  Google Scholar 

  • Kalpakjian, S., & Schmid, S. R. (2014). Manufacturing engineering and technology (p. 913). Upper Saddle River, NJ: Pearson.

  • Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of IEEE international conference on neural networks, Nov./Dec. 1995 (Vol. 4, pp. 1942–1948). IEEE Publisher. doi:10.1109/ICNN.1995.488968.

  • Koster, L. (2005). Influencing factors and parameters in the extrusion process. KGK Kautschuk, Gummi, Kunststoffe, 58(7–8), 362–365.

    Google Scholar 

  • Kuram, E., & Ozcelik, B. (2016). Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling. Journal of Intelligent Manufacturing, 27(4), 817–830. doi:10.1007/s10845-014-0916-5.

    Article  Google Scholar 

  • Lebaal, N., Schmidt, F., & Puissant, S. (2010). Optimisation of extrusion flat die design and die wall temperature distribution, using Kriging and response surface method. International Journal of Materials and Product Technology, 38(2–3), 307–322. doi:10.1504/IJMPT.2010.032107.

    Article  Google Scholar 

  • Lela, B., Musa, A., & Zovko, O. (2014). Model-based controlling of extrusion process. International Journal of Advanced Manufacturing Technology, 74(9–12), 1267–1273. doi:10.1007/s00170-014-6054-6.

    Article  Google Scholar 

  • Li, C., & Kim, I. Y. (2015). Topology, size and shape optimization of an automotive cross car beam. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 229(10), 1361–1378. doi:10.1177/0954407014561279.

    Article  Google Scholar 

  • Lou, S., Wang, Y., Lu, S., & Su, C. (2016). Extrusion process parameters optimization for the aluminum profile extrusion of an upper beam on the train based on response surface methodology. Manufacturing Technology, 16(3), 551–557.

    Google Scholar 

  • Lucignano, C., Montanari, R., Tagliaferri, V., & Ucciardello, N. (2010). Artificial neural networks to optimize the extrusion of an aluminium alloy. Journal of Intelligent Manufacturing, 21(4), 569–574. doi:10.1007/s10845-009-0239-0.

    Article  Google Scholar 

  • Maiyar, L. M., & Thakkar, J. J. (2017). A combined tactical and operational deterministic food grain transportation model: Particle swarm based optimization approach. Computers & Industrial Engineering, 110, 30–42. doi:10.1016/j.cie.2017.05.023.

    Article  Google Scholar 

  • MathWorks. (2015a). Matlab. Natick, MA: The MathWorks Inc.

    Google Scholar 

  • Mogale, D. G., Dolgui, A., Kandhway, R., Kumar, S. K., & Tiwari, M. K. (2017). A multi-period inventory transportation model for tactical planning of food grain supply chain. Computers & Industrial Engineering, 110, 379–394. doi:10.1016/j.cie.2017.06.008.

    Article  Google Scholar 

  • Mousavi, S. A. A. A., Shahab, A. R., & Mastoori, M. (2008). Computational study of Ti–6Al–4V flow behaviors during the twist extrusion process. Materials and Design, 29(7), 1316–1329. doi:10.1016/j.matdes.2007.07.009.

    Article  Google Scholar 

  • Nagarajan, V., Mohanty, A. K., & Misra, M. (2016). Reactive compatibilization of poly trimethylene terephthalate (PTT) and polylactic acid (PLA) using terpolymer: Factorial design optimization of mechanical properties. Materials and Design, 110, 581–591. doi:10.1016/j.matdes.2016.08.022.

    Article  Google Scholar 

  • Ong, P., Chin, D. D. V. S., Ho, C. S., & Ng, C. H. (2017). Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches. Neural Computing and Applications. doi:10.1007/s00521-016-2626-8.

  • Ong, P., & Zainuddin, Z. (2013). An efficient cuckoo search algorithm for numerical function optimization. AIP Conference Proceedings, 1522(1), 1378–1384. doi:10.1063/1.4801290.

    Article  Google Scholar 

  • Paralikas, J., Salonitis, K., & Chryssolouris, G. (2010). Optimization of roll forming process parameters—A semi-empirical approach. The International Journal of Advanced Manufacturing Technology, 47(9), 1041–1052. doi:10.1007/s00170-009-2252-z.

    Article  Google Scholar 

  • Paralikas, J., Salonitis, K., & Chryssolouris, G. (2013). Robust optimization of the energy efficiency of the cold roll forming process. The International Journal of Advanced Manufacturing Technology, 69(1), 461–481. doi:10.1007/s00170-013-5011-0.

    Article  Google Scholar 

  • Raj, K. H., & Setia, R. (2017). Quantum seeded evolutionary computational technique for constrained optimization in engineering design and manufacturing. Structural and Multidisciplinary Optimization, 55(3), 751–766. doi:10.1007/s00158-016-1529-8.

    Article  Google Scholar 

  • Rao, S. S., & Rao, S. (2009). Engineering optimization: Theory and practice. New York: Wiley.

    Book  Google Scholar 

  • Rodríguez-Picón, L. A. (2017). An uncertainty approach for optimization of production parameters—A case study in an extrusion molding process. International Journal of Advanced Manufacturing Technology, 90(1–4), 167–176. doi:10.1007/s00170-016-9358-x.

    Article  Google Scholar 

  • Sarkheyli, A., Zain, A. M., & Sharif, S. (2015). A multi-performance prediction model based on ANFIS and new modified-GA for machining processes. Journal of Intelligent Manufacturing, 26(4), 703–716. doi:10.1007/s10845-013-0828-9.

    Article  Google Scholar 

  • Sharififar, M., & Akbari Mousavi, S. A. A. (2015). Simulation and optimization of hot extrusion process to produce rectangular waveguides. International Journal of Advanced Manufacturing Technology, 79(9–12), 1961–1973. doi:10.1007/s00170-015-6950-4.

    Article  Google Scholar 

  • Sharma, R. S., Upadhyay, V., & Raj, K. H. (2009). Neuro-fuzzy modeling of hot extrusion process. Indian Journal of Engineering and Materials Sciences, 16(2), 86–92.

    Google Scholar 

  • Shen, C., Wang, L., & Li, Q. (2007). Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. Journal of Materials Processing Technology, 183(2), 412–418. doi:10.1016/j.jmatprotec.2006.10.036.

    Article  Google Scholar 

  • Tian, H., Zhao, D., Wang, M., & Jin, Y. (2017). Effect of die lip geometry on polymer extrudate deformation in complex small profile extrusion. Journal of Manufacturing Science and Engineering, Transactions of the ASME,. doi:10.1115/1.4035419.

    Article  Google Scholar 

  • Vadori, R., Misra, M., & Mohanty, A. K. (2017). Statistical optimization of compatibilized blends of poly(lactic acid) and acrylonitrile butadiene styrene. Journal of Applied Polymer Science,. doi:10.1002/app.44516.

    Article  Google Scholar 

  • Venkatesh, C., & Venkatesan, R. (2015). Optimization of process parameters of hot extrusion of SiC/Al 6061 composite using Taguchi’s technique and upper bound technique. Materials and Manufacturing Processes, 30(1), 85–92. doi:10.1080/10426914.2014.962658.

    Article  Google Scholar 

  • Vera-Sorroche, J., Kelly, A. L., Brown, E. C., Gough, T., Abeykoon, C., Coates, P. D., et al. (2014). The effect of melt viscosity on thermal efficiency for single screw extrusion of HDPE. Chemical Engineering Research and Design, 92(11), 2404–2412. doi:10.1016/j.cherd.2013.12.025.

    Article  Google Scholar 

  • Vimal, K. E. K., Vinodh, S., & Raja, A. (2017). Optimization of process parameters of SMAW process using NN-FGRA from the sustainability view point. Journal of Intelligent Manufacturing, 28(6), 1459–1480. doi:10.1007/s10845-015-1061-5.

  • Wanrosli, W. D., Zainuddin, Z., Ong, P., & Rohaizu, R. (2013). Optimization of cellulose phosphate synthesis from oil palm lignocellulosics using wavelet neural networks. Industrial Crops and Products, 50(Supplement C), 611–617. doi:10.1016/j.indcrop.2013.08.048.

    Article  Google Scholar 

  • Xu, E., Pan, X., Wu, Z., Long, J., Li, J., Xu, X., et al. (2016). Response surface methodology for evaluation and optimization of process parameter and antioxidant capacity of rice flour modified by enzymatic extrusion. Food Chemistry, 212, 146–154. doi:10.1016/j.foodchem.2016.05.171.

    Article  Google Scholar 

  • Yang, X. S., & Deb, S. (2009). Cuckoo search via Levy flights. In World congress on nature and biologically inspired computing (NaBIC 2009) 9–11 Dec. 2009 (pp. 210–214). doi:10.1109/NABIC.2009.5393690.

  • Yu, J., Wang, Q., Zhang, Z., & Li, X. (2017). Multi-objective optimizations of multidirectional forming mold based on fractional factorial design. International Journal of Advanced Manufacturing Technology, 88(1–4), 1151–1160. doi:10.1007/s00170-016-8844-5.

    Article  Google Scholar 

  • Zainuddin, Z., Daud, W. R. W., Pauline, O., & Shafie, A. (2011). Wavelet neural networks applied to pulping of oil palm fronds. Bioresource Technology, 102(23), 10978–10986.

    Article  Google Scholar 

  • Zainuddin, Z., & Ong, P. (2011). Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network. Expert Systems with Applications, 38(11), 13711–13722. doi:10.1016/j.eswa.2011.04.164.

    Article  Google Scholar 

  • Zhang, C., Yang, S., Zhang, Q., Zhao, G., Lu, P., & Sun, W. (2017). Automatic optimization design of a feeder extrusion die with response surface methodology and mesh deformation technique. International Journal of Advanced Manufacturing Technology,. doi:10.1007/s00170-017-0018-6.

    Article  Google Scholar 

  • Zhang, X. M., Elkoun, S., Ajji, A., & Huneault, M. A. (2004). Oriented structure and anisotropy properties of polymer blown films: HDPE, LLDPE and LDPE. Polymer, 45(1), 217–229. doi:10.1016/j.polymer.2003.10.057.

    Article  Google Scholar 

  • Zhao, G., Chen, H., Zhang, C., & Guan, Y. (2013). Multiobjective optimization design of porthole extrusion die using Pareto-based genetic algorithm. The International Journal of Advanced Manufacturing Technology, 69(5), 1547–1556. doi:10.1007/s00170-013-5124-5.

    Article  Google Scholar 

Download references

Acknowledgements

Financial supports from the Malaysian Government with the cooperation of Universiti Tun Hussein Onn Malaysia (UTHM) in the form of FRGS Grant Vot 1490 and IGSP Grant Vot U671 are gratefully acknowledged.

Author information

Authors and Affiliations

  1. Mechanical Failure Prevention and Reliability Research Center (MPROVE), Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia

    Pauline Ong

  2. Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia

    Choon Sin Ho, Desmond Daniel Vui Sheng Chin, Chee Kiong Sia & Chuan Huat Ng

  3. Advanced Manufacturing and Materials Centre (AMMC), Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia

    Md Saidin Wahab & Abduladim Salem Bala

Authors
  1. Pauline Ong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Choon Sin Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Desmond Daniel Vui Sheng Chin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chee Kiong Sia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chuan Huat Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Md Saidin Wahab
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Abduladim Salem Bala
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pauline Ong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ong, P., Ho, C.S., Chin, D.D.V.S. et al. Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques. J Intell Manuf 30, 1957–1972 (2019). https://doi.org/10.1007/s10845-017-1365-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-017-1365-8

Keywords

Search

Navigation