Advertisement

Building a Graphical User Interface for Concrete Production Processes: A Combined Application of Statistical Process Control and Design of Experiment

  • Barış Şimşek
  • Fatma Pakdil
  • Yusuf Tansel İç
  • Ali Bilge Güvenç
Research Article - Civil Engineering
  • 29 Downloads

Abstract

Quality improvement and control in the manufacturing industry is a necessity for responding timely to increase customer needs and sustainability expectations. In order to decrease the variance in design and production functions, graphical user interface was built in this study implementing a combined methodology based on multi-response design of experiment and statistical process control. Graphical user interface based on MATLAB\(^{{\textregistered }}\) toolbox allows analyzing the sufficiency of measurement system, calculating the capability of concrete production process, optimizing the manufacturing process via TOPSIS-based Taguchi design methodology and comparing the improvement rate of the process capability indices based on current and optimum conditions. After the Gauge R&R analysis, the current system process capability was considered for the C30/37 class (C30) normal weight concrete through process capability indices. In optimal system, process capability ratios, which are the degrees of compliance with the specifications of C30, were determined on the basis of TOPSIS-based Taguchi optimization. Eventually, the actual capability improvement provided through the proposed methodology was considered quite significant.

Keywords

Graphical user interface (GUI) TOPSIS-based Taguchi optimization Process capability ratios Process monitoring and statistical process control Product design 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Fauzan, K.T.; Hosino, M.; Morita, A.: The influence of mixing techniques on the properties of concrete by using air entraining agent and high range water reducer agent. J. Itenas 7, 1–10 (2003)Google Scholar
  2. 2.
    Türkmen, İ.m; Gül, R.; Çel k, C.; Dem rboğa, R.: Determination by the Taguchi method of optimum conditions for mechanical properties of high strength concrete with admixtures of silica fume and blast furnace slag. Civ. Eng. Environ. Syst. 20, 105–118 (2003)Google Scholar
  3. 3.
    Bayramov, F.; Taşdemir, C.; Taşdemir, M.: Optimisation of steel fibre reinforced concretes by means of statistical response surface method. Cem. Concr. Compos. 26, 665–675 (2004)CrossRefGoogle Scholar
  4. 4.
    Hınıslıoğlu, S.; Bayrak, O.Ü.: Optimization of early flexural strength of pavement concrete with silica fume and fly ash by the Taguchi method. Civ. Eng. Environ. Syst. 21, 79–90 (2004)CrossRefGoogle Scholar
  5. 5.
    Muthukumar, M.; Mohan, D.: Optimization of mechanical properties of polymer concrete and mix design recommendation based on design of experiments. J. Appl. Polym. Sci. 94, 1107–1116 (2004)CrossRefGoogle Scholar
  6. 6.
    Tan, O.; Zaimoglu, A.S.; Hinislioglu, S.; Altun, S.: Taguchi approach for optimization of the bleeding on cement-based grouts. Tunn. Undergr. Space Technol. 20, 167–173 (2005)CrossRefGoogle Scholar
  7. 7.
    Nambiar, E.K.; Ramamurthy, K.: Models relating mixture composition to the density and strength of foam concrete using response surface methodology. Cem. Concr. Compos. 28, 752–760 (2006)CrossRefGoogle Scholar
  8. 8.
    Ozbay, E.; Oztas, A.; Baykasoglu, A.; Ozbebek, H.: Investigating mix proportions of high strength self compacting concrete by using Taguchi method. Constr. Build. Mater. 23, 694–702 (2009)CrossRefGoogle Scholar
  9. 9.
    Olivia, M.; Nikraz, H.: Properties of fly ash geopolymer concrete designed by Taguchi method. Mater. Des. 36, 191–198 (2012)CrossRefGoogle Scholar
  10. 10.
    Şirvancı, M.: Design of Experiments for Quality: Taguchi Approach, 2nd edn. Literatür Publishing, İstanbul (2008)Google Scholar
  11. 11.
    Montgomery, D.C.: Statistical Quality Control. Wiley, Hoboken (2013)zbMATHGoogle Scholar
  12. 12.
    Karhan, Ö.; Ceran, Ö.B.; Şara, O.N.; Şimşek, B.: Response surface methodology based desirability function approach to investigate optimal mixture ratio of silver nanoparticles synthesis process. Ind. Eng. Chem. Res. 56, 8180–8189 (2017)CrossRefGoogle Scholar
  13. 13.
    Tripathy, S.; Tripathy, D.K.: Multi-attribute optimization of machining process parameters in powder mixed electro-discharge machining using TOPSIS and grey relational analysis. Int. J. Eng. Sci. Technol. 19, 62–70 (2016)CrossRefGoogle Scholar
  14. 14.
    Şimşek, B.; Uygunoğlu, T.: Multi-response optimization of polymer blended concrete: a TOPSIS based Taguchi application. Constr. Build. Mater. 117, 251–262 (2016)CrossRefGoogle Scholar
  15. 15.
    Şimşek, B.; Ultav, G.; Küçük, V.A.; İç, Y.T.: PID control performance improvement for a liquid Level system using parameter design. Int. J. Appl. Math. Electron. Comput. 4(special issue), 98–103 (2016).  https://doi.org/10.18100/ijamec.267185
  16. 16.
    Ramesh, S.; Viswanathan, R.; Ambika, S.: Measurement and optimization of surface roughness and tool wear via grey relational analysis. Meas. TOPSIS RSA Tech. 78, 63–72 (2016)Google Scholar
  17. 17.
    Balasubramaniyan, S.; Selvaraj, T.: Application of integrated Taguchi and TOPSIS method for optimization of process parameters for dimensional accuracy in turning of EN25 steel. J. Chin. Inst. Eng. 40, 267–274 (2017)CrossRefGoogle Scholar
  18. 18.
    Chang, C.Y.; Huang, R.; Lee, P.C.; Weng, T.L.: Application of a weighted Grey-Taguchi method for optimizing recycled aggregate concrete mixtures. Cem. Concr. Compos. 33, 1038–1049 (2011)CrossRefGoogle Scholar
  19. 19.
    Şimşek, B.; İç, Y.T.; Şimşek, E.H.: A TOPSIS-based Taguchi optimization to determine optimal mixture proportions of the high strength self-compacting concrete. Chemom. Intell. Lab. Syst. 125, 18–32 (2013)CrossRefGoogle Scholar
  20. 20.
    Kuo, Y.; Yang, T.; Huang, G.-W.: The use of a grey-based Taguchi method for optimizing multi-response simulation problems. Eng. Optim. 40, 517–528 (2008)CrossRefGoogle Scholar
  21. 21.
    Eraslan, E.; Tansel İç, Y.: A multi-criteria approach for determination of investment regions: Turkish case. Ind. Manag. Data Syst. 111, 890–909 (2011)CrossRefGoogle Scholar
  22. 22.
    Yan, S.; Lin, H.-C.; Liu, Y.-C.: Optimal schedule adjustments for supplying ready mixed concrete following incidents. Autom. Constr. 20, 1041–1050 (2011)CrossRefGoogle Scholar
  23. 23.
    Das, S.K.; Sahoo, P.: Tribological characteristics of electroless Ni–B coating and optimization of coating parameters using Taguchi based grey relational analysis. Mater. Des. 32, 2228–2238 (2011)CrossRefGoogle Scholar
  24. 24.
    İç, Y.T.: An experimental design approach using TOPSIS method for the selection of computer-integrated manufacturing technologies. Robot. Comput.-Integr. Manuf. 28, 245–256 (2012)CrossRefGoogle Scholar
  25. 25.
    Koyee, R.D.; Eisseler, R.; Schmauder, S.: Application of Taguchi coupled Fuzzy Multi Attribute Decision Making (FMADM) for optimizing surface quality in turning austenitic and duplex stainless steels. Measurement 58, 375–386 (2014)CrossRefGoogle Scholar
  26. 26.
    Su, T.L.; Chen, H.W.; Lu, C.F.: Systematic optimization for the evaluation of the microinjection molding parameters of light guide plate with TOPSIS-based Taguchi method. Adv. Polym. Technol. 29, 54–63 (2010)CrossRefGoogle Scholar
  27. 27.
    Asafa, T.; Bryce, G.; Severi, S.; Said, S.; Witvrouw, A.: Multi-response optimization of ultrathin poly-SiGe films characteristics for Nano-ElectroMechanical Systems (NEMS) using the grey-Taguchi technique. Microelectron. Eng. 111, 229–233 (2013)CrossRefGoogle Scholar
  28. 28.
    Aslan, N.; Shahrivar, A.A.; Abdollahi, H.: Multi-objective optimization of some process parameters of a lab-scale thickener using grey relational analysis. Sep. Purif. Technol. 90, 189–195 (2012)CrossRefGoogle Scholar
  29. 29.
    Chiang, Y.-M.; Hsieh, H.-H.: The use of the Taguchi method with grey relational analysis to optimize the thin-film sputtering process with multiple quality characteristic in color filter manufacturing. Comput. Ind. Eng. 56, 648–661 (2009)CrossRefGoogle Scholar
  30. 30.
    Dabade, U.A.: Multi-objective process optimization to improve surface integrity on turned surface of Al/SiCp metal matrix composites using grey relational analysis. Procedia CIRP 7, 299–304 (2013)CrossRefGoogle Scholar
  31. 31.
    Hong, G.-B.; Su, T.-L.: Statistical analysis of experimental parameters in characterization of ultraviolet-resistant polyester fiber using a TOPSIS-Taguchi method. Iran. Polym. J. 21, 877–885 (2012)CrossRefGoogle Scholar
  32. 32.
    Kibria, G.; Doloi, B.; Bhattacharyya, B.: Experimental investigation and multi-objective optimization of Nd: YAG laser micro-turning process of alumina ceramic using orthogonal array and grey relational analysis. Opt. Laser Technol. 48, 16–27 (2013)CrossRefGoogle Scholar
  33. 33.
    Lan, T.-S.: Taguchi optimization of multi-objective CNC machining using TOPSIS. Inf. Technol. J. 8, 917–922 (2009)CrossRefGoogle Scholar
  34. 34.
    Lan, T.-S.: Fuzzy Taguchi deduction optimization on multi-attribute CNC turning. 9, 34 (2010)Google Scholar
  35. 35.
    Liao, C.-N.; Kao, H.-P.: Supplier selection model using Taguchi loss function, analytical hierarchy process and multi-choice goal programming. Comput. Ind. Eng. 58, 571–577 (2010)CrossRefGoogle Scholar
  36. 36.
    Nagesh, S.; Murthy, H.N.; Krishna, M.; Basavaraj, H.: Parametric study of CO 2 laser drilling of carbon nanopowder/vinylester/glass nanocomposites using design of experiments and grey relational analysis. Opt. Laser Technol. 48, 480–488 (2013)CrossRefGoogle Scholar
  37. 37.
    Nikdel, P.; Hosseinpour, M.; Badamchizadeh, M.A.; Akbari, M.: Improved Takagi–Sugeno fuzzy model-based control of flexible joint robot via Hybrid-Taguchi genetic algorithm. Eng. Appl. Artif. Intell. 33, 12–20 (2014)CrossRefGoogle Scholar
  38. 38.
    Pandey, R.K.; Panda, S.: Optimization of bone drilling parameters using grey-based fuzzy algorithm. Measurement 47, 386–392 (2014)CrossRefGoogle Scholar
  39. 39.
    Panneerselvam, K.; Pradeep, K.; Asokan, P.: Optimization of end milling parameters for glass fiber reinforced plastic (GFRP) using grey relational analysis. Procedia Eng. 38, 3962–3968 (2012)CrossRefGoogle Scholar
  40. 40.
    Priyadarshini, M.; Pal, K.: Grey-Taguchi based optimizationof EDM process for titanium alloy. Mater. Today: Proc. 2, 2472–2481 (2015)CrossRefGoogle Scholar
  41. 41.
    Sankar, B.R.; Umamaheswarrao, P.; Srinivasulu, V.; Chowdari, G.K.: Optimization of milling process on jute polyester composite using Taguchi based grey relational analysis coupled with principle component analysis. Mater. Today: Proc. 2, 2522–2531 (2015)CrossRefGoogle Scholar
  42. 42.
    Sarıkaya, M.; Güllü, A.: Multi-response optimization of minimum quantity lubrication parameters using Taguchi-based grey relational analysis in turning of difficult-to-cut alloy Haynes 25. J. Clean. Prod. 91, 347–357 (2015)CrossRefGoogle Scholar
  43. 43.
    Sharma, A.; Yadava, V.: Modelling and optimization of cut quality during pulsed Nd: YAG laser cutting of thin Al-alloy sheet for curved profile. Opt. Lasers Eng. 51, 77–88 (2013)CrossRefGoogle Scholar
  44. 44.
    Şimşek, B.; İç, Y.T.: Multi-response simulation optimization approach for the performance optimization of an Alarm Monitoring Center. Saf. Sci. 66, 61–74 (2014)CrossRefGoogle Scholar
  45. 45.
    Sivapirakasam, S.; Mathew, J.; Surianarayanan, M.: Multi-attribute decision making for green electrical discharge machining. Expert Syst. Appl. 38, 8370–8374 (2011)CrossRefGoogle Scholar
  46. 46.
    Sood, A.K.; Ohdar, R.; Mahapatra, S.: Improving dimensional accuracy of fused deposition modelling processed part using grey Taguchi method. Mater. Des. 30, 4243–4252 (2009)CrossRefGoogle Scholar
  47. 47.
    Subbaya, K.; Suresha, B.; Rajendra, N.; Varadarajan, Y.: Grey-based Taguchi approach for wear assessment of SiC filled carbon-epoxy composites. Mater. Des. 41, 124–130 (2012)CrossRefGoogle Scholar
  48. 48.
    Tang, C.-W.; Young, H.-T.: Using Grey relational analysis to determine wet chemical etching parameters in through-silicon-via etching application. Mater. Sci. Semicond. Process. 16, 403–409 (2013)CrossRefGoogle Scholar
  49. 49.
    Wang, P.; Meng, P.; Zhai, J.-Y.; Zhu, Z.-Q.: A hybrid method using experiment design and grey relational analysis for multiple criteria decision making problems. Knowl.-Based Syst. 53, 100–107 (2013)CrossRefGoogle Scholar
  50. 50.
    Xu, J.; Sheng, G.-P.; Luo, H.-W.; Fang, F.; Li, W.-W.; Zeng, R.J.; Tong, Z.-H.; Yu, H.-Q.: Evaluating the influence of process parameters on soluble microbial products formation using response surface methodology coupled with grey relational analysis. Water Res. 45, 674–680 (2011)CrossRefGoogle Scholar
  51. 51.
    Yang, T.; Wen, Y.-F.; Wang, F.-F.: Evaluation of robustness of supply chain information-sharing strategies using a hybrid Taguchi and multiple criteria decision-making method. Int. J. Prod. Econ. 134, 458–466 (2011)CrossRefGoogle Scholar
  52. 52.
    Cheng, M.; Yang, R.; Zhang, L.; Shi, Z.; Yang, W.; Wang, D.; Xie, G.; Shi, D.; Zhang, G.: Restoration of graphene from graphene oxide by defect repair. Carbon 50, 2581–2587 (2012)CrossRefGoogle Scholar
  53. 53.
    Yang, Y.-S.; Huang, W.; Huang, W.-Y.: Mechanical and hydrophobic properties of chromium carbide films via a multi-objective optimization approach. Thin Solid Films 519, 4899–4905 (2011)CrossRefGoogle Scholar
  54. 54.
    Yang, Y.S.; Huang, W.: A grey-fuzzy Taguchi approach for optimizing multi-objective properties of zirconium-containing diamond-like carbon coatings. Expert Syst. Appl. 39(1), 743–750 (2012)CrossRefGoogle Scholar
  55. 55.
    Yeh, J.-H.; Tsai, T.-N.: Optimizing the fine-pitch copper wire bonding process with multiple quality characteristics using a grey-fuzzy Taguchi method. Microelectron. Reliab. 54(1), 287–296 (2014)CrossRefGoogle Scholar
  56. 56.
    Diwan, R.; Shah, S.; Eggers, J.: Statistical quality control and quality assurance evaluation of structural and paving concrete. Transp. Res. Rec.: J. Transp. Res. Board 71–85 (2003)Google Scholar
  57. 57.
    Laungrungrong, B.; Mobasher, B.; Montgomery, D.; Borror, C.M.: Hybrid control charts for active control and monitoring of concrete strength. J. Mater. Civ. Eng. 22, 77–87 (2009)CrossRefGoogle Scholar
  58. 58.
    Rashed, M.G.; Rahman, D.M.M.: Multigrade, multivariable CUSUM control charts for control and monitoring of the concrete production. AUST J. Sci. Technol. 3, 29–45 (2011)Google Scholar
  59. 59.
    Al-Refaie, A.; Bata, N.: Evaluating measurement and process capabilities by GR&R with four quality measures. Measurement 43, 842–851 (2010)CrossRefGoogle Scholar
  60. 60.
    Automotive Industry Action Group (AIAG), Chrysler Group LLC, Ford Motor Company, General Motors Corporation.: Measurement systems analysis reference manual, 3rd edn. Detroit-Michigan, USA (2002)Google Scholar
  61. 61.
    Dhawale, M.R.; Raut, D.: Evaluating Measurement Capabilities by Gauge R&R Using ANOVA for Reliability, system, 3 (2013)Google Scholar
  62. 62.
    Chang, Y.: Interval estimation of capability index Cpmk for manufacturing processes with asymmetric tolerances. Comput. Ind. Eng. 56, 312–322 (2009)CrossRefGoogle Scholar
  63. 63.
    Shinde, J.; Katikar, R.: Importance of process capability and process performance indices in machine tool. Int. J. Res. Eng. Appl. Sci. 2, 1211–1217 (2012)Google Scholar
  64. 64.
    Institute, T.S.: Testing Fresh Concrete-Part 2: Slump Test, EN 12350/2, pp. 1–9. TSE, Ankara (2010)Google Scholar
  65. 65.
    Institute, T.S.: Testing Hardened Concrete—Part 3, Compressive Strength of Test Specimens, p. 21. Ankara (2010)Google Scholar
  66. 66.
    Institute, T.S.: Concrete—Specification, performance, production and Conformity, pp. 1–94. TSE, Ankara (2014)Google Scholar
  67. 67.
    Tansel İç, Y.; Yıldırım, S.: MOORA-based Taguchi optimisation for improving product or process quality. Int. J. Prod. Res. 51, 3321–3341 (2013)CrossRefGoogle Scholar
  68. 68.
    Korucu, H.; Şimşek, B.; Yartaşı, A.: A TOPSIS-based Taguchi design to investigate optimum mixture proportions of graphene oxide powder synthesized by hummers method. Arab. J. Sci. Eng. (2018).  https://doi.org/10.1007/s13369-018-3184-4
  69. 69.
    Şimşek, B.; Ultav, G.; Korucu, H.; Yartaşı, A.: Improvement of the graphene oxide dispersion properties with the use of TOPSIS based Taguchi application. Periodica Polytechnica Chem. Eng. 62(3), 323–335 (2018).  https://doi.org/10.3311/PPch.11412 CrossRefGoogle Scholar
  70. 70.
    Padke, S.: Quality Engineering Using Robust Design. Prentice Hall, New Jersey (1989)Google Scholar
  71. 71.
    Şimşek, B.; Pakdil, F.; Dengiz, B.; Testik, M.C.: Driver performance appraisal using GPS terminal measurements: a conceptual framework. Transp. Res. C: Emerg. Technol. 26, 49–60 (2013)CrossRefGoogle Scholar
  72. 72.
    Pearn, W.; Chen, K.: One-sided capability indices C PU and C PL: decision making with sample information. Int. J. Qual. Reliabil. Manag. 19, 221–245 (2002)CrossRefGoogle Scholar
  73. 73.
    Ryan, T.P.: Statistical Methods for Quality Improvement, 2nd edn. Wiley, Hoboken (2002)zbMATHGoogle Scholar
  74. 74.
    The Math Works, Inc.: Matlab App Building Guide R2018a, Create UIs with Guide: chapter: 4–9, Massachusetts, USA (2018)Google Scholar
  75. 75.
    Şimşek, B.; İç, Y.T.; Şimşek, E.H.; Güvenç, A.B.: Development of a graphical user interface for determining the optimal mixture parameters of normal weight concretes: A response surface methodology based quadratic programming approach. Chemom. Intell. Lab. Syst. 136, 1–9 (2014)CrossRefGoogle Scholar
  76. 76.
    Ballabio, D.; Vasighi, M.: A MATLAB toolbox for Self Organizing Maps and supervised neural network learning strategies. Chemom. Intell. Lab. Syst. 118, 24–32 (2012)CrossRefGoogle Scholar
  77. 77.
    Ferrer-Buedo, J.; Martínez-Sober, M.; Alakhdar-Mohmara, Y.; Soria-Olivas, E.; Benítez-Martínez, J.C.; Martínez-Martínez, J.M.: Matlab-based interface for the simultaneous acquisition of force measures and Doppler ultrasound muscular images. Comput. Methods Programs Biomed. 110, 76–81 (2013)CrossRefGoogle Scholar
  78. 78.
    Kano, M.; Nakagawa, Y.: Data-based process monitoring, process control, and quality improvement: recent developments and applications in steel industry. Comput. Chem. Eng. 32, 12–24 (2008)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  • Barış Şimşek
    • 1
  • Fatma Pakdil
    • 2
  • Yusuf Tansel İç
    • 3
  • Ali Bilge Güvenç
    • 4
  1. 1.Department of Chemical Engineering, Faculty of EngineeringÇankırı Karatekin UniversityÇankırıTurkey
  2. 2.Department of Business AdministrationEastern Connecticut State UniversityWillimanticUSA
  3. 3.Department of Industrial Engineering, Faculty of EngineeringBaşkent UniversityBaglica, Etimesgut, AnkaraTurkey
  4. 4.MGEO (Electro-Optical Systems Engineering Department)Aselsan A.Ş.Akyurt, AnkaraTurkey

Personalised recommendations