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Havacılık Endüstrisi Proseslerine Uygun Robotların Seçimi İçin Çok Ölçütlü Bir Karar Verme Modelinin Geliştirilmesi

Yıl 2021, 1 - 1, 31.12.2022
https://doi.org/10.2339/politeknik.886117

Öz

Endüstriyel robotlar uygulama alanlarına ve gereksinimlere göre farklı kabiliyetlere ve özelliklere sahiptir. Havacılık endüstrisi gibi oldukça özel proseslerin bulunduğu bir sektörde gereksinimleri karşılayabilecek endüstriyel bir robotun seçimini yapmak oldukça karmaşık ve zorlu bir süreçtir. En büyük zorluk uçak üretim ve montaj proseslerine uygun çok sayıda robotun mevcut olmasıdır. Ek olarak endüstriyel robotlar arasından en uygun robotun belirlenmesi işleminde çok sayıda teknik kriterin değerlendirilmesi gerekmektedir. Bu makalede çok ölçütlü bir karar verme yöntemleri olan MOORA ve TOPSIS yöntemleri ile havacılık endüstrisine en uygun robot seçimine ilişkin bir çalışma sunulmuştur. Yöntemlerin uygulanmasına ilişkin örnek bir havacılık endüstrisi uygulamasına da makale içeriğinde yer verilmiştir.

Kaynakça

  • [1] Zhou F., Wang X. and Goh M., “Fuzzy extended VIKOR based mobile robot selection model for hospital pharmacy”, International Journal of Advanced Robotic Systems, 15(4): 1-11, (2018).
  • [2] Liu H. C., Quan M. Y., Shi H. and Guo C., “An integrated MCDM method for robot selection under interval‐valued Pythagorean uncertain linguistic environment”, International Journal of Intelligent Systems, 34(2): 188-214, (2019).
  • [3] Fu Y., Li M., Luo H. and Huang, G. Q., “Industrial robot selection using stochastic multicriteria acceptability analysis for group decision making”, Robotics and Autonomous Systems, 122(1): 103304, (2019).
  • [4] Ali A. and Rashid T., “Best–worst method for robot selection”, Soft Computing, 25(1): 563-583 (2021).
  • [5] Wang J. J., Miao Z. H., Cui F. B., and Liu H. C., “Robot evaluation and selection with entropy-based combination weighting and cloud TODIM approach”, Entropy, 20(5): 349, (2018).
  • [6] Sen D. K., Datta S. and Mahapatra S. S., “Application of TODIM (Tomada de Decisión Inerativa Multicritero) for industrial robot selection”, Benchmarking: An International Journal, 23(7): 1818-1833, (2016).
  • [7] Ghorabaee M. K., “Developing an MCDM method for robot selection with interval type-2 fuzzy sets”, Robotics and Computer-Integrated Manufacturing, 37(1), 221-232, (2016).
  • [8] İç Y. T., Yurdakul M., Günyar A. ve Önel H., “Endüstriyel Robot Seçimi İçin Bir Karar Destek Sistemi”, Makina Tasarım ve İmalat Dergisi, 15(2): 92-105, (2017).
  • [9] Liang G. S., Wang J. J., “A fuzzy multi-criteria decision-making approach for robot selection”, Robotics and Computer-Integrated Manufacturing, 10(4): 267-274, (1993).
  • [10] Sen D. K., Datta S., Patel S. K., and Mahapatra S. S., “Multi-criteria decision making towards selection of industrial robot: Exploration of PROMETHEE II method”, Benchmarking: An International Journal, 22(3): 465-487, (2015).
  • [11] Kapoor V. and Tak S. S., “Fuzzy application to the analytic hierarchy process for robot selection”, Fuzzy Optimization and Decision Making, 4(3): 209-234, (2005).
  • [12] Kahraman C., Kaya I., Ates N. Y. and Gülbay M., “Fuzzy multi-criteria evaluation of industrial robotic systems using TOPSIS”, Springer, 159-186, (2008).
  • [13] Chu T. C. and Lin Y. C., “A fuzzy TOPSIS method for robot selection”, The International Journal of Advanced Manufacturing Technology, 21(4): 284-290, (2003).
  • [14] Zhou F., Wang X., and Goh M., “Fuzzy extended VIKOR-based mobile robot selection model for hospital pharmacy”, International Journal of Advanced Robotic Systems, 15(4), (2018).
  • [15] Yalcin N. and Uncu N., “Applying EDAS as an applicable MCDM method for industrial robot selection”, Sigma Journal of Engineering and Natural Sciences, 37(3): 779-796, (2019).
  • [16] Goswami S. S., Behera D. K., Afzal A., Kaladgi R., Khan S. A., Rajendran P. and Asif M., “Analysis of a robot selection problem using two newly developed hybrid MCDM models of TOPSIS-ARAS and COPRAS-ARAS”, Symmetry, 13(8): 1331, (2021).
  • [17] Ic Y. T., Yurdakul M. and Dengiz B., “Development of a decision support system for robot selection”, Robotics and Computer-Integrated Manufacturing, 29(4): 142-157, (2013).
  • [18] Ecer F., “Multi-criteria decision making for green supplier selection using interval type-2 fuzzy AHP: a case study of a home appliance manufacturer”, Operational Research, 1-35, (2020).
  • [19] Deli I. A., “TOPSIS method by using generalized trapezoidal hesitant fuzzy numbers and application to a robot selection problem”, Journal of Intelligent and Fuzzy Systems, 38(1): 779-793, (2020).
  • [20] Sahin B., Yip T. L., Tseng P. H., Kabak M., and Soylu A. “An application of a fuzzy TOPSIS multi-criteria decision analysis algorithm for dry bulk carrier selection”, Information, 11(5): 251, (2020).
  • [21] Krishna K., Karthikeyan A. and Elango M., “Selection of a Best Humanoid Robot Using TOPSIS for Rescue Operation”, In Advances in Mechanical and Materials Technology, 943-953, (2022).
  • [22] Chodha V., Dubey R., Kumar R., Singh S. and Kaur S., “Selection of industrial arc welding robot with TOPSIS and Entropy MCDM techniques”, Materials Today: Proceedings, (2021).
  • [23] Kumar V., Kalita K., Chatterjee P., Zavadskas E. K. and Chakraborty S., “A SWARA-CoCoSo-Based Approach for Spray Painting Robot Selection”, Informatica, 1-20, (2021).
  • [24] Atkinson A., Hartmann J., Jones S., Gleeson P., “Robotic Drilling System for 737 Aileron”, SAE Technical Paper, 1-8, (2007).
  • [25] Landau C., “High Accuracy Assembly of Large Aircraft Components Using Coordinated Arm Robots”, SAE Technical Paper, 1-5, (2016).
  • [26] Rathjen S. and Richardson C., “High Path Accuracy, High Process Forece Articulated Robot”, SAE Technical Paper, 1-6, (2013).
  • [27] Cibiel C. and Prat P., “Automation for the Assembly of the Bottom Wing Panels on Stringers for the A320”, SAE Technical Paper, 1-5, (2006).
  • [28] Mehlenhoff T. and Vogl S., “Automated Fastening of Aircraft Cargo Door Structures with a Standard Articulating Robot System”, SAE Technical Paper, 1-6, (2009).
  • [29] Gray T., Orf D. and Adams G., “Mobile Automated Robotic Drilling, Inspection and Fastening”, SAE Technical Paper, 1-7, (2013).
  • [30] Cano R., Ibanez G. O., Castillo M. and Marin, R. “Flexible and Low-Cost Robotic System for Drilling Material Stacks”, SAE Technical Paper, 1-6, (2016).
  • [31] Vandaele C., Friot D., Marry S. and Gueydon E., “New Technology for Fully Automated One Side Assembly”, SAE Technical Paper, 1-10, (2016).
  • [32] Muys L. and Bloem J., “Developments in Assembly Technology at Stork Fokker”, SAE Technical Paper, 1-12, (2005).
  • [33] Schwake K. and Wulfsberg J., “Robot-based system for handling of aircraft shell parts”, Conference on Assembly Technologies and Systems Procedia CIRP 23, 104-109, (2014).
  • [34] Kingston R., “Metrology Assisted Robotic Automation”, SAE Technical Paper, 1-5, (2014).
  • [35] Chakraborty S., “Applications Of The MOORA Method For Decision Making in Manufacturing Environment”, The International Journal of Advanced Manufacturing Technology, 1155-1166, (2011).
  • [36] Brauers M., Ginevicius R. and Podvezko V., “Regional development in Lithuania considering multiple objectives by the MOORA method”, Technol Econ Dev Econ, 613–640, (2010).
  • [37] Brauers M., Zavadskas E., “The MOORA Method And Its Application To Privatization In A Transition Economy”, Control and Cybernetics, 35(2): 1-5, (2006).
  • [38] https://www.broetje-automation.com/en/equipment/automatische-montage/bohren-nieten/#bohren-nieten, “Automated Assembly”, (2020).
  • [39] https://www.youtube.com/watch?v=GevBp3nj878, “Mechanic and Machine: Boeing's Advanced Manufacturing Improves 777 Assembly”, (2020).
  • [40] Ic Y. T., Yurdakul M., “Analysis of the effect of the number of criteria and alternatives on the ranking results in applications of the multi criteria decision making approaches in machining center selection problems”, Journal of the Faculty of Engineering and Architecture of Gazi University, 35(2): 991-1001, (2020).

Development of a Multi-Criteria Decision Making Model For the Selection of Appropriate Robots for the Aerospace Industry

Yıl 2021, 1 - 1, 31.12.2022
https://doi.org/10.2339/politeknik.886117

Öz

Industrial robots have different capabilities and features according to their application areas and requirements. In a sector with highly specialized processes such as aerospace industry, accurate selection of an industrial robot to meet the requirements is a very complex and difficult process. The main challenge is that there are many appropriate robots for the aircraft manufacturing and assembly processes. In addition, many technical criteria should be evaluated for determining the most appropriate robot among the industrial robots. In this study the MOORA and TOPSIS method, which are multi-criteria decision making methods are presented for the selection of appropriate industrial robots in the aerospace industry. A real case study related to the application of the methods is also included in the paper content.

Kaynakça

  • [1] Zhou F., Wang X. and Goh M., “Fuzzy extended VIKOR based mobile robot selection model for hospital pharmacy”, International Journal of Advanced Robotic Systems, 15(4): 1-11, (2018).
  • [2] Liu H. C., Quan M. Y., Shi H. and Guo C., “An integrated MCDM method for robot selection under interval‐valued Pythagorean uncertain linguistic environment”, International Journal of Intelligent Systems, 34(2): 188-214, (2019).
  • [3] Fu Y., Li M., Luo H. and Huang, G. Q., “Industrial robot selection using stochastic multicriteria acceptability analysis for group decision making”, Robotics and Autonomous Systems, 122(1): 103304, (2019).
  • [4] Ali A. and Rashid T., “Best–worst method for robot selection”, Soft Computing, 25(1): 563-583 (2021).
  • [5] Wang J. J., Miao Z. H., Cui F. B., and Liu H. C., “Robot evaluation and selection with entropy-based combination weighting and cloud TODIM approach”, Entropy, 20(5): 349, (2018).
  • [6] Sen D. K., Datta S. and Mahapatra S. S., “Application of TODIM (Tomada de Decisión Inerativa Multicritero) for industrial robot selection”, Benchmarking: An International Journal, 23(7): 1818-1833, (2016).
  • [7] Ghorabaee M. K., “Developing an MCDM method for robot selection with interval type-2 fuzzy sets”, Robotics and Computer-Integrated Manufacturing, 37(1), 221-232, (2016).
  • [8] İç Y. T., Yurdakul M., Günyar A. ve Önel H., “Endüstriyel Robot Seçimi İçin Bir Karar Destek Sistemi”, Makina Tasarım ve İmalat Dergisi, 15(2): 92-105, (2017).
  • [9] Liang G. S., Wang J. J., “A fuzzy multi-criteria decision-making approach for robot selection”, Robotics and Computer-Integrated Manufacturing, 10(4): 267-274, (1993).
  • [10] Sen D. K., Datta S., Patel S. K., and Mahapatra S. S., “Multi-criteria decision making towards selection of industrial robot: Exploration of PROMETHEE II method”, Benchmarking: An International Journal, 22(3): 465-487, (2015).
  • [11] Kapoor V. and Tak S. S., “Fuzzy application to the analytic hierarchy process for robot selection”, Fuzzy Optimization and Decision Making, 4(3): 209-234, (2005).
  • [12] Kahraman C., Kaya I., Ates N. Y. and Gülbay M., “Fuzzy multi-criteria evaluation of industrial robotic systems using TOPSIS”, Springer, 159-186, (2008).
  • [13] Chu T. C. and Lin Y. C., “A fuzzy TOPSIS method for robot selection”, The International Journal of Advanced Manufacturing Technology, 21(4): 284-290, (2003).
  • [14] Zhou F., Wang X., and Goh M., “Fuzzy extended VIKOR-based mobile robot selection model for hospital pharmacy”, International Journal of Advanced Robotic Systems, 15(4), (2018).
  • [15] Yalcin N. and Uncu N., “Applying EDAS as an applicable MCDM method for industrial robot selection”, Sigma Journal of Engineering and Natural Sciences, 37(3): 779-796, (2019).
  • [16] Goswami S. S., Behera D. K., Afzal A., Kaladgi R., Khan S. A., Rajendran P. and Asif M., “Analysis of a robot selection problem using two newly developed hybrid MCDM models of TOPSIS-ARAS and COPRAS-ARAS”, Symmetry, 13(8): 1331, (2021).
  • [17] Ic Y. T., Yurdakul M. and Dengiz B., “Development of a decision support system for robot selection”, Robotics and Computer-Integrated Manufacturing, 29(4): 142-157, (2013).
  • [18] Ecer F., “Multi-criteria decision making for green supplier selection using interval type-2 fuzzy AHP: a case study of a home appliance manufacturer”, Operational Research, 1-35, (2020).
  • [19] Deli I. A., “TOPSIS method by using generalized trapezoidal hesitant fuzzy numbers and application to a robot selection problem”, Journal of Intelligent and Fuzzy Systems, 38(1): 779-793, (2020).
  • [20] Sahin B., Yip T. L., Tseng P. H., Kabak M., and Soylu A. “An application of a fuzzy TOPSIS multi-criteria decision analysis algorithm for dry bulk carrier selection”, Information, 11(5): 251, (2020).
  • [21] Krishna K., Karthikeyan A. and Elango M., “Selection of a Best Humanoid Robot Using TOPSIS for Rescue Operation”, In Advances in Mechanical and Materials Technology, 943-953, (2022).
  • [22] Chodha V., Dubey R., Kumar R., Singh S. and Kaur S., “Selection of industrial arc welding robot with TOPSIS and Entropy MCDM techniques”, Materials Today: Proceedings, (2021).
  • [23] Kumar V., Kalita K., Chatterjee P., Zavadskas E. K. and Chakraborty S., “A SWARA-CoCoSo-Based Approach for Spray Painting Robot Selection”, Informatica, 1-20, (2021).
  • [24] Atkinson A., Hartmann J., Jones S., Gleeson P., “Robotic Drilling System for 737 Aileron”, SAE Technical Paper, 1-8, (2007).
  • [25] Landau C., “High Accuracy Assembly of Large Aircraft Components Using Coordinated Arm Robots”, SAE Technical Paper, 1-5, (2016).
  • [26] Rathjen S. and Richardson C., “High Path Accuracy, High Process Forece Articulated Robot”, SAE Technical Paper, 1-6, (2013).
  • [27] Cibiel C. and Prat P., “Automation for the Assembly of the Bottom Wing Panels on Stringers for the A320”, SAE Technical Paper, 1-5, (2006).
  • [28] Mehlenhoff T. and Vogl S., “Automated Fastening of Aircraft Cargo Door Structures with a Standard Articulating Robot System”, SAE Technical Paper, 1-6, (2009).
  • [29] Gray T., Orf D. and Adams G., “Mobile Automated Robotic Drilling, Inspection and Fastening”, SAE Technical Paper, 1-7, (2013).
  • [30] Cano R., Ibanez G. O., Castillo M. and Marin, R. “Flexible and Low-Cost Robotic System for Drilling Material Stacks”, SAE Technical Paper, 1-6, (2016).
  • [31] Vandaele C., Friot D., Marry S. and Gueydon E., “New Technology for Fully Automated One Side Assembly”, SAE Technical Paper, 1-10, (2016).
  • [32] Muys L. and Bloem J., “Developments in Assembly Technology at Stork Fokker”, SAE Technical Paper, 1-12, (2005).
  • [33] Schwake K. and Wulfsberg J., “Robot-based system for handling of aircraft shell parts”, Conference on Assembly Technologies and Systems Procedia CIRP 23, 104-109, (2014).
  • [34] Kingston R., “Metrology Assisted Robotic Automation”, SAE Technical Paper, 1-5, (2014).
  • [35] Chakraborty S., “Applications Of The MOORA Method For Decision Making in Manufacturing Environment”, The International Journal of Advanced Manufacturing Technology, 1155-1166, (2011).
  • [36] Brauers M., Ginevicius R. and Podvezko V., “Regional development in Lithuania considering multiple objectives by the MOORA method”, Technol Econ Dev Econ, 613–640, (2010).
  • [37] Brauers M., Zavadskas E., “The MOORA Method And Its Application To Privatization In A Transition Economy”, Control and Cybernetics, 35(2): 1-5, (2006).
  • [38] https://www.broetje-automation.com/en/equipment/automatische-montage/bohren-nieten/#bohren-nieten, “Automated Assembly”, (2020).
  • [39] https://www.youtube.com/watch?v=GevBp3nj878, “Mechanic and Machine: Boeing's Advanced Manufacturing Improves 777 Assembly”, (2020).
  • [40] Ic Y. T., Yurdakul M., “Analysis of the effect of the number of criteria and alternatives on the ranking results in applications of the multi criteria decision making approaches in machining center selection problems”, Journal of the Faculty of Engineering and Architecture of Gazi University, 35(2): 991-1001, (2020).

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Osman Emre CELEK> (Sorumlu Yazar)
TÜRK HAVACILIK VE UZAY SANAYİ (TUSAŞ)
0000-0003-1932-6695
Türkiye


Mustafa YURDAKUL>
GAZİ ÜNİVERSİTESİ
0000-0002-1562-5738
Türkiye


Yusuf Tansel İÇ>
BAŞKENT ÜNİVERSİTESİ
0000-0001-9274-7467
Türkiye

Yayımlanma Tarihi 31 Aralık 2022
Başvuru Tarihi 24 Şubat 2021
Yayınlandığı Sayı Yıl 2021, Cilt , Sayı

Kaynak Göster

Bibtex @araştırma makalesi { politeknik886117, journal = {Politeknik Dergisi}, eissn = {2147-9429}, address = {Gazi Üniversitesi Teknoloji Fakültesi 06500 Teknikokullar - ANKARA}, publisher = {Gazi Üniversitesi}, year = {2022}, pages = {1 - 1}, doi = {10.2339/politeknik.886117}, title = {Havacılık Endüstrisi Proseslerine Uygun Robotların Seçimi İçin Çok Ölçütlü Bir Karar Verme Modelinin Geliştirilmesi}, key = {cite}, author = {Celek, Osman Emre and Yurdakul, Mustafa and İç, Yusuf Tansel} }
APA Celek, O. E. , Yurdakul, M. & İç, Y. T. (2022). Havacılık Endüstrisi Proseslerine Uygun Robotların Seçimi İçin Çok Ölçütlü Bir Karar Verme Modelinin Geliştirilmesi . Politeknik Dergisi , , 1-1 . DOI: 10.2339/politeknik.886117
MLA Celek, O. E. , Yurdakul, M. , İç, Y. T. "Havacılık Endüstrisi Proseslerine Uygun Robotların Seçimi İçin Çok Ölçütlü Bir Karar Verme Modelinin Geliştirilmesi" . Politeknik Dergisi (2022 ): 1-1 <https://dergipark.org.tr/tr/pub/politeknik/issue/33364/886117>
Chicago Celek, O. E. , Yurdakul, M. , İç, Y. T. "Havacılık Endüstrisi Proseslerine Uygun Robotların Seçimi İçin Çok Ölçütlü Bir Karar Verme Modelinin Geliştirilmesi". Politeknik Dergisi (2022 ): 1-1
RIS TY - JOUR T1 - Havacılık Endüstrisi Proseslerine Uygun Robotların Seçimi İçin Çok Ölçütlü Bir Karar Verme Modelinin Geliştirilmesi AU - Osman EmreCelek, MustafaYurdakul, Yusuf Tanselİç Y1 - 2022 PY - 2022 N1 - doi: 10.2339/politeknik.886117 DO - 10.2339/politeknik.886117 T2 - Politeknik Dergisi JF - Journal JO - JOR SP - 1 EP - 1 VL - IS - SN - -2147-9429 M3 - doi: 10.2339/politeknik.886117 UR - https://doi.org/10.2339/politeknik.886117 Y2 - 2022 ER -
EndNote %0 Politeknik Dergisi Havacılık Endüstrisi Proseslerine Uygun Robotların Seçimi İçin Çok Ölçütlü Bir Karar Verme Modelinin Geliştirilmesi %A Osman Emre Celek , Mustafa Yurdakul , Yusuf Tansel İç %T Havacılık Endüstrisi Proseslerine Uygun Robotların Seçimi İçin Çok Ölçütlü Bir Karar Verme Modelinin Geliştirilmesi %D 2022 %J Politeknik Dergisi %P -2147-9429 %V %N %R doi: 10.2339/politeknik.886117 %U 10.2339/politeknik.886117
ISNAD Celek, Osman Emre , Yurdakul, Mustafa , İç, Yusuf Tansel . "Havacılık Endüstrisi Proseslerine Uygun Robotların Seçimi İçin Çok Ölçütlü Bir Karar Verme Modelinin Geliştirilmesi". Politeknik Dergisi / (Aralık 2022): 1-1 . https://doi.org/10.2339/politeknik.886117
AMA Celek O. E. , Yurdakul M. , İç Y. T. Havacılık Endüstrisi Proseslerine Uygun Robotların Seçimi İçin Çok Ölçütlü Bir Karar Verme Modelinin Geliştirilmesi. Politeknik Dergisi. 2022; 1-1.
Vancouver Celek O. E. , Yurdakul M. , İç Y. T. Havacılık Endüstrisi Proseslerine Uygun Robotların Seçimi İçin Çok Ölçütlü Bir Karar Verme Modelinin Geliştirilmesi. Politeknik Dergisi. 2022; 1-1.
IEEE O. E. Celek , M. Yurdakul ve Y. T. İç , "Havacılık Endüstrisi Proseslerine Uygun Robotların Seçimi İçin Çok Ölçütlü Bir Karar Verme Modelinin Geliştirilmesi", Politeknik Dergisi, ss. 1-1, Ara. 2022, doi:10.2339/politeknik.886117
 
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