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A novel robot arm selection methodology based on axiomatic design principles

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Abstract

Many companies intend to utilize the robotic systems to improve the performance of their manufacturing systems. Since the robotic systems are complex, it is required to determine the most suitable robot arm at the beginning of the complete design process. However, due to the increase in the number of robot arm alternatives and existence of the multiple and conflicting criteria, it has become hard to the decision makers to select the appropriate robot arm for a production system. Although the traditional multiple criteria of decision-making techniques were heavily employed in the past for this problem, they were based on subjective judgments for both the alternatives and the criteria. Therefore, a methodology based on Axiomatic Design principles is proposed to help the decision maker decide the most appropriate robot arm on a scientific, systematic, and objective basis. Moreover, the proposed methodology is extended into a decision support system (DSS) through MATLAB software, to evaluate more alternatives rapidly. Both the proposed methodology and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were applied to solve a small problem, so that the techniques will be compared and the utility of the proposed approach will be revealed. Besides, the proposed DSS was applied to a real robot arm selection problem of a food manufacturing system to show its performance in evaluating several alternatives and choosing the most suitable one in an objective and quick manner.

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References

  1. Hong EP, Park GJ (2011) Collaborative design process of large-scale engineering systems using the axiomatic design approach. J Mech Eng Sci 225:2174–2188

    Article  Google Scholar 

  2. Suh NP (2001) Axiomatic design: advances and applications. Oxford University Press, New York

    Google Scholar 

  3. Khouja M, Offodile F (1994) The industrial robots selection problem: a literature review and future directions. IIE Trans 26(4):50–61

    Article  Google Scholar 

  4. Imany MM, Schlesinger RJ (1989) Decision model for robot selection: a comparison of ordinary least squares and linear goal programming methods. Decis Sci 20(1):40–53

    Article  Google Scholar 

  5. Khouja M, Rabinowitz G, Mehrez A (1995) Optimal robot operation and selection using quality and output trade-off. Int J Adv Manuf Technol 10(5):342–355

    Article  Google Scholar 

  6. Cook JS, Han BT (1994) Optimum robot selection and work station assignment for a CIM system. IEEE Trans Robot Autom 10(2):210–219

    Article  Google Scholar 

  7. Karsak EE (1998) A two phase robot selection procedure. Prod Plan Control 9(7):675–684

    Article  Google Scholar 

  8. Braglia M, Petroni A (1999) Evaluating and selecting investments in industrial robots. Int J Prod Res 37(18):4157–4178

    Article  MATH  Google Scholar 

  9. Khouja M, Booth DE (1991) A decision model for robot selection problem using robust regression. Decis Sci 22(3):656–662

    Article  Google Scholar 

  10. Khouja M, Booth DE, Suh M, Mahaney JK (2000) Statistical procedures for task assignment and robot selection in assembly cells. Int J Comput Integr Manuf 13(2):95–106

    Article  Google Scholar 

  11. Karsak EE (2008) Robot selection using an integrated approach based on quality function deployment and fuzzy regression. Int J Prod Res 46(3):723–738

    Article  MATH  Google Scholar 

  12. Agrawal VP, Kohli VP, Gupta S (1991) Computer aided robot selection: ‘the multi attribute decision making’ approach. Int J Prod Res 29(8):1629–1644

    Article  Google Scholar 

  13. Chang GA, Sims JP (2005) A case-based reasoning approach to robot selection. ASME International Mechanical Engineering Congress and Exposition. November 5–11, Orlando

  14. Goh CH (1997) Analytic hierarchy process for robot selection. J Manuf Syst 16(5):381–386

    Article  Google Scholar 

  15. Parkan C, Wu ML (1999) Decision-making and performance measurement models with applications to robot selection. Comput Ind Eng 36:503–523

    Article  Google Scholar 

  16. Chu TC, Lin YC (2003) A fuzzy TOPSIS method for robot selection. Int J Adv Manuf Technol 21:284–290

    Article  Google Scholar 

  17. Bhangale PP, Agrawal VP, Saha SK (2004) Attribute based specification, comparison and selection of a robot. Mech Mach Theory 39:1345–1366

    Article  MATH  Google Scholar 

  18. Bhattacharya A, Sarkar B, Mukherjee SK (2005) Integrating AHP with QFD for robot selection under requirement perspective. Int J Prod Res 43(17):3671–3685

    Article  Google Scholar 

  19. Kapoor V, Tak SS (2005) Fuzzy application to the analytic hierarchy process for robot selection. Fuzzy Optim Decis Making 4:209–234

    Article  MATH  MathSciNet  Google Scholar 

  20. Venkata Rao R, Padmanabhan KK (2006) Selection, identification and comparison of industrial robots using digraph and matrix methods. Robot Comput Integr Manuf 22(4):373–383

    Article  Google Scholar 

  21. Kahraman C, Cevik S, Ates NY, Gulbay M (2007) Fuzzy multi-criteria evaluation of industrial robotic systems. Comput Ind Eng 52:414–433

    Article  Google Scholar 

  22. Choudhury BB, Biswall BB, Mahapatra RN (2009) Attribute-based ranking and selection of robots for task assignment. International Conference on Advanced Computer Control:459–463, Singapore

  23. Chatterjee P, Athawale VM, Chakraborty S (2010) Selection of industrial robots using compromise ranking and outranking methods. Robot Comput Integr Manuf 26:483–489

    Article  Google Scholar 

  24. Kumar R, Garg RK (2010) Optimal selection of robots by using distance based approach method. Robot Comput Integr Manuf 26:500–506

    Article  Google Scholar 

  25. Devi K (2011) Extension of VIKOR method in intuitionistic fuzzy environment for robot selection. Expert Syst Appl 38:14163–14168

    Google Scholar 

  26. Kentli A, Kar AK (2011) A satisfaction function and distance measure based multi-criteria robot selection procedure. Int J Prod Res 49:1–12

    Article  Google Scholar 

  27. Koulouriotis DE, Ketipi MK (2011) A fuzzy digraph method for robot evaluation and selection. Expert Syst Appl 38:11901–11910

    Article  Google Scholar 

  28. Athawale VM, Chakraborty S (2011) A comparative study on the ranking performance of some multi-criteria decision-making methods for industrial robot selection. Int J Ind Eng Comput 2:831–850

    Google Scholar 

  29. Babic B (1999) Axiomatic design of flexible manufacturing system. Int J Prod Res 37(5):1159–1173

    Article  MATH  Google Scholar 

  30. Kulak O, Kahraman C (2005) Multi-attribute comparison of advanced manufacturing systems using fuzzy vs. crisp axiomatic design approach. Int J Prod Econ 95:415–424

    Article  Google Scholar 

  31. Kulak O, Durmusoglu MB, Kahraman C (2005) Fuzzy multi-attribute equipment selection based on information axiom. J Mater Process Technol 169:337–345

    Article  Google Scholar 

  32. Coelcho AMG, Mourao AJF (2007) Axiomatic design as support for decision-making in a design for manufacturing context: a case study. Int J Prod Econ 109:81–89

    Article  Google Scholar 

  33. Cheng X, Huang Z (2010) A method for applying information axiom to the hybrid multi-attribute alternative evaluation. International Conference on Mechanic Automation and Control Engineering (MACE). June 26–28. TBD Wuhan, China

  34. Maldonado A, Garcia JL, Alvarado A, Balderrama CO (2012) A hierarchical fuzzy axiomatic design methodology for ergonomic compatibility evaluation of advanced manufacturing technology. Int J Adv Manuf Technol. doi:10.1007/s00170-012-4316-8

    Google Scholar 

  35. Weng FT, Jenq SM (2012) Application integrating axiomatic design and agile manufacturing unit in product evaluation. Int J Adv Manuf Technol 63:181–189

    Article  Google Scholar 

  36. Ertay T, Satoglu SI (2012) System parameter selection with information axiom for the new product introduction to the hybrid manufacturing systems under dual-resource constraint. Int J Prod Res 50(7):1825–1839

    Article  Google Scholar 

  37. Helander MG, Lin L (2002) Axiomatic design in ergonomics and an extension of the information axiom. J Eng Des 13(4):321–339

    Article  Google Scholar 

  38. Kulak O (2005) A decision support system for fuzzy multi-attribute selection of material handling equipments. Expert Syst Appl 29:310–319

    Article  Google Scholar 

  39. Kulak O, Kahraman C (2005) Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process. Inf Sci 170:191–210

    Article  MATH  Google Scholar 

  40. Kulak O, Satoglu SI, Durmusoglu MB (2004) Multi-attribute material handling equipment selection using information axiom. The Third International Conference on Axiomatic Design, Seoul, Korea, June 21–24, 2004

  41. Cebi S, Kahraman C (2010) Extension of axiomatic design principles under fuzzy environment. Expert Syst Appl 37(3):2682–2689

    Article  Google Scholar 

  42. Akay D, Kulak O, Henson B (2011) Conceptual design evaluation using interval type-2 fuzzy information axiom. Comput Ind 62:138–146

    Article  Google Scholar 

  43. Kahraman C, Cebi S (2009) A new multi-attribute decision making method: hierarchical fuzzy axiomatic design. Expert Syst Appl 36:4848–4861

    Article  Google Scholar 

  44. Cebi S, Kahraman C (2010) Developing a group decision support system based on fuzzy information axiom. Knowl-Based Syst 23:3–16

    Article  Google Scholar 

  45. Ross L, Fardo S, Masterson J, Towers R (2011) Robotics: theory and industrial applications, 2nd edn. Goodheart-Willcox ISBN: 978-1-60525-321-3

  46. Shiakolas PS, Conrad KL, Yih TC (2002) On the accuracy, repeatability and degree of influence of kinematics parameters for industrial robots. Int J Model Simul 22(3):1–10

    Google Scholar 

  47. Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and applications. Springer, Berlin

    Book  MATH  Google Scholar 

Download references

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Correspondence to Sule Itir Satoglu.

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Bahadir, M.C., Satoglu, S.I. A novel robot arm selection methodology based on axiomatic design principles. Int J Adv Manuf Technol 71, 2043–2057 (2014). https://doi.org/10.1007/s00170-014-5620-2

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  • DOI: https://doi.org/10.1007/s00170-014-5620-2

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