Advertisement

Disassembly Sequence Planning Methodology for EOL Products Through a Computational Approach

  • Anil Kumar Gulivindala
  • Vykunta Rao Matta
  • M. V. A. Raju Bahubalendruni
Conference paper
  • 48 Downloads
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Minimization of adverse environmental effect by generated e-waste day to day became challenging in different sectors of both developed and underdeveloping countries. Promoting 3’Rs policy such as reuse, resale, and remanufacture from the EOL products found as an only possible solution for the encountered challenge. An efficient disassembly sequence plan is needed to perform necessary operations and sorting out the relevant parts from EOL products. In order to achieve this, different existing methods have been studied and observed that subassembly identification is most essential in disassembly sequence planning to formulate an efficient solution. But the involvement of more computational effort in SI-based DSP got less research interest. Part concatenation method in ASG proved for generation in ample amount of subassemblies besides ASP. In this paper, a novel attempt has been made by implementing PCM to perform DSP. The results indicated that the method has tremendous workability not only in DSP but also extendable to PDSP, SDSP, and CDSP. The working of PCM on various classifications in DSP is explained with a case study and described well with suitable illustrations.

Keywords

Disassembly Disassembly sequence planning Part concatenation method 

References

  1. 1.
    Kiddee P, Naidu R, Wong MH (2013) Electronic waste management approaches: an overview. Waste Manage 33(5):1237–1250CrossRefGoogle Scholar
  2. 2.
    Harivardhini S, Krishna KM, Chakrabarti A (2017) An integrated framework for supporting decision making during early design stages on end-of-life disassembly. J Clean Prod 168:558–574CrossRefGoogle Scholar
  3. 3.
    Smith SS, Chen WH (2011) Rule-based recursive selective disassembly sequence planning for green design. Adv Eng Inform 25(1):77–87CrossRefGoogle Scholar
  4. 4.
    Ilgin MA, Gupta SM (2010) Environmentally conscious manufacturing and product recovery (ECMPRO): a review of the state of the art. J Environ Manage 91(3):563–591CrossRefGoogle Scholar
  5. 5.
    Santochi M, Dini G, Failli F (2002) Disassembly for recycling, maintenance and remanufacturing: state of the art and perspectives. In: AMST’02 advanced manufacturing systems and technology. Springer, Vienna, pp 73–89Google Scholar
  6. 6.
    Harivardhini S, Chakrabarti A (2016) A new model for estimating end-of-life disassembly effort during the early stages of product design. Clean Technol Environ Policy 18(5):1585–1598CrossRefGoogle Scholar
  7. 7.
    Dini G, Santochi M (1992) Automated sequencing and subassembly detection in assembly planning. CIRP Ann 41(1):1–4CrossRefGoogle Scholar
  8. 8.
    Smith S, Smith G, Chen WH (2012) Disassembly sequence structure graphs: an optimal approach for multiple-target selective disassembly sequence planning. Adv Eng Inform 26(2):306–316CrossRefGoogle Scholar
  9. 9.
    Sinanoğlu C, Rıza Börklü H (2005) An assembly sequence-planning system for mechanical parts using a neural network. Assembly Autom 25(1):38–52Google Scholar
  10. 10.
    Bahubalendruni MR, Kumar GA (2018) Practically feasible optimal assembly sequence planning with tool accessibility. In: IOP conference series: materials science and engineering, vol 390, no 1. IOP Publishing, Kancheepuram, pp 12–26Google Scholar
  11. 11.
    Bahubalendruni MR, Biswal BB, Kumar M, Deepak BBVL (2016) A note on mechanical feasibility predicate for robotic assembly sequence generation. In: CAD/CAM, robotics and factories of the future. Springer, New Delhi, pp 397–404Google Scholar
  12. 12.
    O’shea B, Kaebernick H, Grewal SS (2000) Using a cluster graph representation of products for application in the disassembly planning process. Concurr Eng 8(3):158–170Google Scholar
  13. 13.
    De Mello LH, Sanderson AC (1991) A correct and complete algorithm for the generation of mechanical assembly sequences. IEEE Trans Robot Autom 7(2):228–240CrossRefGoogle Scholar
  14. 14.
    Baldwin DF, Abell TE, Lui MC, De Fazio TL, Whitney DE (1991) An integrated computer aid for generating and evaluating assembly sequences for mechanical products. IEEE Trans Robot Autom 7(1):78–94CrossRefGoogle Scholar
  15. 15.
    Abdullah MA, Ab Rashid MFF, Ghazalli Z (2018) Optimization of assembly sequence planning using soft computing approaches: a review. Arch Comput Methods Eng 0(0):1–14Google Scholar
  16. 16.
    Kara S, Pornprasitpol P, Kaebernick H (2005) A selective disassembly methodology for end-of-life products. Assembly Autom 25(2):124–134CrossRefGoogle Scholar
  17. 17.
    Bahubalendruni MR, Gulivindala A, Kumar M, Biswal BB, Annepu LN (2019) A hybrid conjugated method for assembly sequence generation and explode view generation. Assembly Autom 39(1):211–225Google Scholar
  18. 18.
    Wang X, Qin Y, Chen M, Wang CT (2005) End-of-life vehicle recycling based on disassembly. J Central South Univ Technol 12(2):153–156CrossRefGoogle Scholar
  19. 19.
    Bahubalendruni MR, Biswal BB (2017) A novel concatenation method for generating optimal robotic assembly sequences. Proc Inst Mech Eng Part C J Mech Eng Sci 231(10):1966–1977CrossRefGoogle Scholar
  20. 20.
    Bahubalendruni MR, Biswal BB (2015) An intelligent method to test feasibility predicate for robotic assembly sequence generation. In: Intelligent computing, communication, and devices. Springer, New Delhi, pp 277–283Google Scholar
  21. 21.
    Bahubalendruni MR, Biswal BB (2016) Liaison concatenation—a method to obtain feasible assembly sequences from the 3D-CAD product. Sadhana 41(1):67–74MathSciNetCrossRefGoogle Scholar
  22. 22.
    Bahubalendruni MR (2018) An efficient method for exploded view generation through assembly coherence data and precedence relations. World J Eng 15(2):248–253CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Anil Kumar Gulivindala
    • 1
  • Vykunta Rao Matta
    • 1
  • M. V. A. Raju Bahubalendruni
    • 2
  1. 1.NIT PuducherryKaraikalIndia
  2. 2.GMRITRajam, SrikakulamIndia

Personalised recommendations