Skip to main content
SpringerLink
Log in

Development of complex products and production strategies using a multi-objective conceptual design approach

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Conceptual design is a fundamental phase for developing optimal product configurations. During conceptual design, the degree of freedom in engineering choices can propose optimal solutions in terms of assembly, manufacturing, cost and material selection. Nevertheless, in current industrial practices, each aspect is analysed independently and a guided decision-making approach based on multi-objective criteria is missing. Multi-objective analysis is a way of combining each production aspect with the aim of choosing the best design option. The goal of this research work is to define a multi-objective design approach for the determination of optimal and feasible design options during the conceptual design phase. The approach is based on the concept of functional basis, module heuristics for defining product modules and the theory of multi-criteria decision-making for mathematical assessment of the best design option. The novelty of this approach lies in making the design process, currently based on company know-how and experience, systematic. A complex product (i.e. tool-holder carousel of a computer numerical control machine tool) is the case study used to assess the economic sustainability of different design options and to validate the proposed design workflow in a real manufacturing context. Different product modules have been re-designed and prototyped for comparing the assemblability, manufacturability and cost of the design solutions.

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.

Institutional subscriptions

Similar content being viewed by others

References

  1. De Fazio TL, Rhee SJ, Whitney DE (1999) Design specific approach to design for assembly (DFA) for complex mechanical assemblies. IEEE Robot Autom 15(5):869–881.

  2. Boothroyd G, Dewhurst P, Knight W (2010) Product design for manufacture and assembly, 3rd edn. CRC Press, Boca Raton

    Google Scholar 

  3. Stone RB, McAdams DA (2004) A product architecture-based conceptual DFA technique. Des Stud 25:301–325

    Article  Google Scholar 

  4. Favi C, Germani M (2012) A method to optimize assemblability of industrial product in early design phase: from product architecture to assembly sequence. Int J Interact Des Manuf 6(3):155–169

    Article  Google Scholar 

  5. Pahl G, Beitz W, Feldhusen J, Grote KH (2007) Engineering design: a systematic approach, 3rd edn. Springer

  6. Ulrich KT, Eppinger SD (2011) Product design and development, 5th edn. USA, McGraw-Hill Education

    Google Scholar 

  7. Nitesh-Prakash W, Sridhar VG, Annamalai K (2014) New product development by DfMA and rapid prototyping. J Eng Appl Sci 9(3):274–279

    Google Scholar 

  8. Selvaraj P, Radhakrishnan P, Adithan M (2009) An integrated approach to design for manufacturing and assembly based on reduction of product development time and cost. Int J Adv Manuf Technol 42:13–29. https://doi.org/10.1007/s00170-008-1580-8).

    Article  Google Scholar 

  9. Otto K, Wood K (2001) Product design: techniques in reverse engineering and new product development. Prentice Hall, New Jersey

    Google Scholar 

  10. Edwards KL (2002) Towards more strategic product DFMA: priorities for concurrent engineering. J Mat Des 23:651–656

    Article  Google Scholar 

  11. Favi C, Germani M, Mandolini M (2016) Design for manufacturing and assembly vs. design to cost: toward a multi-objective approach for decision-making strategies during conceptual design of complex products. Procedia CIRP 50:275–280

    Article  Google Scholar 

  12. Stone RB, Wood KL, Crawford RH (2000) A heuristic method for identifying modules for product architectures. Des Stud 21:5–31

    Article  Google Scholar 

  13. AlGeddawy T, Samy SN, ElMaraghy H (2017) Best design granularity to balance assembly complexity and product modularity. J Eng Des:1–24

  14. Estorilio C, Simião MC (2006) Cost reduction of a diesel engine using the DFMA method. Product: Manag Development 4:95–103

    Google Scholar 

  15. Das SK, Datla V, Samir G (2000) DFQM—an approach for improving the quality of assembled products. Int J Product Res 38(2):457–477

    Article  MATH  Google Scholar 

  16. Todić V, Lukić D, Milošević M, Jovičić G, Vukman J (2012) Manufacturability of product design regarding suitability for manufacturing and assembly (DfMA). J Prod Eng 16(1):47–50

    Google Scholar 

  17. Annamalai K, Naiju CD, Karthik S, Mohan-Prashanth M (2013) Early cost estimate of product during design stage using design for manufacturing and assembly (DFMA) principles. Adv Mater Res 622-623:540–544

    Article  Google Scholar 

  18. Nepal B, Monplaisir L, Singh N, Yaprak A (2008) Product modularization considerıng cost and manufacturability of modules. Int J Of Ind Eng 15(2):132–142

    Google Scholar 

  19. Hoque ASM, Halder PK, Parvez MS, Szecsi T (2013) Integrated manufacturing features and design-for-manufacture guidelines for reducing product cost under CAD/CAM environment. Comput Ind Eng 66:988–1003

    Article  Google Scholar 

  20. Niazi A, Dai JS, Balabani S, Seneviratne L (2006) Product cost estimation: technique classification and methodology review. J Manuf Sci Eng 128:563–575

    Article  Google Scholar 

  21. Fiorentino A (2014) Cost drivers-based method for machining and assembly cost estimations in mould manufacturing. Int J Adv Manuf Technol 70:1437–1444. https://doi.org/10.1007/s00170-013-5419-6).

    Article  Google Scholar 

  22. Ma J, Kremer GEO (2016) A systematic literature review of modular product design (MPD) from the perspective of sustainability. Int J Adv Manuf Technol 86:1509–1539. https://doi.org/10.1007/s00170-015-8290-9

    Article  Google Scholar 

  23. Zaman UK, Siadat A, Rivette M, Baqai AA, Qiao L (2017) Integrated product-process design to suggest appropriate manufacturing technology: a review. Int J Adv Manuf Technol 91:1409–1430. https://doi.org/10.1007/s00170-016-9765-z)

    Article  Google Scholar 

  24. Durga Prasad KG, Subbaiah KW, Rao KN (2014) Multi-objective optimization approach for cost management during product design at the conceptual phase. J Ind Eng Int 10(48)

  25. Marler RT, Arora JS (2004) Survey of multi-objective optimization methods for engineering. Struct Multidisc Optim 26:369–395

    Article  MathSciNet  MATH  Google Scholar 

  26. Chakraborty S (2011) Applications of the MOORA method for decision making in manufacturing environment. Int J Adv Manuf Technol 54:1155–1166. https://doi.org/10.1007/s00170-010-2972-0)

    Article  Google Scholar 

  27. Yurdakul M, Ic YT (2009) Application of correlation test to criteria selection for multi criteria decision making (MCDM) models. Int J Adv Manuf Technol 40:403–412. https://doi.org/10.1007/s00170-007-1324-1).

    Article  Google Scholar 

  28. Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and applications. Springer-Verlag, New York

    Book  MATH  Google Scholar 

  29. Wang YJ, Lee HS (2007) Generalizing TOPSIS for fuzzy multiple-criteria group decision-making. Comput Math Appl 53:1762–1772

    Article  MathSciNet  MATH  Google Scholar 

  30. Mohammadi A, Mohammadi A, Aryaeefar H (2011) Introducing a new method to expand TOPSIS decision making model to fuzzy TOPSIS. J Math Comput Sci 2(1):150–159

    Google Scholar 

  31. Yurdakul M, Ic YT (2009) Analysis of the benefit generated by using fuzzy numbers in a TOPSIS model developed for machine tool selection problems. J Mater Process Technol 209:310–317

    Article  Google Scholar 

  32. Byun HS, Lee KH (2004) A decision support system for the selection of a rapid prototyping process using the modified TOPSIS method. Int J Adv Manuf Technol 26:1338–1347. https://doi.org/10.1007/s00170-004-2099-2).

    Article  Google Scholar 

  33. Yue Z (2011) An extended TOPSIS for determining weights of decision makers with interval numbers. Knowl-Based Syst 24:146–153

    Article  Google Scholar 

  34. Wang TC, Lee HD (2009) Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Syst Appl 36:8980–8985

    Article  Google Scholar 

  35. Ölvander J, Lundén B, Gavel HA (2009) Computerized optimization framework for the morphological matrix applied to aircraft conceptual design. Comput Aided Des 41:187–196

    Article  Google Scholar 

  36. Bryant Arnold CR, Stone RB, McAdams DA (2008) MEMIC: an interactive morphological matrix tool for automated concept generation. Proceedings of the 2008 Industrial Engineering Research Conference

Download references

Author information

Authors and Affiliations

  1. Department of Engineering and Architecture, Università degli Studi di Parma, Parco Area delle Scienze 181/A, 43124, Parma, Italy

    Claudio Favi

  2. Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131, Ancona, Italy

    Michele Germani & Marco Mandolini

Authors
  1. Claudio Favi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michele Germani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marco Mandolini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Claudio Favi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Favi, C., Germani, M. & Mandolini, M. Development of complex products and production strategies using a multi-objective conceptual design approach. Int J Adv Manuf Technol 95, 1281–1291 (2018). https://doi.org/10.1007/s00170-017-1321-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-017-1321-y

Keywords

Search

Navigation