Research Papers: Design for Manufacture and the Life Cycle

A Cost-Driven Design Methodology for Additive Manufactured Variable Platforms in Product Families

[+] Author and Article Information
Xiling Yao

Singapore Centre for 3D Printing,
School of Mechanical and Aerospace Engineering,
Nanyang Technological University,
50 Nanyang Avenue,
Singapore 639798, Singapore
e-mail: yaox0006@e.ntu.edu.sg

Seung Ki Moon

Assistant Professor
Singapore Centre for 3D Printing,
School of Mechanical and Aerospace Engineering,
Nanyang Technological University,
50 Nanyang Avenue,
Singapore 639798, Singapore
e-mail: skmoon@ntu.edu.sg

Guijun Bi

Singapore Institute of Manufacturing Technology,
71 Nanyang Drive,
Singapore 638075, Singapore
e-mail: gjbi@simtech.a-star.edu.sg

1Corresponding author.

Contributed by the Design for Manufacturing Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 4, 2015; final manuscript received January 6, 2016; published online February 5, 2016. Editor: Shapour Azarm.

J. Mech. Des 138(4), 041701 (Feb 05, 2016) (12 pages) Paper No: MD-15-1071; doi: 10.1115/1.4032504 History: Received February 04, 2015; Revised January 06, 2016

Additive manufacturing (AM) has evolved from prototyping to functional part fabrication for a wide range of applications. Challenges exist in developing new product design methodologies to utilize AM-enabled design freedoms while limiting costs at the same time. When major design changes are made to a part, undesired high cost increments may be incurred due to significant adjustments of AM process settings. In this research, we introduce the concept of an additive manufactured variable product platform and its associated process setting platform. Design and process setting adjustments based on a reference part are constrained within a bounded feasible space (FS) in order to limit cost increments. In this paper, we develop a cost-driven design methodology for product families implemented with additive manufactured variable platforms. A fuzzy time-driven activity-based costing (FTDABC) approach is introduced to estimate AM production costs based on process settings. Time equations in the FTDABC are computed in a trained adaptive neuro-fuzzy inference system (ANFIS). The process setting adjustment's FS boundary is identified by solving a multi-objective optimization problem. Variable platform design parameter limitations are computed in a Mamdani-type expert system, and then used as constraints in the design optimization to maximize customer perceived utility. Case studies on designing an R/C racing car family illustrate the proposed methodology and demonstrate that the optimized additive manufactured variable platforms can improve product performances at lower costs than conventional consistent platform-based design.

Copyright © 2016 by ASME
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Gibson, I. , Rosen, D. W. , and Stucker, B. , 2010, Additive Manufacturing Technologies, Springer, New York.
Bourell, D. L. , Rosen, D. W. , and Leu, M. C. , 2014, “ The Roadmap for Additive Manufacturing and Its Impact,” 3D Print. Addit. Manuf., 1(1), pp. 6–9. [CrossRef]
Simpson, T. W. , Jiao, J. , Siddique, Z. , and Hölttä-Otto, K. , 2014, Advances in Product Family and Product Platform Design, Springer, New York.
Moon, S. K. , Park, K. , and Simpson, T. , 2014, “ Platform Design Variable Identification for a Product Family Using Multi-Objective Particle Swarm Optimization,” Res. Eng. Des., 25(2), pp. 95–108. [CrossRef]
Song, B. , Dong, S. , Deng, S. , Liao, H. , and Coddet, C. , 2014, “ Microstructure and Tensile Properties of Iron Parts Fabricated by Selective Laser Melting,” Opt. Laser Technol., 56(0), pp. 451–460. [CrossRef]
Cheng, W. , Fuh, J. Y. H. , Nee, A. Y. C. , Wong, Y. S. , Loh, H. T. , and Miyazawa, T. , 1995, “ Multi-Objective Optimization of Part-Building Orientation in Stereolithography,” Rapid Prototyping J., 1(4), pp. 12–23. [CrossRef]
Canellidis, V. , Giannatsis, J. , and Dedoussis, V. , 2009, “ Genetic-Algorithm-Based Multi-Objective Optimization of the Build Orientation in Stereolithography,” Int. J. Adv. Manuf. Technol., 45(7–8), pp. 714–730. [CrossRef]
Strano, G. , Hao, L. , Everson, R. M. , and Evans, K. E. , 2011, “ Multi-Objective Optimization of Selective Laser Sintering Processes for Surface Quality and Energy Saving,” Proc. Inst. Mech. Eng., Part B, 225(B9), pp. 1673–1682. [CrossRef]
Verma, A. , and Rai, R. , 2013, “ Energy Efficient Modeling and Optimization of Additive Manufacturing Processes,” Solid Freeform Fabrication Symposium, Austin, TX.
Hopkinson, N. , and Dickens, P. , 2003, “ Analysis of Rapid Manufacturing—Using Layer Manufacturing Processes for Production,” Proc. Inst. Mech. Eng., Part C, 217(1), pp. 31–39. [CrossRef]
Ruffo, M. , Tuck, C. , and Hague, R. , 2006, “ Cost Estimation for Rapid Manufacturing—Laser Sintering Production for Low to Medium Volumes,” Proc. Inst. Mech. Eng., Part B, 220(9), pp. 1417–1427. [CrossRef]
Atzeni, E. , and Salmi, A. , 2012, “ Economics of Additive Manufacturing for End-Usable Metal Parts,” Int. J. Adv. Manuf. Technol., 62(9–12), pp. 1147–1155. [CrossRef]
Lindemann, C. , Jahnke, U. , Moi, M. , and Koch, R. , 2013, “Impact and Influence Factors of Additive Manufacturing on Product Lifecycle Costs,” Solid Freeform Fabrication Symposium, Austin, TX.
Lindemann, C. , Jahnke, U. , Moi, M. , and Koch, R. , 2012, “Analyzing Product Lifecycle Costs for a Better Understanding of Cost Drivers in Additive Manufacturing,” Solid Freeform Fabrication Symposium, Austin, TX.
Yim, S. , and Rosen, D. , 2012, “ Build Time and Cost Models for Additive Manufacturing Process Selection,” ASME Paper No. DETC2012-70940.
Williams, C. , Allen, J. , Rosen, D. W. , and Mistree, F. , 2006, “ Process Parameter Platform Design to Manage Workstation Capacity,” Product Platform and Product Family Design, T. Simpson , Z. Siddique , and R. J. Jiao , eds., Springer, New York, pp. 421–455.
Luo, X. , Yang, W. , Kwong, C. , Tang, J. , and Tang, J. , 2014, “ Linear Programming Embedded Genetic Algorithm for Product Family Design Optimization With Maximizing Imprecise Part-Worth Utility Function,” Concurrent Eng., 22(4), pp. 309–319. [CrossRef]
Weck, O. L. D. , Suh, E. S. , and Chang, D. , 2003, “ Product Family and Platform Portfolio Optimization,” ASME Paper No. DETC03/DAC-48721.
Chowdhury, S. , Messac, A. , and Khire, R. A. , 2011, “ Comprehensive Product Platform Planning (CP3) Framework,” ASME J. Mech. Des., 133(10), p. 101004. [CrossRef]
Jiao, J. , Zhang, Y. , and Wang, Y. , 2007, “ A Generic Genetic Algorithm for Product Family Design,” J. Intell. Manuf., 18(2), pp. 233–247. [CrossRef]
Suh, E. , De Weck, O. , and Chang, D. , 2007, “ Flexible Product Platforms: Framework and Case Study,” Res. Eng. Des., 18(2), pp. 67–89. [CrossRef]
Wang, L. , Song, B. , Li, X. , and Ng, W. K. , 2007, “ A Product Family Based Life Cycle Cost Model for Part Variety and Change Analysis,” International Conference on Engineering Design (ICED'07), Paris, France, Paper No. DS14_P_152.
Bryan, A. , Wang, H. , and Abell, J. , 2013, “ Concurrent Design of Product Families and Reconfigurable Assembly Systems,” ASME J. Mech. Des., 135(5), p. 051001. [CrossRef]
Simpson, T. , Siddique, Z. , and Jiao, J. , 2006, “ Platform-Based Product Family Development,” Product Platform and Product Family Design, T. Simpson , Z. Siddique , and R. J. Jiao , eds., Springer, New York, pp. 1–15.
Kaplan, R. S. , and Anderson, S. R. , 2004, “ Time-Driven Activity-Based Costing,” Harv. Bus. Rev., 82(11), pp. 131–140. [PubMed]
Chansaad, A. , Rattanamanee, W. , Chaiprapat, A. , and Yenradee, P. , 2012, “ Fuzzy Time-Driven Activity-Based Costing Model in an Uncertain Manufacturing Environment,” Asia Pacific Industrial Engineering and Management Systems Conference, Phuket, Thailand.
Sarokolaei, M. A. , Saviz, M. , Moradloo, M. F. , and Dahaj, N. S. , 2013, “ Time Driven Activity Based Costing by Using Fuzzy Logics,” Procedia–Soc. Behav. Sci., 75(0), pp. 338–345. [CrossRef]
Dadbakhsh, S. , Hao, L. , Jerrard, P. G. E. , and Zhang, D. Z. , 2012, “ Experimental Investigation on Selective Laser Melting Behaviour and Processing Windows of In Situ Reacted Al/Fe2O3 Powder Mixture,” Powder Technol., 231(0), pp. 112–121. [CrossRef]
Leekwijck, W. V. , and Kerre, E. E. , 1999, “ Defuzzification: Criteria and Classification,” Fuzzy Sets Syst., 108(2), pp. 159–178. [CrossRef]
Jang, J. S. R. , 1993, “ Anfis: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. Syst., Man Cybernetics, 23(3), pp. 665–685. [CrossRef]
Verma, A. , and Rai, R. , 2014, “ Computational Geometric Solutions for Efficient Additive Manufacturing Process Planning,” ASME Paper No. DETC2014-34067.
Babuška, R. , 2003, “ Neuro-Fuzzy Methods for Modeling and Identification,” Recent Advances in Intelligent Paradigms and Applications, A. Abraham , L. C. Jain , and J. Kacprzyk , eds., Physica-Verlag HD, Heidelberg, Germany, pp. 161–186.
“Willit3dprint,” last accessed July 13, 2015, http://www.willit3dprint.com/
Konak, A. , Coit, D. W. , and Smith, A. E. , 2006, “ Multi-Objective Optimization Using Genetic Algorithms: A Tutorial,” Reliab., Eng. Syst. Saf., 91(9), pp. 992–1007. [CrossRef]
Hague, R. , Mansour, S. , and Saleh, N. , 2004, “ Material and Design Considerations for Rapid Manufacturing,” Int. J. Prod. Res., 42(22), pp. 4691–4708. [CrossRef]
Samperi, M. , Chernow, E. , Simpson, T. W. , Joshi, S. , and Talbot, M. , 2013, “ Towards a Process Workflow for Designing and Fabricating Parts Using Additive Manufacturing,” 2013 RAPID Conference and Exposition, Pittsburgh, PA, June 10–13, Paper No. 64832.
Vayre, B. , Vignat, F. , and Villeneuve, F. , 2012, “ Designing for Additive Manufacturing,” Procedia CIRP, 3(0), pp. 632–637. [CrossRef]
Vayre, B. , Vignat, F. , and Villeneuve, F. , 2013, “ Identification on Some Design Key Parameters for Additive Manufacturing: Application on Electron Beam Melting,” Procedia CIRP, 7(0), pp. 264–269. [CrossRef]
Train, K. E. , 2009, Discrete Choice Methods With Simulation, Cambridge University Press, Cambridge, UK.
“Traxxas Products,” last accessed Jan. 22, 2015, http://traxxas.com/products
Rosen, D. W. , 2014, “ Research Supporting Principles for Design for Additive Manufacturing,” Virtual Phys. Prototyping, 9(4), pp. 225–232. [CrossRef]
Moon, S. K. , Tan, Y. , Hwang, J. , and Yoon, Y.-J. , 2014, “ Application of 3D Printing Technology for Designing Light-Weight Unmanned Aerial Vehicle Wing Structures,” Int. J. Precis. Eng. Manuf.-Green Technol., 1(3), pp. 223–228. [CrossRef]


Grahic Jump Location
Fig. 1

Overview of the proposed cost-driven design methodology and data flow

Grahic Jump Location
Fig. 2

Activities, cost drivers, and process setting adjustment terms in AM (the SLM)

Grahic Jump Location
Fig. 3

A sample AM process setting adjustments' FS

Grahic Jump Location
Fig. 4

Bumper design with the honeycomb feature

Grahic Jump Location
Fig. 5

Calculated fuzzy capacity cost rate Ri=μ(Ci/Ti)

Grahic Jump Location
Fig. 6

Identified FS with the boundary



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