IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v32y2021i8d10.1007_s10845-021-01741-y.html
   My bibliography  Save this article

On the role of complexity in machining time estimation

Author

Listed:
  • Antonio Armillotta

    (Politecnico di Milano)

Abstract

Early cost estimation of machined parts is difficult as it requires detailed process information that is not usually available during product design. Parametric methods address this issue by estimating machining time from predictors related to design choices. One of them is complexity, defined as a function of dimensions and tolerances from an analogy with information theory. However, complexity has only a limited correlation with machining time unless restrictive assumptions are made on part types and machining processes. The objective of the paper is to improve the estimation of machining time by combining complexity with additional parameters. For this purpose, it is first shown that three factors that influence machining time (part size, area of machined features, work material) are not fully captured by complexity alone. Then an optimal set of predictors is selected by regression analysis of time estimates made on sample parts using an existing feature-based method. The proposed parametric model is shown to predict machining time with an average percentage error of 25% compared to the baseline method, over a wide range of part geometries and machining processes. Therefore, the model is accurate enough to support comparison of design alternatives as well as bidding and make-or-buy decisions.

Suggested Citation

  • Antonio Armillotta, 2021. "On the role of complexity in machining time estimation," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2281-2299, December.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:8:d:10.1007_s10845-021-01741-y
    DOI: 10.1007/s10845-021-01741-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01741-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-021-01741-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Loyer, Jean-Loup & Henriques, Elsa & Fontul, Mihail & Wiseall, Steve, 2016. "Comparison of Machine Learning methods applied to the estimation of manufacturing cost of jet engine components," International Journal of Production Economics, Elsevier, vol. 178(C), pages 109-119.
    2. Cavalieri, Sergio & Maccarrone, Paolo & Pinto, Roberto, 2004. "Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry," International Journal of Production Economics, Elsevier, vol. 91(2), pages 165-177, September.
    3. Roy, R. & Souchoroukov, P. & Shehab, E., 2011. "Detailed cost estimating in the automotive industry: Data and information requirements," International Journal of Production Economics, Elsevier, vol. 133(2), pages 694-707, October.
    4. Qian, Li & Ben-Arieh, David, 2008. "Parametric cost estimation based on activity-based costing: A case study for design and development of rotational parts," International Journal of Production Economics, Elsevier, vol. 113(2), pages 805-818, June.
    5. Doriana M. D’Addona & A. M. M. Sharif Ullah & D. Matarazzo, 2017. "Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1285-1301, August.
    6. Magali Mauchand & Ali Siadat & Alain Bernard & Nicolas Perry, 2008. "Proposal for tool-based method of product cost estimation during conceptual design," Post-Print hal-00476631, HAL.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Johnson, Michael D. & Kirchain, Randolph E., 2009. "Quantifying the effects of product family decisions on material selection: A process-based costing approach," International Journal of Production Economics, Elsevier, vol. 120(2), pages 653-668, August.
    2. Duffner, Fabian & Mauler, Lukas & Wentker, Marc & Leker, Jens & Winter, Martin, 2021. "Large-scale automotive battery cell manufacturing: Analyzing strategic and operational effects on manufacturing costs," International Journal of Production Economics, Elsevier, vol. 232(C).
    3. Nasser Amaitik & Ming Zhang & Zezhong Wang & Yuchun Xu & Gareth Thomson & Yiyong Xiao & Nikolaos Kolokas & Alexander Maisuradze & Oscar Garcia & Michael Peschl & Dimitrios Tzovaras, 2022. "Cost Modelling to Support Optimum Selection of Life Extension Strategy for Industrial Equipment in Smart Manufacturing," Circular Economy and Sustainability, Springer, vol. 2(4), pages 1425-1444, December.
    4. Duffner, F. & Wentker, M. & Greenwood, M. & Leker, J., 2020. "Battery cost modeling: A review and directions for future research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    5. Bodendorf, Frank & Xie, Qiao & Merkl, Philipp & Franke, Jörg, 2022. "A multi-perspective approach to support collaborative cost management in supplier-buyer dyads," International Journal of Production Economics, Elsevier, vol. 245(C).
    6. Antonio Del Prete & Rodolfo Franchi & Stefania Cacace & Quirico Semeraro, 2020. "Optimization of cutting conditions using an evolutive online procedure," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 481-499, February.
    7. Deng, S. & Yeh, Tsung-Han, 2011. "Using least squares support vector machines for the airframe structures manufacturing cost estimation," International Journal of Production Economics, Elsevier, vol. 131(2), pages 701-708, June.
    8. Zbigniew Leszczyński & Tomasz Jasiński, 2020. "Comparison of Product Life Cycle Cost Estimating Models Based on Neural Networks and Parametric Techniques—A Case Study for Induction Motors," Sustainability, MDPI, vol. 12(20), pages 1-14, October.
    9. Sachin Kumar & T. Gopi & N. Harikeerthana & Munish Kumar Gupta & Vidit Gaur & Grzegorz M. Krolczyk & ChuanSong Wu, 2023. "Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 21-55, January.
    10. Sebastjan Škerlič & Robert Muha, 2020. "A Model for Managing Packaging in the Product Life Cycle in the Automotive Industry," Sustainability, MDPI, vol. 12(22), pages 1-19, November.
    11. Askarany, Davood & Yazdifar, Hassan, 2012. "An investigation into the mixed reported adoption rates for ABC: Evidence from Australia, New Zealand and the UK," International Journal of Production Economics, Elsevier, vol. 135(1), pages 430-439.
    12. K. Reddy & H. S. Venter & M. S. Olivier, 2012. "Using time-driven activity-based costing to manage digital forensic readiness in large organisations," Information Systems Frontiers, Springer, vol. 14(5), pages 1061-1077, December.
    13. Qian, Li & Ben-Arieh, David, 2008. "Parametric cost estimation based on activity-based costing: A case study for design and development of rotational parts," International Journal of Production Economics, Elsevier, vol. 113(2), pages 805-818, June.
    14. Kwon, He-Boong, 2017. "Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 159-170.
    15. Lee, Jooh & Kwon, He-Boong, 2017. "Progressive performance modeling for the strategic determinants of market value in the high-tech oriented SMEs," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 91-102.
    16. Wang, Hui & Gong, Qiguo & Wang, Shouyang, 2017. "Information processing structures and decision making delays in MRP and JIT," International Journal of Production Economics, Elsevier, vol. 188(C), pages 41-49.
    17. Yaxuan Liu, 2021. "Developing the network social media in graphic design based on artificial neural network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(4), pages 640-653, August.
    18. Ciurana, J. & Quintana, G. & Garcia-Romeu, M.L., 2008. "Estimating the cost of vertical high-speed machining centres, a comparison between multiple regression analysis and the neural networks approach," International Journal of Production Economics, Elsevier, vol. 115(1), pages 171-178, September.
    19. Johnson, Michael & Kirchain, Randolph, 2009. "Quantifying the effects of parts consolidation and development costs on material selection decisions: A process-based costing approach," International Journal of Production Economics, Elsevier, vol. 119(1), pages 174-186, May.
    20. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:32:y:2021:i:8:d:10.1007_s10845-021-01741-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.