Skip to main content
SpringerLink
Log in

Implementation of Artificial Neural Network for Demanufacturing Operation in the Rail Industry

  • Conference paper
  • First Online:
Soft Computing: Theories and Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 627))

  • 328 Accesses

Abstract

End-of-life (EoL) component recovery is gaining popularity in a variety of industries around the world. The dominance of linear manufacturing in a variety of industries has given birth to new ideas about EoL component recirculation. End-of-life railcar component degrading treatment is expected to have an impact on disposal and resource sustainability. As a result, the goal of this study is to create a predictive model for the scheduling of demanufacturing operations activities using an artificial neural network (ANN). During the development of the predictive tool, an ANN tool with a prediction mechanism was set up to focus on the completion times of demanufacturing options. The MATLAB2018a program and the transfer function “tansig” were used to train the ANN using the backpropagation and Levenberg–Marquardt methods. Data was trained to visualize the predictability of the demanufacturing operation, and a correlation coefficient of 1 was obtained. The data set was observed to fall along the line of best fit after repeated training of the input data. This indicated that the developed approach was highly efficient, with a strong ability to predict the completion timeframes of demanufacturing operation options.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Merkisz-Guranowska A, Merkisz J, Jacyna M, Pyza D, Stawecka H (2014) Rail vehicles recycling. WIT Trans Built Environ 135:425–436

    Article  Google Scholar 

  2. Lamerew YA, Brissaud D (2019) Circular economy assessment tool for end of life product recovery strategies. J Remanuf 9:169–185

    Article  Google Scholar 

  3. Paterson DA, Ijomah WL, Windmill JF (2017) End-of-life decision tool with emphasis on remanufacturing. J Clean Prod 148:653–664

    Article  Google Scholar 

  4. Jiang Z, Wang H, Zhang H, Mendis G, Sutherland JW (2019) Value recovery options portfolio optimization for remanufacturing end of life product. J Clean Prod 210:419–431

    Article  Google Scholar 

  5. Johnson MR, Mccarthy IP (2014) Product recovery decisions within the context of extended producer responsibility. J Eng Tech Manage 34:9–28

    Article  Google Scholar 

  6. Mistry P, Johnson M, Galappaththi U (2021) Selection and ranking of rail vehicle components for optimal lightweighting using composite materials. Proc Inst Mech Eng Part F: J Rail Rapid Transit 235:390–402

    Article  Google Scholar 

  7. Muvunzi R, Mpofu K, Daniyan I, Fameso F (2022) Analysis of potential materials for local production of a rail car component using additive manufacturing. Heliyon 8(e09405):1–8

    Google Scholar 

  8. Williams JAS (2007) A review of research towards computer integrated demanufacturing for materials recovery. Int J Comput Integr Manuf 20:773–780

    Article  Google Scholar 

  9. Karakayal I, Emir-Farinas H, Akçal E (2010) Pricing and recovery planning for demanufacturing operations with multiple used products and multiple reusable components. Comput Ind Eng 59:55–63

    Article  Google Scholar 

  10. Munakata T (2008) Fundamentals of the new artificial intelligence: neural, evolutionary, fuzzy and more, Springer Science and Business Media

    Google Scholar 

  11. Mohamed Na, M AS, Abd Wahab D, Abdullah S, Tihth RM (2011) Development of artificial neural network for optimisation of reusability in automotive components. J Appl Sci 11:996–1003

    Google Scholar 

  12. Lv C, Xing Y, Zhang J, Na X, Li Y, Liu T, Cao D, Wang F-Y (2017) Levenberg–Marquardt backpropagation training of multilayer neural networks for state estimation of a safety-critical cyber-physical system. IEEE Trans Industr Inf 14:3436–3446

    Article  Google Scholar 

  13. Wang Y, Kim SP, Principe JC (2005) Comparison of TDNN training algorithms in brain machine interfaces. In: Proceedings. 2005 IEEE international joint conference on neural networks. IEEE, 2459–2462

    Google Scholar 

  14. Seiffert U (2006) Training of large-scale feed-forward neural networks. The 2006 IEEE international joint conference on neural network proceedings, IEEE, 5324–5329

    Google Scholar 

  15. Bilski J, Kowalczyk B, Marchlewska A, Zurada JM (2020) Local Levenberg-Marquardt algorithm for learning feedforwad neural networks. J Artific Intell Soft Comput Res 10(4):290–316

    Google Scholar 

  16. Wahab DA, Amelia L, Hooi NK, Cheharsu C, Azhari C (2008) The application of artificial intelligence in optimisation of automotive components for reuse. Mater Manuf Eng 31(2):595–601

    Google Scholar 

  17. Shokohyar S, Mansour S, Karimi B (2014) A model for integrating services and product EoL management in sustainable product service system (S-PSS). Intell Manuf 25:427–440

    Article  Google Scholar 

  18. Walczak S (2007) Neural networks in organizational research: applying pattern recognition to the analysis of organizational behavior. Organ Res Methods 10:710

    Article  Google Scholar 

  19. Daniyan IA, Mpofu K, Tlhabadira I, Ramatsetse BI (2021) Process design for milling operation of titanium alloy (Ti6Al4V) using artificial neural network. Int J Mech Eng Robot Res 10(11):601–611

    Article  Google Scholar 

  20. May RJ, Maier HR, Dandy GC (2010) Data splitting for artificial neural networks using SOM-based stratified sampling. Neural Netw 23:283–294

    Article  Google Scholar 

  21. Shaikhina T, Khovanova NA (2017) Handling limited datasets with neural networks in medical applications: a small-data approach. Artif Intell Med 75:51–63

    Article  Google Scholar 

  22. Bagshaw KB (2021) PERT and CPM in project management with practical examples. Am J Oper Res 11:215–226

    Google Scholar 

  23. Boushaala AA (2014) An approach for project scheduling using PERT/CPM and petri nets (PNs) tools. Indus Eng Oper Manage 5:939–947

    Google Scholar 

  24. Daniyan IA, Bello EI, Ogedengbe TI, Mpofu K (2020) Use of central composite design and artificial neural network for predicting the yield of biodiesel. Procedia CIRP 89:59–67

    Article  Google Scholar 

  25. Daniyan IA, Tlhabadira I, Mpofu K, Adeodu AO (2020) Development of numerical models for the prediction of temperature and surface roughness during the machining operation of titanium alloy (Ti6Al4V). Acta Polytech J 60(5):369–390

    Article  Google Scholar 

Download references

Acknowledgements

Funding: The authors disclosed receipt of the following financial support for the research: Technology Innovation Agency (TIA) South Africa, Gibela Rail Transport Consortium (GRTC), National Research Foundation (NRF grant 123575), and the Tshwane University of Technology (TUT).

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Tshwane University of Technology, Pretoria, 0001, South Africa

    Humbulani Simon Phuluwa, Ilesanmi Daniyan & Khumbulani Mpofu

Authors
  1. Humbulani Simon Phuluwa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ilesanmi Daniyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khumbulani Mpofu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Rajesh Kumar

  2. Western Norway University of Applied Sciences, Bergen, Norway

    Ajit Kumar Verma

  3. Department of Computer Science, Shobhit University, Gangoh, India

    Tarun K. Sharma

  4. Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Om Prakash Verma

  5. University Institute of Technology, Himachal Pradesh University, Shimla, Himachal Pradesh, India

    Sanjay Sharma

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Phuluwa, H.S., Daniyan, I., Mpofu, K. (2023). Implementation of Artificial Neural Network for Demanufacturing Operation in the Rail Industry. In: Kumar, R., Verma, A.K., Sharma, T.K., Verma, O.P., Sharma, S. (eds) Soft Computing: Theories and Applications. Lecture Notes in Networks and Systems, vol 627. Springer, Singapore. https://doi.org/10.1007/978-981-19-9858-4_2

Download citation

Publish with us

Policies and ethics

Search

Navigation