Skip to main content
SpringerLink
Log in

Part family formation method for delayed reconfigurable manufacturing system based on machine learning

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), were proposed to solve the convertibility problems of RMS. As a part family-oriented manufacturing system paradigm, D-RMS should concern delayed reconfiguration at the outset of part family formation. To bring the characteristics of delayed reconfiguration into the part family of D-RMS, an exclusive part family formation method for D-RMS based on machine learning is proposed in this paper. Firstly, a similarity coefficient that considers the characteristics of D-RMS is put forward based on the operation sequence of part. The positions of the common operations in the corresponding operation sequences are investigated. The more former common operations there are, the more probability it is that the parts are grouped into the same part family. The relative positions of the common operations are considered by proposing a concept of the longest relative position common operation subsequence (LPCS). Additionally, the position difference and discontinuity of the LPCSs in the corresponding operation sequences are analyzed. A similarity coefficient is proposed that incorporates the abovementioned factors. Secondly, a machine learning method named K-medoids is adopted to group parts into families based on the calculation result of the similarity coefficient. Finally, a case study is presented to implement the proposed part family formation method for D-RMS, where the effectiveness of the proposed method is verified through comparison.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  • Abdi, M. R. (2012). Product family formation and selection for reconfigurability using analytical network process. International Journal of Production Research, 50(17), 4908–4921.

    Article  Google Scholar 

  • Abdi, M. R., & Labib, A. W. (2004). Grouping and selecting products: The design key of reconfigurable manufacturing systems (RMSs). International Journal of Production Research, 42(3), 521–546.

    Article  Google Scholar 

  • Alonso-Perez, J. L., Cardenas-Maciel, S. L., Trujillo-Navarrete, B., et al. (2021). An approach for designing smart manufacturing for the research and development of dye-sensitize solar cell. Journal of Intelligent Manufacturing, 171, 1–14.

    Google Scholar 

  • Ameer, M., & Dahane, M. (2021). Reconfigurability improvement in Industry 4.0: A hybrid genetic algorithm-based heuristic approach for a co-generation of setup and process plans in a reconfigurable environment. Journal of Intelligent Manufacturing, 7, 1–23.

    Google Scholar 

  • Ashraf, M., & Hasan, F. (2015). Product family formation based on multiple product similarities for a reconfigurable manufacturing system. International Journal of Modelling in Operations Management, 5(3–4), 247–265.

    Article  Google Scholar 

  • Askin, R. G., & Zhou, M. (1998). Formation of independent flow-line cells based on operation requirements and machine capabilities. IIE Transactions, 30(4), 319–329.

    Article  Google Scholar 

  • Bortolini, M., Galizia, F. G., & Mora, C. (2018). Reconfigurable manufacturing systems: Literature review and research trend. Journal of Manufacturing Systems, 49, 93–106.

    Article  Google Scholar 

  • Choobineh, F. (1988). A framework for the design of cellular manufacturing systems. International Journal of Production Research, 26(7), 1161–1172.

    Article  Google Scholar 

  • Ezzat, O., Medini, K., Boucher, X., et al. (2022). A clustering approach for modularizing service-oriented systems. Journal of Intelligent Manufacturing, 33, 719–734.

    Article  Google Scholar 

  • Farid, A. M. (2017). Measures of reconfigurability and its key characteristics in intelligent manufacturing systems. Journal of Intelligent Manufacturing, 28(2), 353–369.

    Article  Google Scholar 

  • Gauss, L., Lacerda, D. P., & Cauchick Miguel, P. A. (2021). Module-based product family design: Systematic literature review and meta-synthesis. Journal of Intelligent Manufacturing, 32(1), 265–312.

    Article  Google Scholar 

  • Goyal, K. K., Jain, P. K., & Jain, M. (2013). A comprehensive approach to operation sequence similarity based part family formation in the reconfigurable manufacturing system. International Journal of Production Research, 51(6), 1762–1776.

    Article  Google Scholar 

  • Gupta, A., Jain, P. K., & Kumar, D. (2014). Part family formation for reconfigurable manufacturing system using K-means algorithm. International Journal of Internet Manufacturing and Services, 3(3), 244–262.

    Article  Google Scholar 

  • Hasan, S. M., Baqai, A. A., Butt, S. U., et al. (2018). Product family formation based on complexity for assembly systems. The International Journal of Advanced Manufacturing Technology, 95(1), 569–585.

    Article  Google Scholar 

  • Ho, Y. C., Lee, C. C., & Moodie, C. L. (1993). Two sequence-pattern, matching-based, flow analysis methods for multi-flowlines layout design. International Journal of Production Research, 31(7), 1557–1578.

    Article  Google Scholar 

  • Huang, S., Wang, G., Lei, D., et al. (2022). Toward digital validation for rapid product development based on digital twin: A framework. The International Journal of Advanced Manufacturing Technology, 119, 1–15.

    Article  Google Scholar 

  • Huang, S., Wang, G., Shang, X., et al. (2018a). Reconfiguration point decision method based on dynamic complexity for reconfigurable manufacturing system (RMS). Journal of Intelligent Manufacturing, 29(5), 1031–1043.

    Article  Google Scholar 

  • Huang, S., Wang, G., Yan, Y., et al. (2018b). Similarity coefficient of RMS part family grouping considering reconfiguration efforts. IEEE Access, 6, 71871–71883.

    Article  Google Scholar 

  • Huang, S., Wang, G., & Yan, Y. (2019). Delayed reconfigurable manufacturing system. International Journal of Production Research, 57(8), 2372–2391.

    Article  Google Scholar 

  • Huang, S., & Yan, Y. (2019). Part family grouping method for reconfigurable manufacturing system considering process time and capacity demand. Flexible Services and Manufacturing Journal, 31(2), 424–445.

    Article  Google Scholar 

  • Kashkoush, M., & ElMaraghy, H. (2014). Product family formation for reconfigurable assembly systems. Procedia CIRP, 17, 302–307.

    Article  Google Scholar 

  • Khanna, K., & Kumar, R. (2017). Part family and operations group formation for RMS using bond energy algorithm. International Journal of Engineering and Technology, 9(2), 1365–1373.

    Article  Google Scholar 

  • Koren, Y., Gu, X., & Guo, W. (2017). Reconfigurable manufacturing systems: Principles, design, and future trends. Frontiers of Mechanical Engineering, 13(2), 121–136.

    Article  Google Scholar 

  • Koren, Y., Gu, X., & Guo, W. (2018). Choosing the system configuration for high-volume manufacturing. International Journal of Production Research, 56(1–2), 476–490.

    Article  Google Scholar 

  • Koren, Y., Heisel, U., Jovane, F., et al. (1999). Reconfigurable manufacturing system. CIRP Annals - Manufacturing Technology, 48(2), 527–540.

    Article  Google Scholar 

  • Maganha, I., Silva, C., & Ferreira, L. M. D. (2018). Understanding reconfigurability of manufacturing systems: An empirical analysis. Journal of Manufacturing Systems, 48, 120–130.

    Article  Google Scholar 

  • Massimi, E., Khezri, A., Benderbal, H. H., et al. (2020). A heuristic-based non-linear mixed integer approach for optimizing modularity and integrability in a sustainable reconfigurable manufacturing environment. The International Journal of Advanced Manufacturing Technology, 108, 1997–2020.

    Article  Google Scholar 

  • Mehrabi, M. G., Ulsoy, A. G., Koren, Y., et al. (2002). Trends and perspectives in flexible and reconfigurable manufacturing systems. Journal of Intelligent Manufacturing, 13(2), 135–146.

    Article  Google Scholar 

  • Montalto, A., Graziosi, S., Bordegoni, M., et al. (2020). An approach to design reconfigurable manufacturing tools to manage product variability: The mass customisation of eyewear. Journal of Intelligent Manufacturing, 31(1), 87–102.

    Article  Google Scholar 

  • Mukattash, A., Dahmani, N., Al-Bashir, A., et al. (2018). Comprehensive grouping efficacy: A new measure for evaluating block-diagonal forms in group technology. International Journal of Industrial Engineering Computations, 9(1), 155–172.

    Article  Google Scholar 

  • Park, H. S., & Jun, C. H. (2009). A simple and fast algorithm for K-medoids clustering. Expert Systems with Applications, 36(2), 3336–3341.

    Article  Google Scholar 

  • Rajesh, K. D., Krishna, M. M., Ali, M. A., et al. (2017). A modified hybrid similarity coefficient based method for solving the cell formation problem in cellular manufacturing system. Materials Today: Proceedings, 4(2), 1469–1477.

    Google Scholar 

  • Rakesh, K., Jain, P. K., & Mehta, N. K. (2010). A framework for simultaneous recognition of part families and operation groups for driving a reconfigurable manufacturing system. Advances in Production Engineering & Management Journal, 5(1), 45–58.

    Google Scholar 

  • Selim, H. M., Aal, R. M. A., & Mahdi, A. I. (2003). Formation of machine groups and part families: A modified SLC method and comparative study. Integrated Manufacturing Systems, 14(2), 123–137.

    Article  Google Scholar 

  • Tam, K. Y. (1990). An operation sequence based similarity coefficient for part families formation. Journal of Manufacturing Systems, 9(1), 55–67.

    Article  Google Scholar 

  • Tang, L., & Meng, Y. (2021). Data analytics and optimization for smart industry. Frontiers of Engineering Management, 8(2), 157–171.

    Article  Google Scholar 

  • Ulutas, B. H. (2019). An immune system based algorithm for cell formation problem. Journal of Intelligent Manufacturing, 30(8), 2835–2852.

    Article  Google Scholar 

  • Vakharia, A. J., & Wemmerlov, U. (1990). Designing a cellular manufacturing system: A materials flow approach based on operation sequences. IIE Transactions, 22(1), 84–97.

    Article  Google Scholar 

  • Wang, G. X., Huang, S. H., Shang, X. W., et al. (2016). Formation of part family for reconfigurable manufacturing systems considering bypassing moves and idle machines. Journal of Manufacturing Systems, 41, 120–129.

    Article  Google Scholar 

  • Wang, K., Jiang, Z., Peng, B., et al. (2021). Servitization of manufacturing in the new ICTs era: A survey on operations management. Frontiers of Engineering Management, 8(2), 223–235.

    Article  Google Scholar 

  • Xu, H., Liu, Q., Casillas, J., et al. (2020). Prediction of cell viability in dynamic optical projection stereolithography-based bioprinting using machine learning. Journal of Intelligent Manufacturing, 33, 1–11.

    Google Scholar 

  • Yao, X., Zhou, J., Lin, Y., et al. (2019). Smart manufacturing based on cyber-physical systems and beyond. Journal of Intelligent Manufacturing, 30(8), 2805–2817.

    Article  Google Scholar 

  • Zerrougui, R., Adamou-Mitiche, A. B., & Mitiche, L. (2022). A novel machine learning algorithm for interval systems approximation based on artificial neural network. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01874-0

    Article  Google Scholar 

  • Zhang, Q., Liao, B., & Yang, S. (2020). Application of blockchain in the field of intelligent manufacturing: Theoretical basis, realistic plights, and development suggestions. Frontiers of Engineering Management, 7(4), 578–591.

    Article  Google Scholar 

  • Zheng, P., Xu, X., & Chen, C. H. (2020). A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment. Journal of Intelligent Manufacturing, 31(1), 3–18.

    Article  Google Scholar 

  • Zhou, Y., Wang, L., Ding, L., et al. (2020). Intelligent technologies help operating mobile cabin hospitals effectively cope with COVID-19. Frontiers of Engineering Management, 7(3), 459–460.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the anonymous reviewers and the editor for their comments and feedback, which helped us to improve this paper. All the authors have approved this manuscript for submission to the journal, and there are no conflicts of interest regarding the publication of this manuscript. The authors acknowledge the supporting fund, the China National Postdoctoral Program for Innovative Talents (Grant No. BX20200053), the National Natural Science Foundation of China (Grant No. 51975056) and the China Postdoctoral Science Foundation (Grant No. 2021M700420).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China

    Sihan Huang, Guoxin Wang, Shiqi Nie & Yan Yan

  2. China Academy of Launch Vehicle Technology, Beijing, 100076, China

    Bin Wang

Authors
  1. Sihan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guoxin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shiqi Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yan Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guoxin Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, S., Wang, G., Nie, S. et al. Part family formation method for delayed reconfigurable manufacturing system based on machine learning. J Intell Manuf 34, 2849–2863 (2023). https://doi.org/10.1007/s10845-022-01956-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-022-01956-7

Keywords

Search

Navigation