Skip to main content
SpringerLink
Log in

Six Sigma and Random Forests Application for Product Quality System Control Development

  • Conference paper
  • First Online:
Advances in Manufacturing III (MANUFACTURING 2022)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Included in the following conference series:

Abstract

Intelligent companies are able to become more and more advanced due to the accessible high-tech solutions, data collection and processing systems. Appropriate analysing and using of these large collections of data is significant in Industry 4.0 applications. The advancement of intelligent systems has a powerful effect on the progress of numerous innovative ideas considering the data analysis. They will make the choice of the proper data possible, but they will also suggest suitable methods for obtaining knowledge from these data. On that account, qualitative and quantitative research methods are employed in this paper to examine the outcomes on the product quality acquired from the case study company. The features which affect the product quality in a manufacturing process were recognized on the basis of statistical analysis of the acquired outcomes. Moreover, the work demonstrates that the machine learning method might be applied as a decision-supporting tool in a manufacturing process. The model created with the random forest method allowed to examine the influence of every single process parameter as well as the connection between them on the product quality. The acquired findings may be helpful while defining the most significant variables that might be considered to constitute the input data for developing an automated system for the uninterrupted monitoring of the process parameters, and, in consequence, for the product quality control.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Ivchenko, O., et al.: Method for an effective selection of tools and cutting conditions during precise turning of non-alloy quality steel C45. Materials 15(2), 505 (2022). https://doi.org/10.3390/ma15020505

    Article  Google Scholar 

  2. Kotowska, J., Markowski, M., Burduk, A.: Optimization of the supply of components for mass production with the use of the ant colony algorithm. In: Burduk, A., Mazurkiewicz, D. (eds.) Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017, pp. 347–357. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-64465-3_34

    Chapter  Google Scholar 

  3. Weiss, S.M., Dhurandhar, A., Baseman, R.J.: Improving quality control by early prediction of manufacturing outcomes. In: 19th ACM KIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2013), pp. 1258–1266 (2013)

    Google Scholar 

  4. Guo, J., Li, Y.B., Bai Gang, D.: Dynamic quality prediction of manufacturing process based on extreme learning machine. Adv. Mater. Res. 889–890, 1231–1235 (2014)

    Article  Google Scholar 

  5. Bai, Y., Sun, Z.Z., Deng, J.: Manufacturing quality prediction based on two-step feature learning approach. In: International Conference on Sensing, Diagnostics, Prognostics and Control (SDPC), pp. 260–263 (2017)

    Google Scholar 

  6. Deng, J., Bai, Y., Li, C.: A deep regression model with low-dimensional feature extraction for multi parameter manufacturing quality prediction. Appl. Sci. 10(7), 2522 (2020)

    Article  Google Scholar 

  7. Ji, M.: Research on the framework of quality prediction in intelligent manufacturing. In: Wang, K., Wang, Y., Strandhagen, J., Yu, T. (eds.) Advanced Manufacturing and Automation VII, vol. 451, pp. 573–579. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5768-7_60

    Chapter  Google Scholar 

  8. Schmitt, J., Deuse, J.: Similarity-search and prediction based process parameter adaptation for quality improvement in interlinked manufacturing processes. In: IEEE International Conference on Industrial Engineering and Engineering Management (IEEE IEEM), pp. 700–704 (2018)

    Google Scholar 

  9. Jun, J.H., Chang, T.W., Jun, S.: Quality prediction and yield improvement in process manufacturing based on data analytics. Processes 8(8), 1068 (2020)

    Article  Google Scholar 

  10. Yan, H., Sergin, N.D., Brenneman, W.A., Lange, S.J., Ba, S.: Deep multistage multi-task learning form quality prediction of multistage manufacturing systems. J. Qual. Technol. 53(5), 526–544 (2021)

    Article  Google Scholar 

  11. Zhang, D.H., Liu, Z.Y., Jia, W.Q., Liu, H., Tan, J.R.: Path enhanced bidirectional graph attention network for quality prediction in multistage Manufacturing process. IEEE Trans. Industr. Inf. 18(2), 1018–1027 (2022)

    Article  Google Scholar 

  12. Jung, H., Jeon, J., Choi, D., Park, J.Y.: Application of machine learning techniques in injection molding quality prediction: implications on sustainable manufacturing industry. Sustainability 13(8), 4120 (2021)

    Article  Google Scholar 

  13. Devotta, S., Chelani, A., Vonsild, A.: Prediction of flammability classifications of refrigerants by artificial neural network and random forest model. Int. J. Refrig. 131, 947–955 (2021)

    Article  Google Scholar 

  14. De Freitas, A.G.M., Minho, L.A.C., de Magalhaes B.E.A., Dos Santos, W.N.L., Santos, L.S., Fernandes, S.A.D.: Infracted spectroscopy combined with random forest to deterimine tylosin residues in powdered milk. Food Chem. 365, 130477 (2021)

    Google Scholar 

  15. Lim, D.K., Mustapha, K.B., Pagwiwoko, C.P.: Delamination detection in composite plates using random forests. Compos. Struct. 278, 114676 (2021)

    Google Scholar 

  16. Burduk, A., Musiał, K.: Genetic algorithm adoption to transport task optimization. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds.) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. Advances in Intelligent Systems and Computing, vol. 527, pp. 366–375. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47364-2_35

    Chapter  Google Scholar 

  17. Zhou, H., Yu, K.M., Chen, Y.C., Hsu, H.P.: A hybrid feature selection method RFSTL for manufacturing quality prediction based on a high dimensional imbalanced dataset. IEEE Access 9, 29719–29735 (2021)

    Article  Google Scholar 

  18. Arrais-Castro, A., Varela, M.L.R., Putnik, G.D., Ribeiro, R.A., Machado, J., Ferreira, L.: Collaborative framework for virtual organisation synthesis based on a dynamic multi-criteria decision model. Int. J. Comput. Integr. Manuf. 31(9), 857–868 (2018)

    Article  Google Scholar 

  19. Kunz, G., Machado, J., Perondi, E., Vyatkin, V.A.: Formal methodology for accomplishing IEC 61850 real-time communication requirements. IEEE Trans. Industr. Electron. 64(8), 6582–6590 (2017)

    Article  Google Scholar 

  20. Cohen, S., Kamarck, T., Mermelstein, R.: Perceived stress scale. Measuring Stress Guide Health Soc. Sci. 10(2), 1–2 (1994)

    Google Scholar 

  21. Araújo, A.F., Varela, M.L.R., Gomes, M.S., Barreto, R.C.C., Trojanowska, J.: Development of an intelligent and automated system for lean industrial production, adding maximum productivity and efficiency in the production process. In: Hamrol, A., Ciszak, O., Legutko, S., Jurczyk, M. (eds.) Advances in Manufacturing. LNME, pp. 131–140. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-68619-6_13

    Chapter  Google Scholar 

  22. Breiman, L.: Bagging predictors. Mach. Learn. 26, 123–140 (1996)

    MATH  Google Scholar 

  23. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2011)

    Article  Google Scholar 

  24. Antosz, K., Jasiulewicz-Kaczmarek, M., Paśko, Ł., Zhang, C., Wang, S.: Application of machine learning and rough set theory in lean maintenance decision support system development. Eksploatacja i Niezawodnosc Maintenance Reliab. 23(4), 695–708 (2021). https://doi.org/10.17531/ein.2021.4.12

  25. Costa, E.P., Lorena, A.C., Carvalho, A.C.P.L.F., Freitas, A.A.: A review of performance evaluation measures for hierarchical classifiers. In: Evaluation Methods for Machine Learning II: Papers from the AAAI-2007 Workshop, pp. 182–196. AAAI Press (2007)

    Google Scholar 

  26. Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861–874 (2006)

    Article  Google Scholar 

  27. Powers, D.: Evaluation: from precision, recall and F-score to ROC, unforcedness, nakedness & correlation. J. Mach. Learn. Technol. 2, 37–63 (2011)

    Google Scholar 

  28. Provost, F., Fawcett, T., Kohavi, R.: The case against accuracy estimation for comparing classifiers. In: Proceedings of the ICML 1998, pp. 445–453. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  29. Pasko, Ł., Setlak, G.: Examination of quality of predictive market segmentation. Zeszyty Naukowe Politechniki Śląskiej, Seria Informatyka, 37, 1(123), 83–97 (2016). (in Polish)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, Al. Powstancow Warszawy 12, 35-959, Rzeszów, Poland

    Katarzyna Antosz & Łukasz Paśko

  2. Department of Production Computerisation and Robotisation, Faculty of Mechanical Engineering, Lublin University of Technology, Lublin, Poland

    Arkadiusz Gola

  3. Research Centre, Guimarães, Portugal

    Teresa Malheiro & Arminda Manuela Gonçalves

  4. Algoritmi Research Centre, School of Engineering, University of Minho, Guimarães, Portugal

    Leonilde Varela

Authors
  1. Katarzyna Antosz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arkadiusz Gola
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Łukasz Paśko
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Teresa Malheiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Arminda Manuela Gonçalves
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Leonilde Varela
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Katarzyna Antosz .

Editor information

Editors and Affiliations

  1. Department of Production Engineering, Poznan University of Technology, Poznan, Poland

    Adam Hamrol

  2. Production Engineering Department, Poznan University of Technology, Poznan, Poland

    Marta Grabowska

  3. Faculty of Organizational Sciences, University of Maribor, Kranj, Slovenia

    Damjan Maletič

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Antosz, K., Gola, A., Paśko, Ł., Malheiro, T., Gonçalves, A.M., Varela, L. (2022). Six Sigma and Random Forests Application for Product Quality System Control Development. In: Hamrol, A., Grabowska, M., Maletič, D. (eds) Advances in Manufacturing III. MANUFACTURING 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-00218-2_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-00218-2_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-00166-6

  • Online ISBN: 978-3-031-00218-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation