Skip to main content
SpringerLink
Log in

Analysing Impact of the Digitalization on Visual Inspection Process in Smartphone Manufacturing by Using Computer Vision

  • 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

This work shows a case study of the application of data digitalization in visual inspections on smartphone manufacturing, from the context of the need for inspection carried out by humans, in order to analyze its impacts. The impacts of visual stress and fatigue in employees, the technological techniques that can be used through computer vision and artificial intelligence to reduce those impacts in a person, along with the support that digitization can bring to the daily lives of companies and workers are focused in this paper. In this paper is also shown how the digitalization actions were proposed and applied, using a new method for prioritization, along with preliminary results obtained.

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

Similar content being viewed by others

References

  1. Lobato, T.T.: O Sistema Kaizen Como Alicerce Para o Lean Manufacturing: O Caso de Um Centro de Distribuição de Uma Empresa de Cosméticos, 69 (2019)

    Google Scholar 

  2. ABNT NBR 5426. Sampling Plans and Procedures in Attribute Inspection (2015)

    Google Scholar 

  3. Kuric, I., Kandera, M., Klarák, J., Ivanov, V., Więcek, D.: Visual product inspection based on deep learning methods. In: Tonkonogyi, V., et al. (eds.) InterPartner 2019. LNME, pp. 148–156. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40724-7_15

    Chapter  Google Scholar 

  4. Kujawinska, A., Vogt, K., Diering, M., Rogalewicz, M., Waigaonkar, S.D.: Organization of visual inspection and its impact on the effectiveness of inspection. In: Hamrol, A., Ciszak, O., Legutko, S., Jurczyk, M. (eds.) Advances in Manufacturing. LNME, pp. 899–909. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-68619-6_87

    Chapter  Google Scholar 

  5. Vilhena, D., Freitas, S., Guimarães, M., Pinheiro, A.: O papel do psicopedagogo na identificação e intervenção nos distúrbios de aprendizagem relacionados à visão : caso de uma intervenção tardia. O Papel Do Psicopedagogo Na Identificação e Intervenção Nos Distúrbios de Aprendizagem Relacionados à Visão: Caso de Uma Intervenção, 49 (2018)

    Google Scholar 

  6. Mora, J.A.: Study of risk factors that influence visual fatigue and musculoskeletal stress in an open office Work done under the academic supervision of Ana Sofia de Pinho Colim (2019)

    Google Scholar 

  7. Pimenta, A., Carneiro, D., Novais, P., Neves, J.: Monitoring mental fatigue through the analysis of keyboard and mouse interaction patterns. In: Pan, J.S., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds.) Hybrid Artificial Intelligent Systems, pp. 222–231. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40846-5_23

  8. Araújo, P.D.: Análise e classificação da qualidade do Linter e do óleo de algodão utilizando técnicas de visão computacional (2018)

    Google Scholar 

  9. Coughlan, P., Coghlan, D.: Action research for operations management. Int. J. Oper. Prod. Manag. 22(2), 220–240 (2002). https://doi.org/10.1108/01443570210417515

    Article  Google Scholar 

  10. Ribeiro, T.A.O.: Deep Reinforcement Learning for Robot Navigation Systems. Universidade do Minho, Escola de Engenharia, Portugal (2019)

    Google Scholar 

  11. CounterPoint: Global Smartphone Market Share: By Quarter (2021). https://www.counterpointresearch.com/global-smartphone-share/. Accessed 15 July 2021

  12. Pinheiro, R., Viaro, F., Teixeira, F., Silva, R.: Aplicativo de Desdobramento das Funções da Qualidade (QFD) Utilizando Conceitos de Programação Orientada a Objetos. Aplicativo de Desdobramento Das Funções Da Qualidade (QFD) Utilizando Conceitos de Programa Orientada a Objetos, 15 (2018)

    Google Scholar 

  13. Sousa, R.D.O.: Qualidade na Administração Pública: o impacto da certificação ISO 9001: 2000 na satisfação dos munícipes, pp. 1–121 (2007). http://repositorium.sdum.uminho.pt/handle/1822/7020

  14. Data Science. publicado O que é visão computacional? O Que é Visão Computacional? - Data Science Academy (2018). http://datascienceacademy.com.br/blog/o-que-e-visao-computacional/

  15. Jian, C., Gao, J., Ao, Y.: Automatic surface defect detection for mobile phone screen glass based on machine vision. Appl. Soft Comput. J. 52, 348–358 (2017). https://doi.org/10.1016/j.asoc.2016.10.030

    Article  Google Scholar 

  16. Lopes, F.: Visão computacional para estimativa de comportamento de aglomeração de galinhas poedeiras, 72 (2018)

    Google Scholar 

  17. Oliveira, D.: Um sistema inteligente que prevê a produtividades do algodão em imagens de lavouras comerciais, 56 (2019)

    Google Scholar 

  18. Baptista, D.: Machine learning approaches for predicting effects of drug combinations in cancer. (June), 77 (2016)

    Google Scholar 

  19. Six Powerful Use Cases for Machine Learning in Manufacturing (eleks.com), 5th May 2021. https://eleks.com/blog/machine-learning-in-manufacturing/

  20. Shah, V., Costa, D.E.B., Moreira, S.F., Lima, J.F., Varela, M.L.R., Putnik, G.D.: Machine learning applications for industry 4.0. In: Manupati, V.K., Putnik, G.D., Varela, M.L.R. (eds.) Smart and Sustainable Manufacturing Systems for Industry 4.0. CRC Press, Taylor & Francis Group (in press)

    Google Scholar 

  21. Putnik, G.D., Shah, V., Putnik, Z., Ferreira, L.: Machine learning in cyber-physical systems and manufacturing singularity – it does not mean total automation, human is still in the centre: part II – in-CPS and a view from community on industry 4.0 impact on society. J. Mach. Eng. 21(1), 133–153 (2021a). https://doi.org/10.36897/jme/134245

  22. Putnik, G.D., Pabba, S.K., Manupati, V.K., Varela, M.L.R., Ferreira, F.: Semi-double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications. CIRP Ann. Manuf. Technol. 70(1), 365–368 (2021). ISSN 0007-8506. https://doi.org/10.1016/j.cirp.2021.04.046

  23. Barreto, L., Amaral, A., Pereira, T.: Industry 4.0 implications in logistics: an overview. Procedia Manuf. 13, 1245–1252 (2017)

    Google Scholar 

  24. Xu, L.D., Xu, E.L., Li, L.: Industry 4.0: state of the art and future trends. Int. J. Prod. Res. 56, 2941–2962 (2018)

    Google Scholar 

  25. Ferreira, L., et al.: A framework for collaborative practices platforms for humans and machines in industry 4.0 oriented smart and sustainable manufacturing environments. In: Manupati, V.K., Goran, D.P., Rocha, M.L. (eds.) Smart and Sustainable Manufacturing Systems for Industry 4.0. CRC Press, Taylor & Francis Group, Boca Raton (2022, in press)

    Google Scholar 

  26. Deep Learning Book. Visto 06/03/2021. O que é visão computacional? Capítulo 62 - O Que é Aprendizagem Por Reforço? - Deep Learning Book. http://deeplearningbook.com.br/o-que-e-aprendizagem-por-reforco/

Download references

Acknowledgement

This work has been supported by national funds through FCT – Fundação para a Ciência e Tecnologia within the project references: UIDB/00319/2020, EXPL/EME-SIS/1224/2021, and UID/CEC/00319/2019.

Author information

Authors and Affiliations

  1. Department of Production and Systems, Universidade do Minho, Guimarães, Portugal

    Josilene Lima, Leonilde Varela & Goran Putnik

  2. Department of Information Systems, Universidade do Minho, Guimarães, Portugal

    Vaibhav Shah

  3. Department of Mechanical Engineering, Universidade do Minho, Guimarães, Portugal

    Caetano Monteiro & José Machado

  4. Centro ALGORITMI, Universidade do Minho, Guimarães, Portugal

    Vaibhav Shah, Leonilde Varela, Caetano Monteiro & Goran Putnik

  5. MEtRICs Research Center, Universidade do Minho, Guimarães, Portugal

    José Machado

Authors
  1. Josilene Lima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vaibhav Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leonilde Varela
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Caetano Monteiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Goran Putnik
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. José Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Josilene Lima .

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

Lima, J., Shah, V., Varela, L., Monteiro, C., Putnik, G., Machado, J. (2022). Analysing Impact of the Digitalization on Visual Inspection Process in Smartphone Manufacturing by Using Computer Vision. 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_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-00218-2_11

  • 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