METHOD OF APPLICATION OF INDUSTRY 4.0 IN AUTOMOTIVE ASSEMBLY LINES
Resumen
The present work has the objective of proposing a method for applying the concepts of industry 4.0 in automotive assembly lines. In methodological terms, a bibliometric analysis was conducted in order to build a bibliographic portfolio with a high impact factor on the subject. Then, the systemic analysis of such portfolio was carried out, extracting the main key technologies of industry 4.0 for the automotive sector, which are: internet of things, machine-learning systems, edge computing, digital twins, big data, cloud manufacturing and cyber-physical systems in manufacturing. As a research result, it was obtained a five step methodology for the application of industry 4.0 in automotive assembly companies. These steps are: search for industry 4.0 know-how, definition of goals and targets, training of employees, application of industry 4.0 key technologies and evaluation of results / feedback.Descargas
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Referencias
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WANG, M. et al. Machine Learning for Networking: Workflow, Advances and Opportunities. Ieee Network. 2017, 32(2), 92-99.
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WU, D. et al. Cloud manufacturing: Strategic vision and state-of-the-art. Journal Of Manufacturing Systems. 2013, 32(4), 564-579.
WU, D. et al. A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing. Journal Of Manufacturing Systems. 2017, 53(1), 25-34.
XU, L.; HE, W.; LI, S.. Internet of Things in Industries: A Survey. Ieee Transactions On Industrial Informatics. 2014, 10(4), 2233-2243.
XU, X.. From cloud computing to cloud manufacturing. Robotics And Computer-integrated Manufacturing. 2012, 28(1), 75-86.
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ANDRADE JÚNIOR, P. P.. Método RIA: seleção de artigos para formação de um portfólio bibliográfico a partir de periódicos de alto fator de impacto. In press.
BI, Z.; XU, L.; WANG, C.. Internet of Things for Enterprise Systems of Modern Manufacturing. Ieee Transactions on Industrial Informatics. 2014, 10(2), 1537-1546.
BISIO, I. et al. Exploiting Context-Aware Capabilities over the Internet of Things for Industry 4.0 Applications. Ieee Network. 2018, 32(3), 108-115.
CHEN, S. et al. A Vision of IoT: Applications, Challenges, and Opportunities With China Perspective. Ieee Internet Of Things Journal. 2014, 1(4), 349-359.
CHERUBINI, A. et al. Collaborative manufacturing with physical human–robot interaction. Robotics And Computer-integrated Manufacturing. 2016, 40, 1-13.
CHIANG, M.; ZHANG, T.. Fog and IoT: An Overview of Research Opportunities. Ieee Internet Of Things Journal. 2016, 3(6), 854-864.
ESMAEILIANA, B.; BEHDAD, S.; WANG, B.. The evolution and future of manufacturing: A review. Journal Of Manufacturing Systems. 2016, 39, 79-100.
FERRÀS-HERNÁNDEZ, X.; TARRATS-PONS, E.; ARIMANY-SERRAT, N.. Disruption in the automotive industry: A Cambrian moment. Business Horizons. 2017, 60(6), 855-863.
FISHER, O. et al. Cloud manufacturing as a sustainable process manufacturing route. Journal Of Manufacturing Systems. 2018, 47, 53-68.
HE, K.; ZHANG, Q.; HONG, Y.. Profile monitoring based quality control method for fused deposition modeling process. Journal Of Intelligent Manufacturing. 2018, 30(2), 1-12.
JESUS, R.A.. Adaptação de um método para adoção de requisitos do programa industrie 4.0: aplicação em uma montadora de veículos. Master degree, Universidade Metodista de Piracicaba, Santa Bárbara D´oeste, 2017.
KAMBLE, S. S.; GUNASEKARAN, A.A; SHARMA, R.. Analysis of the driving and dependence power of barriers to adopt industry 4.0 in Indian manufacturing industry. Computers in Industry. 2018, 101, 107-119.
LI, H.; OTA, K.; DONG, M.. Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing. Ieee Network. 2018, 32(1), 96-101.
LIN, J. et al. A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications. Ieee Internet Of Things Journal. 2017, 4(5), 1125-1142.
MINOLI, D.; SOHRABY, K.; OCCHIOGROSSO, B.. IoT Considerations, Requirements, and Architectures for Smart Buildings—Energy Optimization and Next-Generation Building Management Systems. Ieee Internet Of Things Journal. 2017, 4(1), 269-283.
MONOSTORI, L. et al. Cyber-physical systems in manufacturing. Manufacturing Technology. 2016, 65(2), 621-641.
PANDEY, A.; PRADHAN, S. K.. Investigations into Complete Liquefier Dynamics and Optimization of Process Parameters for Fused Deposition Modeling. Materials Today. 2018, 5(5), 12940-12955.
PREMSANKAR, G.; FRANCESCO, M.; TALEB, T.. Edge Computing for the Internet of Things: A Case Study. Ieee Internet Of Things Journal. 2018, 5(2), 1275-1284.
PwC. Industry 4.0: digitalization as a competitive advantage in Brazil. 2016. Available online: https://www.pwc.com.br/pt/publicacoes/servicos/assets/consultoria-negocios/2016/pwc-industry-4-survey-16.pdf (acessed on 01/07/2018).
SCHLUSE, M.L. et al. Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0. Ieee Transactions On Industrial Informatics. 2018, 14(4), 1722-1731.
SHARP, M.; AK, R.; HEDBERG JUNIOR, T.. A survey of the advancing use and development of machine learning in smart manufacturing. Journal Of Manufacturing Systems. 2018, 48(C), 115-136.
SHI, W. et al. Edge Computing: Vision and Challenges. Ieee Internet Of Things Journal. 2016, 3(5), 637-646.
SUSTO, G. A. et al. Machine Learning for Predictive Maintenance: A Multiple Classifier Approach. Ieee Transactions On Industrial Informatics. 2014, 11(3), 812-820.
TAO, F. et al. Data-driven smart manufacturing. Journal Of Manufacturing Systems. 2018, 48(C), 130-139.
WAN, J. et al. A Manufacturing Big Data Solution for Active Preventive Maintenance. IEEE Transactions On Industrial Informatics. 2017, 13(4), 18-27.
WANG, J. et al. Deep learning for smart manufacturing: Methods and applications. Journal Of Manufacturing Systems. 2018, 48(C), 128-132.
WANG, T. et al. Data-driven prognostic method based on self-supervised learning approaches for fault detection. Journal Of Intelligent Manufacturing. 2018, p. 26-38.
WANG, M. et al. Machine Learning for Networking: Workflow, Advances and Opportunities. Ieee Network. 2017, 32(2), 92-99.
WANG, L.; TÖRNGREN, M.; ONORI, M.. Current status and advancement of cyber-physical systems in manufacturing. Journal Of Manufacturing Systems. 2015, 37(2), 517-527.
WU, D. et al. Cloud manufacturing: Strategic vision and state-of-the-art. Journal Of Manufacturing Systems. 2013, 32(4), 564-579.
WU, D. et al. A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing. Journal Of Manufacturing Systems. 2017, 53(1), 25-34.
XU, L.; HE, W.; LI, S.. Internet of Things in Industries: A Survey. Ieee Transactions On Industrial Informatics. 2014, 10(4), 2233-2243.
XU, X.. From cloud computing to cloud manufacturing. Robotics And Computer-integrated Manufacturing. 2012, 28(1), 75-86.
ZHANG, J. et al. Automatic assembly simulation of product in virtual environment based on interaction feature pair. Journal Of Intelligent Manufacturing. 2018, 29(6), 1235-1256.
ZHONG, R.Y. et al. Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering. 2017, 3(5), 616-630
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Publicado
2019-07-02
Número
Sección
INGENIERÍA DE OPERACIONES Y PROCESOS DE PRODUCCIÓN
Licencia
Derechos de autor 2019 Brazilian Journal of Production Engineering - BJPE
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Cómo citar
Endler, C. L., & Andrade Júnior, P. P. de. (2019). METHOD OF APPLICATION OF INDUSTRY 4.0 IN AUTOMOTIVE ASSEMBLY LINES. Brazilian Journal of Production Engineering, 5(3), 104-119. https://periodicos.ufes.br/bjpe/article/view/V05N03_008
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