Abstract
One of the first metrics of production quality is the FTQ (First Time Quality) index, which has gained new attention with process control methods driven by Industry 4.0. A manufacturing line with an FTQ below 100% causes the discarding of produced units or rework time, implying higher production costs and negative impacts on quality goals. To guide quality actions, mainly in processes with several incident factors, there is a need for a lower granularity target that allows the definition of correction actions still during production. This paper presents a study on quality indicators that can better represent the performance regarding the occurrence of defective units in the final test process of the equipment assembled by Ingeteam Brasil. The methodological guideline is to assess the effectiveness of using some quality indicators for short- (weekly) and medium-term (monthly) monitoring of the performance of the mentioned process, aiming to identify correlations between them and support faster decisions to reach the desired quality targets. In this sense, a short-term upper control limit is derived for each product based on its behavior during the test process to identify units and failure causes that may compromise the FTQ goal and should be analyzed. To this end a software quality test tool was developed, the Quality Test System (QTS), which records important information about the test process such as required test stations, operators, product serial number, test steps, detected failures, etc. QTS is integrated with the shop floor system integrating production and testing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guelbert, M.: Strategy of process and quality management. IESDE Brazil, Curitiba (2012)
Rogošić, A.: Quality cost reporting as a determinant of quality costing maturity. Int. J. Qual. Res. 15 (2021)
Crosby, P.B.: Quality is Free: The Art of Making Quality Certain, 1st edn. McGraw-Hill, New York (1979)
Midor, K.: Use of Six Sigma in the production process-case study. Syst. Wspomagania w Inżynierii Prod. 6 (2017)
Gallo, T., Cagnetti, C., Silvestri, C., Ruggieri, A.: Industry 4.0 tools in lean production: a systematic literature review. Procedia Comput. Sci. 180, 394–403 (2021)
Antony, J., McDermott, O., Powell, D.J., Sony, M.: Mapping the terrain for Lean Six Sigma 4.0. In: Powell, D.J., Alfnes, E., Holmemo, M.D.Q., Reke, E. (eds.) Learning in the Digital Era. IFIP Advances in Information and Communication Technology, vol. 610, pp. 193–204. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-92934-3_20
Ou, X., Huang, J., Chang, Q., Hucker, S., Lovasz, J.G.: First time quality diagnostics and improvement through data analysis: a study of a crankshaft line. Procedia Manuf. 49, 2–8 (2020)
Park, Y.-J., Fan, S.-K.S., Hsu, C.-Y.: A review on fault detection and process diagnostics in industrial processes. Processes 8, 1123 (2020)
Kibira, D., Morris, K.C., Kumaraguru, S.: Methods and tools for performance assurance of smart manufacturing systems. J. Res. Natl. Inst. Stand. Technol. 121, 282 (2016)
Vaz, G.C., Iano, Y., de Oliveira, G.G.: IoT - from industries to houses: an overview. In: Iano, Y., Saotome, O., Kemper Vásquez, G.L., Cotrim Pezzuto, C., Arthur, R., Gomes de Oliveira, G. (eds.) Proceedings of the 7th Brazilian Technology Symposium (BTSym 2021). Smart Innovation, Systems and Technologies, vol. 295, pp. 734–741. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08545-1_73
Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N., Yin, K.: A review of process fault detection and diagnosis: Part III: Process history based methods. Comput. Chem. Eng. 27, 327–346 (2003)
Laouti, N., Sheibat-Othman, N., Othman, S.: Support vector machines for fault detection in wind turbines. IFAC Proc. 44, 7067–7072 (2011)
Holanda, G., de Souza, J.M., Adorni, C.Y.K.O., de Nader, M.V.P.: Tacit knowledge and a multi-method approach in asset management. Logeion Filos. da Informação 8, 197–212 (2022)
Montgomery, D.C., Runger, G.C.: Applied Statistics and Probability for Engineers. Wiley, New York (1999)
de Souza, J.M., de Holanda, G.M., Henriques, H.A., Furukawa, R.H.: Modified control charts monitoring long-term semiconductor manufacturing processes. In: Iano, Y., Saotome, O., Kemper, G., Mendes de Seixas, ACl., Gomes de Oliveira, G. (eds.) Proceedings of the 6th Brazilian Technology Symposium (BTSym’20). Smart Innovation, Systems and Technologies, vol. 233, pp. 80–87. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75680-2_11
Carpinetti, L.C.R.: Gestão da qualidade. EDa Atlas SA (2012)
Guarnieri, J.P.: Eficiência dos gráficos de controle na detecção de outliers em processos autorregressivos e de médias móveis (2010)
Carmona, A.R.R.: Application of statistical process control in an automotive industry. Dissertation (Master), Faculty of Science and Technology, New University of Lisbon (2017)
Russo, S., Rodrigues, P.M.M.: Metodologia alternativa às técnicas de gráficos de controle. In: Proceedings of the XXXVII Simpósio Brasileiro de Pesquisa Operacional - SBPO, Gramado – RS (2005)
Ferreira, A.P.D.: Gerenciamento de falhas baseada em evidências: proposta de método e protótipo computacional. Thesis (Doctoral), UNISINOS (2020)
Acknowledgments
The authors thank the Ministry of Science, Technology, and Innovation for the financial support to this R&D project through the PPB (Basic Productive Process).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Homem, M.O., Bettanin, F.R., de Souza, J.M., de Holanda, G.M., Cristófani, F. (2023). Fault Management in Manufacturing Process: Quality Indicators for Short- and Medium-Term Monitoring and Their Interrelationship. In: Iano, Y., Saotome, O., Kemper Vásquez, G.L., de Moraes Gomes Rosa, M.T., Arthur, R., Gomes de Oliveira, G. (eds) Proceedings of the 8th Brazilian Technology Symposium (BTSym’22). BTSym 2022. Smart Innovation, Systems and Technologies, vol 353. Springer, Cham. https://doi.org/10.1007/978-3-031-31007-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-31007-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-31006-5
Online ISBN: 978-3-031-31007-2
eBook Packages: EngineeringEngineering (R0)