Zero-Defect Manufacturing in the Industry 4.0 Era: A Hybrid Approach to Detecting Quality Shifts

Rafael Kovalechyn; Pablo Deivid Valle; Dalberto Dias Da Costa

doi:10.5935/jetia.v11i56.2736

Autores

Rafael Kovalechyn Thyssenkrupp Steering Division, Curitiba, Brazil https://orcid.org/0009-0009-1798-3051 (não autenticado)
Pablo Deivid Valle Mechanical Engineering Department, Federal University of Paraná, P.O BOX 19011, Curitiba, Brazil https://orcid.org/0000-0002-8709-2969 (não autenticado)
Dalberto Dias Da Costa Mechanical Engineering Department, Federal University of Paraná, P.O BOX 19011, Curitiba, Brazil https://orcid.org/0000-0002-5665-579X (não autenticado)

DOI:

https://doi.org/10.5935/jetia.v11i56.2736

Resumo

Thanks to the emergence of the Industry 4.0 paradigm, the idea of Zero-Defect Manufacturing (ZDM) has now been claimed to be possible. The ZDM is based on four strategies: detecting, repairing, predicting, and preventing. The main objective of the present work is to defend the proposition that detecting small changes in product quality is a necessary condition to prevent the generation of non-conformance and consequently implement the zero-defect concept. The traditional statistical control charts - such as the Shewhart, EWMA, and CUSUM - and a new method known as ‘concept drift’ were assessed as candidates to build a Decision Support System (DSS). A prototype of the designed DSS was applied to detect quality changes of tubular steering input shafts produced by end-forming. The combination of a concept drift algorithm with a Shewhart chart presented a better performance when compared to the individual behaviour of the statistical control charts.