Fault region localization (FRL): collaborative product and process improvement based on field performance

Kamal Mannar; Darek Ceglarek

doi:10.1117/12.631169

17 November 2005 Fault region localization (FRL): collaborative product and process improvement based on field performance

Kamal Mannar, Darek Ceglarek

Author Affiliations +

Proceedings Volume 5999, Intelligent Systems in Design and Manufacturing VI; 59990P (2005) https://doi.org/10.1117/12.631169
Event: Optics East 2005, 2005, Boston, MA, United States

Abstract

Customer feedback in the form of warranty/field performance is an important and direct indicator of quality and robustness of a product. Linking warranty information to manufacturing measurements can identify key design parameters and process variables (DPs and PVs) that are related to warranty failures. Warranty data has been traditionally used in reliability studies to determine failure distributions and warranty cost. This paper proposes a novel Fault Region Localization (FRL) methodology to map warranty failures to manufacturing measurements (hence to DPs/PVs) to diagnose warranty failures and perform tolerance revaluation. The FRL methodology consists of two parts: 1. Identifying relations between warranty failures and DPs and PVs using the Generalized Rough Set (GRS) method. GRS is a supervised learning technique to identify specific DPs and PVs related to the given warranty failures and then determining the corresponding Warranty Fault Regions (WFR), Normal Region (NR) and Boundary region (BND). GRS expands traditional Rough Set method by allowing inclusion of noise and uncertainty of warranty data classes. 2. Revaluating the original tolerances of DPs/PVs based on the WFR and BND region identified. The FRL methodology is illustrated using case studies based on two warranty failures from the electronics industry.

Citation Download Citation

Kamal Mannar and Darek Ceglarek "Fault region localization (FRL): collaborative product and process improvement based on field performance", Proc. SPIE 5999, Intelligent Systems in Design and Manufacturing VI, 59990P (17 November 2005); https://doi.org/10.1117/12.631169

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