Manufacturing processes leave on the machined surface a specific “fingerprint” of the process used, which can be usefully adopted to improve the quality control strategy (i.e., to reduce the time required to detect out-of-control conditions). Approaches proposed up to now in the literature are mainly devoted to monitoring simple signatures, where data measured on the profile are not autocorrelated. Unfortunately, most of the times data collected on a machined profile are autocorrelated because they are obtained in similar condition of the machining process and because they are related to local properties of the material machined. This paper presents a novel method for monitoring bi-dimensional profiles when the autocorrelation structure is modeled as a part of the manufacturing signature. The proposed method is based on combining a regression model with autocorrelated errors to a multivariate Hotelling T2 control chart and it is applied to real process data in which the roundness of items obtained by turning has to be monitored. A simulation study indicates that the proposed approach outperforms competing method (based on monitoring the out-of-roundness value for each profile) in terms of the average number of samples required to detect out-of-control conditions. http://www.amstat-online.org/sections/qp/qprc/2006/JRC%20Presentations/Presentation_JRC_QPRC2006_Colosimo.pdf
Quality control of geometric features: monitoring roundness profiles obtained by turning
PACELLA, Massimo;
2006-01-01
Abstract
Manufacturing processes leave on the machined surface a specific “fingerprint” of the process used, which can be usefully adopted to improve the quality control strategy (i.e., to reduce the time required to detect out-of-control conditions). Approaches proposed up to now in the literature are mainly devoted to monitoring simple signatures, where data measured on the profile are not autocorrelated. Unfortunately, most of the times data collected on a machined profile are autocorrelated because they are obtained in similar condition of the machining process and because they are related to local properties of the material machined. This paper presents a novel method for monitoring bi-dimensional profiles when the autocorrelation structure is modeled as a part of the manufacturing signature. The proposed method is based on combining a regression model with autocorrelated errors to a multivariate Hotelling T2 control chart and it is applied to real process data in which the roundness of items obtained by turning has to be monitored. A simulation study indicates that the proposed approach outperforms competing method (based on monitoring the out-of-roundness value for each profile) in terms of the average number of samples required to detect out-of-control conditions. http://www.amstat-online.org/sections/qp/qprc/2006/JRC%20Presentations/Presentation_JRC_QPRC2006_Colosimo.pdfI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
