SCOPUS eid=2-s2.0-39449124359 - Quality of mechanical components is critically related to both dimensional and geometric specifications. Traditionally, approaches for statistical process control (SPC) focused on the first type of specification only. When the quality of a manufactured product is related to geometric specifications (e.g., profile and form tolerances as straightness, roundness, cylindricity, flatness, etc.), the process should be considered in control if the relationship used to represent that profile or surface in the space is stable over time. This paper presents a novel method for monitoring bidimensional profiles. The proposed method is based on combining a spatial autoregressive regression (SARX) model (i.e., a regression model with spatial autoregressive errors) with control charting. To show the effectiveness of the proposed method, the approach 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 methods (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 arising in phase II and due to spindle-motion errors.
Statistical process control for geometric specifications: On the monitoring of roundness profiles
PACELLA, Massimo
2008-01-01
Abstract
SCOPUS eid=2-s2.0-39449124359 - Quality of mechanical components is critically related to both dimensional and geometric specifications. Traditionally, approaches for statistical process control (SPC) focused on the first type of specification only. When the quality of a manufactured product is related to geometric specifications (e.g., profile and form tolerances as straightness, roundness, cylindricity, flatness, etc.), the process should be considered in control if the relationship used to represent that profile or surface in the space is stable over time. This paper presents a novel method for monitoring bidimensional profiles. The proposed method is based on combining a spatial autoregressive regression (SARX) model (i.e., a regression model with spatial autoregressive errors) with control charting. To show the effectiveness of the proposed method, the approach 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 methods (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 arising in phase II and due to spindle-motion errors.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
