Quality of mechanical components is critically related to both dimension and geometric specifications. Traditionally, approaches for statistical process control (SPC) focus on the first type of specification only. When the quality of a manufactured product is related to geometric specifications (e.g., form and profile tolerances as roundness, cilindricity, flatness, etc.), the process should be judged in- control if the relationship used to represent that profile or surface in the space, is stable with time. This paper presents a novel method for monitoring profiles. The proposed method is based on combining the auto regressive moving average with exogenous variables (ARMAX) model with multivariate Hotelling T2 control chart. Fourier analysis is used to describe the exogenous variables in the ARMAX model. To show effectiveness of the proposed method, the approach is applied to a real case in which the roundness of items obtained by turning has to be monitored. A simulation study indicates that the proposed approach outperforms competing method in terms of the average number of samples required to detect an out-of- control related with spindle error motions.
A profile-monitoring method for quality control of manufactured items
PACELLA, Massimo
2005-01-01
Abstract
Quality of mechanical components is critically related to both dimension and geometric specifications. Traditionally, approaches for statistical process control (SPC) focus on the first type of specification only. When the quality of a manufactured product is related to geometric specifications (e.g., form and profile tolerances as roundness, cilindricity, flatness, etc.), the process should be judged in- control if the relationship used to represent that profile or surface in the space, is stable with time. This paper presents a novel method for monitoring profiles. The proposed method is based on combining the auto regressive moving average with exogenous variables (ARMAX) model with multivariate Hotelling T2 control chart. Fourier analysis is used to describe the exogenous variables in the ARMAX model. To show effectiveness of the proposed method, the approach is applied to a real case in which the roundness of items obtained by turning has to be monitored. A simulation study indicates that the proposed approach outperforms competing method in terms of the average number of samples required to detect an out-of- control related with spindle error motions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
