The traditional use of control charts assumes the independence of data. It is widely recognized that many processes are autocorrelated thus violating the main assumption of independence. As a result, there is a need for a broader approach to quality monitoring when data are time-dependent or autocorrelated. The aim of this work is to present a new procedure for manufacturing process quality control in the case of serially correlated data. In particular, a recurrent neural network is introduced for quality control problem. Performance comparisons between the neural-based algorithm and control charts are also presented in the paper in order to validate the proposed approach. The simulation results indicate that the neural-based procedure is quite effective as it achieves improved performance over control charts.

Detecting Changes in Autoregressive Processes with a Recurrent Neural Network for Manufacturing Quality Monitoring

Pacella, M.;Semeraro, Q.;
2005-01-01

Abstract

The traditional use of control charts assumes the independence of data. It is widely recognized that many processes are autocorrelated thus violating the main assumption of independence. As a result, there is a need for a broader approach to quality monitoring when data are time-dependent or autocorrelated. The aim of this work is to present a new procedure for manufacturing process quality control in the case of serially correlated data. In particular, a recurrent neural network is introduced for quality control problem. Performance comparisons between the neural-based algorithm and control charts are also presented in the paper in order to validate the proposed approach. The simulation results indicate that the neural-based procedure is quite effective as it achieves improved performance over control charts.
2005
Proceedings of AMST’05 Advanced Manufacturing Systems and Technology
978-3-211-26537-6
ARMA Models; Manufacturing; Neural Networks; Mechanical Engineering; Mechanics of Materials; Computer Science Applications1707 Computer Vision and Pattern Recognition; Modeling and Simulation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1065209
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