A methodology for data-driven adjustment of variation propagation models in multistage manufacturing processes
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Altres documents de l'autoria: Moliner-Heredia, Rubén; Peñarrocha-Alós, Ignacio; Abellán-Nebot, José V.
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Títol
A methodology for data-driven adjustment of variation propagation models in multistage manufacturing processesData de publicació
2023Editor
ElsevierCita bibliogràfica
MOLINER-HEREDIA, Rubén; PEÑARROCHA-ALÓS, Ignacio; ABELLÁN-NEBOT, José Vicente. A methodology for data-driven adjustment of variation propagation models in multistage manufacturing processes. Journal of Manufacturing Systems, 2023, vol. 67, p. 281-295.Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://www.sciencedirect.com/science/article/pii/S0278612523000298Versió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
In the current paradigm of Zero Defect Manufacturing, it is essential to obtain mathematical models that
express the propagation of manufacturing deviations along Multistage Manufacturing Processes (MMPs). Linear
... [+]
In the current paradigm of Zero Defect Manufacturing, it is essential to obtain mathematical models that
express the propagation of manufacturing deviations along Multistage Manufacturing Processes (MMPs). Linear
physical-based models such as the Stream of Variation (SoV) model are commonly used, but its accuracy may
be limited when applied to MMPs with a large amount of stages, mainly because of the modeling errors at
each stage that are accumulated downstream.
In this paper we propose a methodology to calibrate the SoV model using data from the inspection stations
and prior engineering-based knowledge. The data used for calibration does not contain information about the
sources of variation, and they must be estimated as part of the model adjustment procedure. The proposed
methodology consists of a recursive algorithm that minimizes the difference between the sample covariance of
the measured Key Product Characteristic (KPC) deviations and its estimation, which is a function of a variation
propagation matrix and the covariance of the deviation of the variation sources. To solve the problem with
standard convex optimization tools, Schur complements and Taylor series linearizations are applied. The output
of the algorithm is an adjusted model, which consists of a variation propagation matrix and an estimation of
the aforementioned variation source covariance.
In order to validate the performance of the algorithm, a simulated case study is analyzed. The results, based
on Monte Carlo simulations, show that the estimation errors of the KPC deviation covariances are proportional
to the measurement noise variance and inversely proportional to the number of processed parts that have been
used to train the algorithm, similarly to other process estimators in the literature. [-]
Publicat a
Journal of Manufacturing Systems 67 (2023)Entitat finançadora
Generalitat Valenciana | Fondo Social Europeo | Universitat Jaume I
Codi del projecte o subvenció
ACIF/2018/245 | UJI-B2020-33
Drets d'accés
info:eu-repo/semantics/openAccess
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