A methodology for data-driven adjustment of variation propagation models in multistage manufacturing processes
نویسندگان
چکیده
In the current paradigm of Zero Defect Manufacturing, it is essential to obtain mathematical models that express propagation manufacturing deviations along Multistage Manufacturing Processes (MMPs). Linear physical-based such as Stream Variation (SoV) model are commonly used, but its accuracy may be limited when applied MMPs with a large amount stages, mainly because modeling errors at each stage accumulated downstream. this paper we propose methodology calibrate SoV using data from inspection stations and prior engineering-based knowledge. The used for calibration does not contain information about sources variation, they must estimated part adjustment procedure. proposed consists recursive algorithm minimizes difference between sample covariance measured Key Product Characteristic (KPC) estimation, which function variation matrix deviation sources. To solve problem standard convex optimization tools, Schur complements Taylor series linearizations applied. output an adjusted model, estimation aforementioned source covariance. order validate performance algorithm, simulated case study analyzed. results, based on Monte Carlo simulations, show KPC covariances proportional measurement noise variance inversely number processed parts have been train similarly other process estimators in literature.
منابع مشابه
Variability monitoring of multistage manufacturing processes using regression adjustment methods
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ژورنال
عنوان ژورنال: Journal of Manufacturing Systems
سال: 2023
ISSN: ['1878-6642', '0278-6125']
DOI: https://doi.org/10.1016/j.jmsy.2023.02.005