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- W3208488390 abstract "Throughout their useful life, plastic injection moulds operate in rapidly varying cyclic environments, and are prone to continual degradation. Quantifying the remaining useful life of moulds is a necessary step for minimizing unplanned downtime and part scrap, as well as scheduling preventive mould maintenance tasks such as cleaning and refurbishment. This paper presents a data-driven approach for identifying degradation progression and remaining useful life of moulds, using real-world production data. An industrial data set containing metrology measurements of a solidified plastic part, along with corresponding life-cycle data of 13 high production volume injection moulds, was analyzed. Multivariate Statistical Process Control techniques and XGBoost classification models were used for constructing data-driven models of mould degradation progression, and classifying mould state (early run-in, production, worn-out). Results show the XGBoost model developed using element metrology & relevant mould lifecycle data classifies worn-out moulds with an in-class accuracy of 88%. Lower in-class accuracy of 73% and 61% were achieved for the compared to mould-worn out less critical early run-in and production states respectively." @default.
- W3208488390 created "2021-11-08" @default.
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- W3208488390 date "2021-11-01" @default.
- W3208488390 modified "2023-09-26" @default.
- W3208488390 title "Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds" @default.
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- W3208488390 doi "https://doi.org/10.1007/978-3-030-90700-6_49" @default.
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