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2022 | OriginalPaper | Chapter

Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds

Authors : Till Böttjer, Georg Ørnskov Rønsch, Cláudio Gomes, Devarajan Ramanujan, Alexandros Iosifidis, Peter Gorm Larsen

Published in: Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems

Publisher: Springer International Publishing

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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.

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Metadata
Title
Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds
Authors
Till Böttjer
Georg Ørnskov Rønsch
Cláudio Gomes
Devarajan Ramanujan
Alexandros Iosifidis
Peter Gorm Larsen
Copyright Year
2022
DOI
https://doi.org/10.1007/978-3-030-90700-6_49

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