TY - GEN
T1 - Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds
AU - Böttjer, Till
AU - Ørnskov Rønsch, Georg
AU - Gomes, Cláudio
AU - Ramanujan, Devarajan
AU - Iosifidis, Alexandros
AU - Larsen, Peter Gorm
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Data-driven model
KW - Injection moulding
KW - Machine learning
KW - Predictive maintenance
KW - Smart manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85119434371&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-90700-6_49
DO - 10.1007/978-3-030-90700-6_49
M3 - Article in proceedings
SN - 978-3-030-90699-3
T3 - Lecture Notes in Mechanical Engineering
SP - 431
EP - 439
BT - Towards Sustainable Customization
A2 - Andersen, Ann-Louise
A2 - Andersen, Rasmus
A2 - Brunoe, Thomas Ditlev
A2 - Stoettrup Schioenning Larsen, Maria
A2 - Nielsen, Kjeld
A2 - Napoleone, Alessia
A2 - Kjeldgaard, Stefan
PB - Springer
T2 - 8th Changeable, Agile, Reconfigurable and Virtual Production Conference, CARV 2021 and 10th World Mass Customization and Personalization Conference, MCPC 2021
Y2 - 1 November 2021 through 2 November 2021
ER -