Abstract
The current work addresses an industrial problem related to injection moulding manufacturing with focus on mould wear-out prediction. Real data sets are provided by an industrial partner that uses a multitude of moulds with different shapes and sizes in its production. An analysis of the data is presented and begins with clustering the moulds based on their characteristics and pre-chosen running settings. Using the results of the clustering, the mould wear-out is modelled using Kaplan-Meier survival curves. Furthermore, a random survival forest model is fitted for comparison and model performance is assessed. The main novelty of the case study is the implementation of mould wear-out prediction in real-time with the outcomes presented in terms of conditional survival curves including a proposed early warning system. For visualization and further industrial implementation, an R Shiny dashboard is developed and presented.
Original language | English |
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Journal | International Journal of Computer Integrated Manufacturing |
Volume | 33 |
Issue number | 12 |
Pages (from-to) | 1245-1258 |
ISSN | 0951-192X |
DOIs | |
Publication status | Published - 2021 |
Keywords
- Censored data
- Industry 4.0
- Injection moulding
- Mixed data
- Mould wear-out
- Predictive maintenance
- Reliability analysis