Mould wear-out prediction in the plastic injection moulding industry: a case study

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.

KEYWORDS:

• Industry 4.0
• predictive maintenance
• injection moulding
• mixed data
• reliability analysis
• censored data
• mould wear-out

Acknowledgments

This work has been carried out under MADE SPIR – Strategic Platform for Innovation and Research, Denmark and the authors are grateful for the given opportunity. The authors would like to thank Max Peter Spooner for proofreading the article and the industrial partner for providing the data sets and for the allocated time.

Disclosure statement

The authors of the article have not encountered any potential conflict of interest.

Additional information

Funding

This work was supported by the Manufacturing Academy of Denmark.