Cycle-based tool replacement is very costly in terms of downtime it causes, tooling waste and, on many occasions, unnecessary replacement of perfectly good tooling.
Condition-based tool replacement using data and machine learning to inform decisions. Leading to a reduction in cost and extending the life of the tools.
- Investigate drilling data and identify the parameters that indicate good and bad performance
- Develop condition based monitoring of hole quality
- Develop predictive monitoring to know when hole quality will fall below standard
- Develop anomaly detection focusing on abnormal behaviours during the drilling process
- Deploy alerting system to indicate when degradation of hole quality starts to occur