Tool Life Optimisation


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.

The How

  1. Investigate drilling data and identify the parameters that indicate good and bad performance
  2. Develop condition based monitoring of hole quality
  3. Develop predictive monitoring to know when hole quality will fall below standard
  4. Develop anomaly detection focusing on abnormal behaviours during the drilling process
  5. Deploy alerting system to indicate when degradation of hole quality starts to occur