3 Applications of AI in System Calibration Testing 

Calibration is one of the most resource heavy tasks in engineering. Today, teams spend hours on expensive test rigs, running long cycles to capture every condition, and manually generating lookups to define system behaviour. The result is wasted capacity, bottlenecks around equipment availability, and inconsistent outputs between programmes. 

At the same time, engineers are expected to deliver and get products to market faster. Traditional methods cannot keep up. Artificial intelligence offers a practical alternative by learning directly from existing test data. With machine learning, engineers can identify high value tests, generate calibration maps, and replace costly sensors with accurate virtual equivalents. The impact is less time spent in the lab, faster decisions, and greater confidence in calibration results.

3 Core Applications of AI in Calibration 

Virtual Sensor Modelling 

Replace costly hardware with AI driven virtual sensors. Achieve ±6% accuracy across operating ranges using twenty times less test data. 

Test Cycle Design Optimisation 

Use Monolith’s Next Test Recommender to prioritise only the most valuable drive cycles. Accelerate testing by a factor of twenty while still capturing critical behaviours. 

Calibration Map Prediction 

Generate accurate first pass maps with less data and fewer manual iterations. Standardise calibration outputs across platforms and programmes. 

What Monolith Brings to the Table

Monolith has partnered with leading OEMs and suppliers to apply AI across calibration programmes, from building virtual sensors to cutting test cycles by a factor of twenty. In nearly every case, what begins as a single project quickly expands into a broader strategy. New business units adopt the same methods, workflows become standardised, and efficiency compounds across the organisation.