The predictive algorithms applied to the quality area allow to minimize the production of defected items, to real time and in advance alert operators and quality users about possible defects and their causes.
The results of the quality tests, automatically and in real time collected from the devices, are transferred to the AI system (Neural Network), which will process them and make predictions about future values, with the aim of preventing in advance processes out of control, based on the nominal and tolerance range values set on the test cycles of the product.
- Definition of the predictive quality model (test cycles, nominal value and range of tolerance)
- Device connection and real time acquisition of quality test results
- Data elaboration from AI system
- Real time visualization of quality test results and “predictions” about future ones
- Real time alerts to notify possible values out of control, process deviations and anomalies
- Minimize scraps, defects and reworks
- Increase machine productivity and efficiency, avoiding machine downtimes due to quality issues
- Improve quality product and customer satisfaction