ComoNeoPREDICT, the online quality prediction system, provides users with reliable statements in advance about each manufactured component, based on the current cavity pressure profile. With this information, they can improve machine settings so as to maximize quality, production time and process stability.
ComoNeoPREDICT is based on models that make it possible to calculate the characteristics of molded parts. The online quality prediction system uses the statistical DoE (Design of Experiments) process for test planning to determine relationships between pressure / temperature profiles and defined quality features. To implement prediction, all you need in addition to the machine and the part are Kistler's ComoNeo monitoring system and the software that generates the DoE and the prediction model.
Benefits
An exact method for separating scrap – thanks to complete quality records
Direct monitoring of predicted quality features – much easier for users to understand
Pseudoscrap is minimized
Simple handling of tolerances as scrap criteria (transferred from the part drawing)
Automatic selection of relevant curve points for calculation
Can be used even without in-depth mathematical or statistical knowledge
Models can easily be retrained for further improvement
ComoNeoPREDICT, the online quality prediction system, provides users with reliable statements in advance about each manufactured component, based on the current cavity pressure profile. With this information, they can improve machine settings so as to maximize quality, production time and process stability.
ComoNeoPREDICT is based on models that make it possible to calculate the characteristics of molded parts. The online quality prediction system uses the statistical DoE (Design of Experiments) process for test planning to determine relationships between pressure / temperature profiles and defined quality features. To implement prediction, all you need in addition to the machine and the part are Kistler's ComoNeo monitoring system and the software that generates the DoE and the prediction model.
Benefits
- An exact method for separating scrap – thanks to complete quality records
- Direct monitoring of predicted quality features – much easier for users to understand
- Pseudoscrap is minimized
- Simple handling of tolerances as scrap criteria (transferred from the part drawing)
- Automatic selection of relevant curve points for calculation
- Can be used even without in-depth mathematical or statistical knowledge
- Models can easily be retrained for further improvement
