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Crack detection for reliable quality in production

With the crack detection system PROgress keep track of your production process at all times - from monitoring and control to optimization.

 

The system works on the basis of structure-borne sound analysis. The reliable detection of cracks and defects can be carried out using both classic algorithms and the latest AI methods.

 

You follow everything in real time. PROgress also offers valuable overviews in the form of statistics and dashboards with current and historical measurement and process data.

The advantages of the PROgress

  • Inline and real-time crack detection in the running process without additional steps

  • Multidimensional crack detection algorithms

  • Self-optimizing, intelligent crack detection parameters

  • Process optimization based on crack data and AI

  • Transparent decision-making process

  • Understandable, physically based functionality

  • simple and clear operation

  • open interfaces and data formats

  • low-maintenance and safe thanks to automated self-tests

Consistently best possible quality

less

downtime

Scrap reduction

Optimization of production parameters

The PROgress Software packet

Live Software

  • records structure-borne noise from the production process 24/7

  • communicates with machine via IOs

  • evaluates signals and outputs status (OK/NOK).

  • Operation via touchscreen, web & desktop possible

  • two user levels (operator and admin)

  • stores features and ratings in Postgre's database

  • stores raw data locally or in DB

  • loads rating recipes from database

  • manual and automatic self-tests

Analysis software

Analyse_Risserkennung
  • Presentation of the production data

  • Display of the raw data including transients

  • Display of the spectrograms

  • Presentation of the rating features in histograms

  • Selection of suitable evaluation limits (manually or as an automatic suggestion)

  • Export of rating settings to Postgres

  • Testing of any valuation settings on historical data

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