Aluminum foundry

Integration and development of an advanced analytics platform

Starting point

  • Aluminum wheels are produced using the low-pressure die casting process, and after casting, they undergo an X-ray inspection
  • During the casting process, around 130 sensor data points are recorded at 300 timestamps per wheel produced
  • Currently, the sensor data is not used for error analysis or for the earliest possible removal of defective products
 

Procedure

  • Explorative analysis and visualization of sensor data
  • Transformation and statistical analysis of sensor and casting performance data
  • Development of multiple use cases and scenarios for testing on a casting machine
  • Designing a hybrid data architecture for persisting sensor data
  • Developing a dashboard for visualizing various key metrics

 

Features/Project outcome

  • Tableau dashboard for visualizing various key metrics and evaluations
  • R/Rshiny analytics platform
  • Big data integration
  • Evaluation of different error models
  • Customer-specific training offerings
  • Independent analysis and visualization of results by the specialist departments using Tableau

Customer benefits

  • Reduction of defective wheels through new opportunities to optimize the casting process
  • Reduction of inspection costs through error prediction immediately after the casting process
  • Visualisation and analysis of casting performance for casting machines, wheel types, and production shifts
  • Daily retrospective of casting performance for further root cause analysis of errors
  • Data discovery to identify which sensors are relevant for production errors
 
Erich Holzinger | Senior Manager / Authorised Officer