Automobile manufacturer

Process transparency through analytical visualization of additive manufacturing

Starting point

  • Different sensors monitor each individual layer of the Laser Powder Bed Fusion 3D manufacturing process
  • The sensor data is available in various formats and resolutions
  • Based on this data, efficient real-time analysis and quality monitoring are not possible
  • Without analytical processing of the data, detecting issues and errors during the ongoing process is not feasible
  • AI methods for optimization cannot be integrated

Procedure

  • Requirement engineering and design of an efficient solution
  • Definition of a cloud solution and the tool stack
  • Design of an interactive dashboard
  • Definition of agile user stories for implementation
  • Development of real-time algorithms and data processing
  • 3D & 2D visualization of sensor data in Python Dash
  • Persistence of sensor data on Amazon S3

Features/Project outcome

  • Intuitive visualisation of consistently processed sensor data
  • 'Near real-time' validation of production results based on statistical analysis
  • Processing, adjusting, and persisting sensor data
  • Visualisation of layer-wise inconsistencies based on data science model predictions
  • Integration of AI models into the visualizsation

Customer benefits

  • Transparency of production results through advanced analytics and visualization of sensor data
  • 'Near real-time' quality control for additive manufacturing
  • Persisting sensor data for long-term optimisation of production processes and analysis of production outcomes
  • AI-driven prediction of production quality
Erich Holzinger | Senior Manager / Authorised Officer