The global automotive industry is at a pivotal moment, driven by stringent emission regulations and a rapid shift towards electric vehicles. In this dynamic landscape, efficient production testing and high-quality assessment of the device under test (DUT) have never been more critical. New assessment methodologies ensure high-level product and process quality, minimizing the risk of costly recalls and compensation claims.

In this webinar, AVL’s Kurt Reininger and Bernhard Groechenig explore how high-quality result data, centralized data management, and machine learning (ML) algorithms can substantially improve production testing processes and turn testbed data into valuable insights that enhance product and process quality.

Key topics and takeaways:

• Diverse production landscape: Understand today’s data requirements
• Use of test field data: Learn how to extract maximum values from your data efficiently
• Train ML models: Gain insight into how to train ML models to analyze data in real-time
• Boost evaluation quality: Find out how to improve the accuracy of pass/fail assessments (OK/NOT-OK ) for e-axle use cases