Automotive manufacturing case studies

Case Study Objectives Locate source(s) of failure in axle assemblies Reduce failure and rework rates using automotive manufacturing data Challenge Assemblies require >200 measurements across 20 different operations to produce one unit Multiple sources contributed

Case Study Objectives Reduce warranty claims through enhanced End-of-Line Testing with machine learning Accelerate root cause analysis for faulty transmissions Challenge Training data was drawn from only 100 units, none of which failed EOL tests

Case Study Objectives Estimate maximum available friction between tires and road surface (surface μ) using vehicle data in real time Develop miniaturized machine learning models for edge deployment Challenge Data was generated via low excitation

While some automotive OEMs use machine learning and artificial intelligence (ML/AI) in their manufacturing processes, many are now moving towards using data analytics with predictive analysis to enhance the driver experience.

Case Study Objectives Improve vehicle diagnostics by predicting failures in five different engine platforms Identify the causes of engine failures using test signal data Challenge Client data collected from four engine platforms, each of which

Case Study Objectives Improve EOL testing for electric power steering systems to catch more defects and reduce warranties in the field Identify failing units with 0% false negative rate and <1% false positive rate Challenge