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Smartly Detect Product Defects in Production

Minimising the amount of scrapped material by identifying early which products cannot be fully finished

Challenge
  • One of the world’s largest automotive manufacturers

  • Minimising amount of scrapped material during the riveting process helps to reduce costs

  • Defects could be identified by collecting relevant data from the assembly line in real time

  • Wanted a solution to empower assembly line workers to identify defects during the riveting process

  • Built data pipelines to collect the relevant data from the assembly line

  • Trained a classification Machine Learning model

  • Designed a dashboard for easy interpretation of model’s outputs

Solution
Value
  • Empowered workers to identify riveting defects in real time, allowing for the manufacturing process to be adjusted much more efficiently

  • Contributed to the overall reduction in costs for the manufacturer

Roles

Data Scientist, Machine Learning Engineer

Tools

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Sector

Automotive

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