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Analytics & Machine Learning in COREF

The need and application of ML in manufacturing and assembly has increased in the last two decades because of challenges that have arisen, the availability of large and complex data with little transparency, and that ML tools have become increasingly usable and powerful.

Challenges in manufacturing and assembly that have lent themselves to ML-driven solutions include:

  • The increase in modern digital equipment and maximising their potential e.g., robotics
  • Personalisation of mass products
  • Demand for high-quality products
  • Production costs
  • Equipment failure and bottlenecks
  • Compliance with environmental standards and wastage 

 

 

The ML use case types that have been developed to address the challenges previously listed fall under 4 main solution categories:

  1. Predictive Maintenance
  2. Predictive Quality/Yield
  3. Quality Checks
  4. Production

COREF has investigated the use of ML in the low volume, high complexity manufacturing environment, an environment that typically is poorer in data than other types of manufacture. Given the nature of the data available, COREF has focused on assisted automation inspection use cases using imaging/vision equipment as well as applications of ML in automation, particularly Cobots. 

Analytics & Machine Learning Benefits In COREF