Skip to content

Reinforcement Learning, specifically Deep Q-Networks, was applied to semiconductor manufacturing data to efficiently identify substandard products, with a model optimized using the F1-Metric achieving 87% accuracy and a 22.4% time-saving, highlighting RL's potential in enhancing manufacturing processes.

License

Notifications You must be signed in to change notification settings

janmayer15/Optimizing-Production-Profitability-through-Deep-Reinforcement-Learning-Driven-Quality-Prediction

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Optimizing-Production-Profitability-through-Deep-Reinforcement-Learning-Driven-Quality-Prediction

Reinforcement Learning, specifically Deep Q-Networks, was applied to semiconductor manufacturing data to efficiently identify substandard products, with a model optimized using the F1-Metric achieving 87% accuracy and a 22.4% time-saving, highlighting RL's potential in enhancing manufacturing processes.

This repo supports a research paper which will be handed in soon. Any updates will be made available soon.

About

Reinforcement Learning, specifically Deep Q-Networks, was applied to semiconductor manufacturing data to efficiently identify substandard products, with a model optimized using the F1-Metric achieving 87% accuracy and a 22.4% time-saving, highlighting RL's potential in enhancing manufacturing processes.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published