Sample-Efficient Bayesian Transfer Learning for Online Machine Parameter Optimization
This work addresses cost reduction and efficiency in manufacturing settings, but it is incremental as it applies known techniques to a specific domain.
The paper tackled the problem of optimizing production machine parameters to reduce costs and improve quality by minimizing the number of iterative attempts, introducing a Bayesian optimization method with transfer learning that was validated on a real-world laser cutting machine.
Correctly setting the parameters of a production machine is essential to improve product quality, increase efficiency, and reduce production costs while also supporting sustainability goals. Identifying optimal parameters involves an iterative process of producing an object and evaluating its quality. Minimizing the number of iterations is, therefore, desirable to reduce the costs associated with unsuccessful attempts. This work introduces a method to optimize the machine parameters in the system itself using a Bayesian optimization algorithm. By leveraging existing machine data, we use a transfer learning approach in order to identify an optimum with minimal iterations, resulting in a cost-effective transfer learning algorithm. We validate our approach on a laser machine for cutting sheet metal in the real world.