用贝叶斯迁移学习减少机器参数调优的试错次数，降低成本。
Sample-Efficient Bayesian Transfer Learning for Online Machine Parameter Optimization
- 基于贝叶斯优化与迁移学习，复用历史数据加速参数寻优
- 在真实激光切割机上验证，显著减少达到最优参数所需的实验次数
- 适合工业场景中需快速调参、样本稀缺的智能制造系统
正确设置生产机器参数对于提升产品质量、提高效率、降低生产成本并支持可持续发展目标至关重要。确定最佳参数需要反复进行产品制造与质量评估的迭代过程。因此，减少迭代次数可有效降低失败尝试带来的成本。本文提出一种在系统内使用贝叶斯优化算法进行机器参数优化的方法。通过利用现有机器数据，采用迁移学习策略，在最少迭代次数下找到最优参数，实现低成本的迁移学习算法。该方法在真实世界的金属板激光切割机上进行了验证。
原文摘要 · Abstract (English)
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.
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