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.

翻译：正确设置生产机器的参数对于提高产品质量、提升效率、降低生产成本以及支持可持续发展目标至关重要。识别最优参数涉及一个迭代过程：生产对象并评估其质量。因此，最小化迭代次数对于减少因不成功尝试而产生的成本是可取的。本文提出一种利用贝叶斯优化算法在系统内部优化机器参数的方法。通过利用现有机器数据，我们采用迁移学习方法以最少迭代次数识别最优解，从而形成一种成本效益高的迁移学习算法。我们在现实世界的金属板材激光切割机上验证了所提方法。