@ARTICLE{Kalandyk_Dawid_CNC_Early,
 author={Kalandyk, Dawid and Kwiatkowski, Bogdan and Mazur, Damian},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e148940},
 howpublished={online},
 year={Early Access},
 abstract={Optimization of industrial processes such as manufacturing or processing of specific materials is a point of interest for many researchers, and its application can lead not only to speeding up the processes in question, but also to reducing the energy cost incurred during them. This article presents a novel approach to optimizing the spindle motion of a computer numeric control (CNC) machine. The proposed solution is to use deep learning with reinforcement to map the performance of the Reference Points Realization Optimization (RPRO) algorithm used in industry. A detailed study was conducted to see how well the proposed method performs the targeted task. In addition, the influence of a number of different factors and hyperparameters of the learning process on the performance of the trained agent was investigated. The proposed solution achieved very good results, not only satisfactorily replicating the performance of the benchmark algorithm, but also, speeding up the machining process and providing significantly higher accuracy.},
 type={Article},
 title={CNC Machine Control Using DeepReinforcement Learning},
 URL={http://www.czasopisma.pan.pl/Content/130036/PDF/BPASTS-04135-EA.pdf},
 doi={10.24425/bpasts.2024.148940},
 keywords={deep reinforcement learning, cnc machining, machining optimization},
}