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CNC machine control using deep reinforcement learning

Kalandyk Dawid, Kwiatkowski Bogdan, Mazur Damian

Bulletin of the Polish Academy of Sciences. Technical Sciences

2024

Vol. 72, nr 3

art. no. e148940

Optimization of industrial processes such as manufacturing or processing of specific materials constitutes 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 the 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.

Improved Matlab/Simulink model of dual three-phase fractional slot and concentrated winding PM motor for EV applied brushless DC drive

Shchur Ihor, Mazur Damian, Makarchuk Oleksandr, Bilyakovskyy Ihor, Turkovskyi Valentyn, Kwiatkowski Bogdan, Kalandyk Dawid

Archives of Control Sciences

2022

Vol. 32, No. 4

677--707

The development of electric vehicles (EV) necessitates the search for new solutions for configuring powertrain systems to increase reliability and efficiency. The modularity of power supplies, converters, and electrical machines is one such solution. Among modular electric machines, dual three-phase (DTP) motors are the most common in high-power drives. To simplify low and medium power drives for EVs based on DTP PM motor, it is proposed to use a BLDC drive and machine of the simplest design - with concentrated windings and surface mounted PMs on the rotor. To study and create such drives, an improved mathematical model of DTP PM machine was developed in this work. It is based on the results of 2D FEM modeling of the magnetic field. According to the developed method, the dependences of the self and mutual inductances between all phase windings from the angle of rotor position and loads of different motor modulus were determined. Based on these inductances, the circuit computer model of DTP PM machine was created in the Matlab/Simulink. It has a high simulation speed and a high level of adequacy, which is confirmed by experimental studies with a mock-up sample of the electric drive system.