Abstract

Machine tools are among the most important components in modern production engineering where cost-effective machining of parts with high geometric accuracy is required. The demand for ever higher productivity and shorter cycle times is met by high jerk, acceleration and velocity of moving feed axes. This rises the need for highly dynamical drive trains with precise position accuracy. Among various existing drive concepts, ball screw spindle, rack and pinion and linear motor drives are most frequently used in machine tools. Each concept has its advantages and disadvantages. Depending on different requirements, such as machining process, operating range and desired trajectory dynamics, a best fitting drive concept for each application exists. A detailed investigation of the different drive concepts regarding the impact on the overall machine tool dynamics requires expensive prototyping and intensive testing. However, mechatronic system simulation offers the possibility to virtually examine different drive concepts in advance, reducing the need for physical test benches. In the present work, the two different drive concepts ball screw spindle and linear motor drive for a milling machine are investigated. The structural mechanics including all physical drive specifications is represented as a finite element model. The control of the drive train axes influences the machine tool behavior and cannot be neglected. Hence, drive trains are modelled in a graphical block diagramming tool leading to a highly accurate and versatile mechatronic system simulation model. Various analysis methods, both in frequency and time domain are available to investigate the overall dynamic behavior of the machine tool. The simulation results are compared to measurements on a real test machining center and show excellent agreement. Thus, different drive concepts can be analyzed and assessed during the development process. Vulnerabilities may be identified and suggestions for improvements can be derived. Furthermore, the system simulation model serves as a digital twin during the entire life cycle for various purposes.