Productive, reliable, and precise machines
Research and development of machines

Research and development of machines
Research and development of productive, reliable, precise machine tools in connection with their technological application.
In machine R&D we focus on the development of machines and machine-building assemblies. We work on new machine concepts, research in computational modeling, and simulations of machine behavior and of the machine’s interaction with the manufacturing process. Our focus includes advanced modeling, machine design solutions, accuracy, and energy efficiency.
One specific research topic is the development of digital twins of machines. These simulation models provide computational and simulation support for the design and optimization of machining technology and for the machine’s interaction with the machining process.
Our activities target both development cooperation with machine manufacturers and the research and development of modern computational methods and procedures—without which the design and operation of modern machine tools would be unthinkable today.
We provide support for machine development from the initial design through to prototype realization.
Contacts
Ing. Matěj Sulitka, Ph.D.
M.Sulitka@rcmt.cvut.cz
+420 605 205 927
+420 221 990 944
Ing. Jan Smolík, Ph.D.
J.Smolik@rcmt.cvut.cz
+420 605 205 918
+420 221 990 918
The main research topics include:
Developing an integrated approach to optimizing load-bearing structures, including conceptual topological optimization, topological optimization, and parametric optimization. Our original solution for conceptual topological optimization designs the optimal sizing of the volume of the machine’s structural members—i.e., compares different structural concepts and computes the mass required to achieve a target stiffness. Successful applications show up to a 50% reduction in machine structure weight while simultaneously improving dynamic properties.
More information about optimization and modeling.

The research focuses on sources of damping in machine-tool structures, using the findings to optimize load-bearing structures for vibration absorption and to formulate general recommendations. The main goal is to identify suitable areas for practical design improvements through tuned mass dampers, damping fills (e.g., leaving cores in castings), redesign, application of unconventional materials, replacing rolling guides with hydrostatic ones, and more.

This research aims to provide manufacturers and machine users with reliable tools for full-fledged virtual testing and optimization of machining processes to increase productivity, reliability, and accuracy. The digital models describe the machine as a complex system linking CNC control, feedback control of drives, the machine’s mechanical structure (including drives), and the machining process. A unique feature of the software is inclusion of a model of the compliant workpiece’s dynamics in the machining simulation. The long-term goal is to use digital twins for online machine condition monitoring and automated process control interventions in line with Industry 4.0.

The research covers modeling the dynamics of the machine–tool–workpiece system and modeling process forces. We develop refined, validated multi-parameter models of cutting-force components that enable simulation of process forces and machining stability for arbitrary tool geometries in both turning and milling. The work focuses on original, extended stability models that overcome limitations of common approaches and address complex multi-axis machining and force interactions in a mutually vibrating tool–workpiece system. Novelty lies in a physically relevant description of process damping due to frictional force components, including the effect of variable tool engagement.

We are developing a unique technology for enhanced compensation of thermal deformations based on transfer functions. The commanded axis position is corrected in real time using a mathematically modeled correction. The model structure accommodates diverse heat sources and sinks (exchangeable spindle heads, adaptive cooling systems, ambient changes), volumetric-error compensation, and multi-purpose machine configurations, enabling easy portability across machines of the same type. The models leverage maximum information from the CNC. The research targets automatic model recalibration and machine-learning methods to boost long-term stability and machine intelligence in line with Industry 4.0.

The research incorporates the effects of process fluids, material removal, process conditions, various workpiece materials, and tool types into models that minimize machining’s impact on the accuracy of the machine–tool–workpiece system. Achieving adequate results requires capturing, processing, and using information from hard-to-access locations (tool or workpiece temperature, deformations at the tool tip and in machine components) via unique smart sensors. The effectiveness of compensation approaches is then validated in practice.

The research focuses on validating and verifying models of frictional losses (heat sources) in rolling bearings. Together with bearing-kinematics models, these form coupled models of spindle thermo-mechanical behavior that capture bearing property changes as a function of speed. The coupled models are used to predict spindle thermo-mechanical stability, support spindle design, estimate thermal errors, and predict bearing failure states.

R&D on modeling hydrostatic (HS) bearings for motion axes has produced original models of the interaction between HS pockets and compliant machine structures, including heat sources, damping, stiffness, load capacity with pocket tilting, and energy consumption. The research targets active control of the throttle-gap height in HS cells to compensate machine geometric errors—especially angular errors that would otherwise be uncompensable. Ongoing work covers modeling damping in HS cells and developing new passive and active flow regulators and their control.

The work focuses on developing machine-tool milling heads with increased rotational-speed parameters. Activities split in two directions: (1) research into the thermo-mechanical state of milling heads (drive, bearings, preload, lubrication, cooling) and design optimization to achieve thermal stability and symmetry; and (2) the actual development of milling heads and accessories in cooperation with industrial partners.

Developing tools for drive design and optimization includes algorithms for rapid selection of suitable drive components based on required machine parameters and service life, as well as mathematical models of individual components and of the drive as a whole, including its interaction with the machine’s mechanical structure and the control system. Further research targets diagnostics of motion axes and parameter optimization to achieve higher motion accuracy.

Our research and industrial collaboration in machine-tool testing has long focused on both standard and non-standard methods. Standard tests include those under ISO 230 (measurement of geometric accuracy of machine tools; measurement of machine and component vibrations, vibro-diagnostics; measurement of acoustic quantities of machines and workshops; measurement of static stiffness; measurement of machine thermal behavior). Non-standard tests include laser-optical accuracy methods, customer-specific acceptance tests, prototype behavior tests, and operational behavior tests.

A specific focus area is vibro-diagnostics to identify the causes of unwanted vibrations and noise and to propose corrective measures. Beyond analyzing a specific machine in a given state, this area is research-relevant because many foundational diagnostic insights can now be advanced through data processing and the Industry 4.0 concept. Measurement and analysis of machine and component dynamics include locating structural weak points and verifying computational models. Closely related is the machine–process dynamic interaction (machining stability tests).
