This paper presents some important methods of optimisation of CNC programming which covers algorithms of feed changes along the path of tool movement, the concept of feature-based recognition machining (FBM), numerical simulation (FEM/FEA), advanced virtual modelling of machining systems, real-time process simulations with the use of digital twins (DTs) and intelligent control strategies based on the STEP-NC standards. Selected examples of practical industrial applications are overviewed and future development trends are discussed.

KEYWORDS: CNC machine tool, control program, feed optimization, numerical simulation, virtual simulation, STEP-NC interface

This paper covers the areas of importance in the development of intelligent machine tools (IMTs), including information/knowledge-based computing, advanced virtual modelling of machining systems, real-time process simulations with the use of digital twins (DTs) and intelligent control strategies based on the STEP-NC standards. Select examples of current industrial implementations are discussed, along with prognosing of future development trends. Moreover, the importance of human-machine interaction (HMI) and considering operator behavior and skill levels in the development of HMI is signified.

KEYWORDS: intelligent machine tool, information exchange, virtual simulation, STEP-NC interface

In this paper some rules of mesh-free/meshless modeling and numerical simulations of fundamental physical phenomena associated with the machining process, including mechanisms of plastic deformation, chip formation and interfacial friction are overviewed. Some representative examples of the mesh-free modeling application to material removal processes at different scales, i.e. machining processes, water-jet assisted processes, additive machining processes and simulation of micromachining processes using commonly used SPH (smoothed particle hydrodynamics) method are given.

KEYWORDS: mesh-free modeling, numerical simulations, machining process

This paper presents an approach of empirical modeling of cutting process physical phenomena with measurement uncertainty parameters accompanied to the model exponents/ /coefficients. The approach is presented trough an example of creating a power mathematical model for average cutting temperature in turning with details about the uncertainty contributions from different experimental plans. The approach is proposed to be implemented as usual practice during empirical modeling, in order the resulting models to fit with the needs of the smart machining systems and the needs of interoperability between researchers.

The paper concerns the analysis of accuracy of two methods of probe radius correction which are based on Bézier curves. Those methods can be applied in the case of measurements of free-form surfaces. The analysis of accuracy of selected methods was done in relation to reference corrected measured points.

In this paper the method based on the ensemble of artificial neural networks is presented for prediction of the geometrical quality of workpieces after electro-discharge machining (EDM). The complexity and random nature of physical phenomena accompanying the EDM process excluded the theoretical ways. The working electrodes were measured using CMM in flexible manufacturing system. The data obtained from inter-operational measurements were used for the neural networks training. Commonly used measures to express the tool wear turn out to be useless due to their large uncertainty. The tool monitoring and the ensemble method provided more stable diagnosis of the condition of the tool.