The article presents an attempt to identify an appropriate regression model for the estimation of cutting tool lifespan in the milling process based on the analysis of the R² parameters of these models. The work is based on our own experiments and the accumulated database (which we make available for further use). The study uses a Haas VF-1 milling machine equipped with vibration sensors and based on a Beckhoff PLC data collector. As the acquired sensor data are continuous, and in order to account for dependencies between them, regression models were used. Support Vector Regression (SVR), decision trees and neural networks were tested during the work. The results obtained show that the best prediction results with the lowest error values were obtained for two-dimensional neural networks using the LBFGS solver (93.9%). Very similar results were also obtained for SVR (93.4%). The research carried out is related to the realisation of intelligent manufacturing dedicated to Industry 4.0 in the field of monitoring production processes, planning service downtime and reducing the level of losses resulting from damage to materials, semi-finished products and tools.

This article presents a strength analysis of drone structural components made of polymeric materials (PLA, PETG, ASA). The geometry of three-dimensional research models was developed and constructed using artificial intelligence-based software. The geometric data was processed into a form suitable for production using a 3D printer based on MEX (Material Extrusion) polymer layer extrusion technology. Strength tests of the drone's arms, which serve as motor mounts, were performed using a static bending tester equipped with a special fixture.

The LEACH protocol remains a foundational clustering algorithm for wireless sensor networks, yet its random cluster head selection leads to uneven energy consumption and premature network degradation. Despite numerous modifications, no existing variant incorporates a spatially deterministic...

Additive manufacturing technologies rely on extensive digital data processing, beginning with 3D-CAD model development and continuing through data preparation, process planning, manufacturing execution, and post-processing operations. As additive manufacturing systems become increasingly adopted...

The study concerns the provision of fault tolerance for computer networks, regarded as a key component in the functioning of modern Smart City and IoT critical infrastructure. The authors present a layered architecture for fault tolerance infrastructure. The proposed concept constitutes a flexible tool supporting the design and management of networks that are sensitive to disruptions and downtime, while also being oriented toward the security and operational stability of urban ecosystems. The notion of overall system availability is introduced as a function of the availability of its individual layers, and an analysis is conducted of the key technical solutions that collectively form a complementary system of failure resilience.

The article presents the use of IT systems and cooperative robots in the 3D printing process based on the layered extrusion of polymer materials, enabling the construction of autonomous production cells that meet the assumptions of cyber-physical production systems. This solution allows for an innovative approach to planning the production of products from polymer materials in terms of pre- and post-process operations using remote monitoring, and optionally allows the use of artificial intelligence to control the production process. An important feature of the proposed solution is its modularity and scalability, which is important when manufacturing products using the 3D printing method from thermoplastic polymer materials.