Edyta Ładyżyńska-Kozdraś

Multi-material additive manufacturing using thermoplastic extrusion enables the integration of soft and rigid components in a single print operation, yet the interfacial bond strength between new foaming thermoplastic polyurethane (TPU) and engineering-grade filaments remains poorly characterized. This study investigates the bond strength between a foaming TPU filament (Varioshore, colorFabb) and PETG in a lap-joint configuration based on the ASTM 3165 standard, with respect to printing temperature (190 °C, 205 °C, 220 °C, 240 °C) and infill orientation (0° and 45°). Samples were fabricated using a multi-nozzle Prusa XL printer and tested on an MTS hydraulic testing machine. The mean forces recorded were approximately 0.10 kN, 0.21 kN, 0.56 kN, and 0.45 kN at 190°C, 205°C, 220°C, and 240°C, respectively (averaged across both infill orientations), corresponding to bond stresses of 0.6 MPa, 1.3 MPa, 3.5 MPa, and 2.8 MPa. Interestingly, the 220 °C is the optimal printing temperature, yielding the highest bond strength and stiffness, while 240 °C increases elongation-at-break at the cost of bond strength. The 0° infill orientation produced bond up to 50% stronger than the 45° orientation at lower temperatures, with the difference diminishing at higher temperatures. Even under optimal conditions (220°C), the foaming filament bond was approximately 33% weaker than the non-foaming 30D TPU–PETG reference bond, likely due to interface porosity introduced by the foaming agent. The findings are demonstrated through a practical application in the form of a multi-material prosthetic finger design.

This article presents the properties of a front-load washing machine equipped with a self-balancing system and its possibility of industrial application in a series of productions. The dynamic model describes the behavior of the unbalanced drum and the free elements during balancing. The article...

The paper presents a dynamic model of a small electro-pneumatic percussive tool whose handle is elastically connected to the casing. The pneumatic force is defined as a function of the position of the piston and the striker. The Hertz model has been applied to the impact forces and given the...

The paper addresses the dynamics modeling of underwater vehicles that are inertia propelled, i.e., they can move based upon the change of the amount of water in their water tanks and the motion of an internal mass, enabling maneuvers. Underwater vehicles of this type can be successfully applied in ocean scientific reconnaissance and exploration missions or for water pollution monitoring. Usually, dynamics modeling methods for them are based upon the Newton–Euler or Lagrange approaches modified to encompass variable mass. The main motivation of this research is to explore other modeling methods and compare them to those traditionally used. In this paper, modeling methods based on the Maggi and Boltzmann–Hamel approaches are presented and discussed with respect to their effectiveness in modeling, operation, and control applications. The resulting comparisons indicate that the traditional approaches are sufficient for the analysis of vehicle operation and performance in the realization of simple tasks; however, they become of limited application when the variable mass or constraints on vehicle dynamics or motion are added or complex maneuvers are required. In this regard, the Maggi or Boltzmann–Hamel approaches are more effective for dynamics modeling. The theoretical development is illustrated by examples of vehicle dynamics developed using the approaches we propose.

The paper presents a method for modeling and estimating the orientation of a launch cart in the magnetic suspension system of an innovative UAV catapult. The catapult consists of stationary tracks lined with neodymium magnets, generating a trough-shaped magnetic field. The cart levitates above the tracks, supported by four containers housing high-temperature YBCO superconductors cooled with liquid nitrogen. The Meissner effect, characterized by the expulsion of magnetic fields from superconductors, ensures stable hovering of the cart. The main research challenge was to determine the cart’s orientation relative to the tracks, with a focus on the pitch angle, which is critical for collision-free operation and system efficiency. A dedicated measurement stand equipped with Hall sensors and Time-of-Flight (ToF) distance sensors was developed. Hall sensors mounted on the cart’s supports captured magnetic field data at specific points. To model the tracks, the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology was employed—a structured framework consisting of six stages; from problem understanding and data preparation to model evaluation and deployment. This approach guided the analysis of data-driven models and facilitated accurate pitch angle estimation. Evaluation metrics, including mean squared error (MSE), were used to identify and select the optimal models. The final model achieved an MSE of 0.084°, demonstrating its effectiveness for precise orientation control.

This paper considers the problem of flying a UAV along a given trajectory at speeds close to the speed of sound and above. A novel pitch channel control system is presented using the example of a trajectory with rapid and large changes in flight height. The control system uses a proportional–integral–differential (PID) controller, whose gains were first determined using the Ziegler-Nichols II method. The determined gains were then optimised to minimise height error using a recurrent back-propagation neural network (PIDNN), with which new controller gains were determined, which is also a novelty of this study. Simulations were carried out for flights at subsonic speeds close to the speed of sound and supersonic speeds, at low and high altitudes. The simulations showed that determining controller gains using a recurrent neural network significantly minimises height errors and increases the flexibility of the PID controller.

This paper examines the influence of two different supports, i.e., composite and steel, on the results obtained during a quasi-static crush test of an aluminum alloy honeycomb impact attenuator. It’s part of a vehicle that competes in the Formula Student series and requires safety tests to be...

The surgical robots currently used in cardiac surgery are equipped with a remote center of motion (RCM) mechanism that enables the required spherical workspace. The dynamics model of the surgical robot’s RCM mechanism presented in this work includes a direct current (DC) motor, an optimal proportional–integral–derivative (PID) controller, and a LuGre friction model that takes into account the Stribeck effect and surface deformation. A finite element method (FEM) analysis of transients was carried out using the energy hypothesis of von Mises with an optimal input signal from the mechatronic system with a PID controller obtained using the Runge–Kutta differentiation method in the Dormand–Prince ordinary differential equations variant (ODE45). Five criteria were adopted for the objective function: the safety factor related to the stress function in the time-varying strength problem, the first natural frequency related to stiffness and the resonance phenomenon, the buckling coefficient in the statics problem related to stability, the static factor of safety, and the displacement of the operating tip. The force inputs to the dynamics model were derived from in vitro force measurements on cardiovascular tissue using a force sensor. The normality of the statistical distribution of the experimental data was confirmed using the Kolmogorov–Smirnov statistical test. The problem of multi-criteria optimization was solved using the non-sorter genetic algorithm (NSGA-II), the finite element method, and the von Mises distortion energy hypothesis. Velocity input signals for the transient dynamics model were obtained from a second in vitro experiment on cardiovascular tissue using the minimally robotic invasive surgery (MIRS) technique. An experienced cardiac surgeon conducted the experiment in a modern method using the Robin Heart Vision surgical robot, and a system of four complementary metal–oxide–semiconductor (CMOS) optical sensors and ariel performance analysis system (APAS-XP 2002) software were used to obtain the endoscopic tool trajectory signal. The trajectory signal was accurate to ±2 [mm] in relation to the adopted standard, and it was smoothed using the Savitzky–Golay (SG) polynomial smoothing, whose parameters were optimally selected using the Durbin–Watson (DW) statistical test.