The use of magnesium alloys in various industries and commerce is increasing due to their properties such as high strength and casting properties, high vibration damping capability, good shielding of electromagnetic radiation and high machinability. Conventional machining methods can, however, pose a risk of ignition. AWJM is a safe alternative to conventional machining, but the deflection and vibration of the water jet can affect surface quality. Therefore, the aim of this study was to investigate the effects of selected AWJM parameters on the surface quality and vibration of machined magnesium alloys. Jet deflection angle, surface roughness parameters and vibration during AWJM were investigated. The findings showed that higher skewness occurred at a lower abrasive flow rate, while higher average values of the Sku roughness parameter were obtained at m_a = 8 g/s in the range of 60–140 mm/min. It was also observed that higher vibration values occurred at m_a = 8 g/s. The input parameters for creating an artificial neural network (ANN) model used in this study were the cutting speed v_f and the mass flow rate m_a. The results of this study provided valuable insights into ways of ensuring a safe and efficient machining environment for magnesium alloys. The use of ANN modeling for predicting the vibration and surface roughness of AZ91D magnesium alloy after water-jet cutting could be an effective tool for optimizing AWJM parameters.

This paper reports the results of measurements of cutting forces and delamination in drilling of Glass-Fiber-Reinforced Polymer (GFRP) composites. Four different types of GFRP composites were tested, made by a different manufacturing method and had a different fiber type, weight fraction (wf) ratio, number of layers, but the same stacking sequence. GFRP samples were made using two technologies: a novel method based on the use of a specially designed pressing device and hand lay-up and vacuum bag technology process. The study was conducted with variable technological parameters: cutting speed v_c and feed per tooth f_z. The two-edge carbide diamond-coated drill produced by Seco Company was used in the experiments. Cutting-force components and delamination factor were measured in the experiments, and photos of the holes were taken to determine the delamination. In addition, modeling of cause-and-effect relationships between the technological drilling parameters v_c and f_z was simulated with the use of artificial neural network modeling. For all tested GFRP materials, an increase in f_z led to an increase in the amplitude of cutting-force component F_z. The lowest values of the amplitude of cutting-force component F_z were obtained with the lowest tested feed per tooth value of 0.04 mm/tooth for all tested materials. It was observed that materials produced with the use of the specially designed pressing device were characterized by lower values of the cutting-force component F_z. It was also found that the delamination factor increased with an increase in f_z for all tested GFRP materials. A comparison of the lowest and the highest values of f_z revealed that the lowest delamination factor increase was archived by the B1 material and amounted to about 12.5%. The error margin of the obtained numerical modeling results does not exceed 15%, so it can be concluded that artificial neural networks are a suitable tool for modeling cutting force amplitudes as a function of v_c and f_z. The study has shown that the use of the special pressing device during the manufacturing of composite materials has a positive effect on delamination.

The paper presents the results of an experimental work on the assessment of microscopic morphology of the surfaces obtained as a result of cutting with a Water Jet (WJ) water abrasive beam. During the tests, the following values were taken as input values: feedratev f [mm•min-1 ] and mass flow rate of the abrasive material m a [g•s-1 ]. As constant parameters were assumed: the angle of incidence of the water-abrasive mixture stream on the workpiece material equal to 90°, the type of abrasive (garnet 80 mesh), the pressure value p [MPa] and the distance of the nozzle from the cut material l [mm]. The values disturbing the process were vibrations generated during the cutting process and motion resistance resulting from friction processes in the guides and kinematic gears. The subject of analysis were the interfacial structures of multilayer structures connected in a vulcanization process with the surface of thin-walled parts made of AW-5754 aluminum alloy. As an effect, the results describing the nature of changes in spatial parameters of surface roughness as well as parameters of skewness and kurtosis as a function of WJ technological cutting of composites materials conditions were obtained.

The paper presents the results of experimental work on the assessment of 3D geometry of surface stereometry obtained as a result of Water Jet (AWJ) water-abrasive beam cutting using an Eckert hydro-abrasive cutter. Studies of geometric structures were conducted using the Alicona microscope. 3D spatial roughness parameters such as Sa, Sq, Ssk, and Sku were analyzed. The roughness parameters has been determined for 3 macro-areas of the multilayer structure surfaces combined as a result of the vulcanization process with an aluminium alloy surface (for representative surface of the aluminium alloy (B) and silicone (A), as well as the interphase surface (A/B)). The obtained experimental results were described by means of a 2nd degree regression function and the relevant determination coefficients were determined. All recorded characteristics of roughness changes showed an upward trend. The progressive nature of the changes concerned the entire range of input values as a function of the feed rate v f for both analyzed mass expenditures of the Water Jet cut. The results of the research work are summarized in the technological function of the AWJ cutting conditions, such as the cutting speed and the mass flow of the abrasive material.

This study focused on analyzing vibrations during waterjet cutting with variable technological parameters (speed, v_fi; and pressure, p_i), using a three-axis accelerometer from SEQUOIA for three different materials: aluminum alloy, titanium alloy, and steel. Difficult-to-machine materials often require specialized tools and machinery for machining; however, waterjet cutting offers an alternative. Vibrations during this process can affect the quality of cutting edges and surfaces. Surface roughness was measured by contact methods after waterjet cutting. A machine learning (ML) model was developed using the obtained maximum acceleration values and surface roughness parameters (Ra, Rz, and RSm). In this study, five different models were adopted. Due to the characteristics of the data, five regression methods were selected: Random Forest Regressor, Linear Regression, Gradient Boosting Regressor, LGBM Regressor, and XGBRF Regressor. The maximum vibration amplitude reached the lowest acceleration value for aluminum alloy (not exceeding 5 m/s²), indicating its susceptibility to cutting while maintaining a high surface quality. However, significantly higher acceleration amplitudes (up to 60 m/s²) were registered for steel and titanium alloy in all process zones. The predicted roughness parameters were determined from the developed models using second-degree regression equations. The prediction of vibration parameters and surface quality estimators after waterjet cutting can be a useful tool that for allows for the selection of the optimal abrasive waterjet machining (AWJM) technological parameters.

The aim of this study is to investigate the impact of machining parameters on the deflection of a cutting tool (i.e., end mill) in the milling of a surface with a curvilinear profile. Test samples were made of aluminium alloy EN AW-7075 T651. Experiments were conducted using the Gocator 2530 laser line profile sensor for real-time measurement of dynamic tool displacement with an inspection speed up to 10 kHz at resolution ranging from 0.028 to 0.054 mm. Response surface methodology was used. Five main technological factors were analysed: cutting speed, feed per tooth (cutting parameters), amplitude, term (curvilinear profile parameters), and the number of flutes (end mill parameter). Obtained data were filtered and visualised as 3D plots. The results showed that cutting speed and amplitude had the greatest impact on tool deflection, while feed per tooth also played a significant role in process stability. In particular, the use of tools with a higher number of flutes led to a considerable reduction in tool deflection, confirming their positive effect on the stability of the machining process. These findings may serve as a basis for the optimisation of machining parameters by taking into account the dynamic deformation of cutting tools.