This paper set out to investigate the effect of cutting speed v_c and trochoidal step s_tr modification on selected machinability parameters (the cutting force components and vibration). In addition, for a more detailed analysis, selected surface roughness parameters were investigated. The research was carried out for two grades of magnesium alloys—AZ91D and AZ31—and aimed to determine stable machining parameters and to investigate the dynamics of the milling process, i.e., the resulting change in the cutting force components and in vibration. The tests were performed for the specified range of cutting parameters: v_c = 400–1200 m/min and s_tr = 5–30%. The results demonstrate a significant effect of cutting data modification on the parameter under scrutiny—the increase in v_c resulted in the reduction of the cutting force components and the displacement and level of vibration recorded in tests. Selected cutting parameters were modelled by means of Statistica Artificial Neural Networks (Radial Basis Function and Multilayered Perceptron), which, furthermore, confirmed the suitability of neural networks as a tool for prediction of the cutting force and vibration in milling of magnesium alloys.

This paper investigates the effect of change of the abrasive flow rate and the jet feed on the effectiveness of machining of AZ91D casting magnesium alloy. The evaluation of the state of the workpiece surface was based on surface and area roughness parameters (2D and 3D), which provided data on: irregularities formed on the workpiece edge surface (water jet exit), the surface quality after cutting, the workpiece surface chamfering, microhardness of the machined surface, and of specimen cross-sections (along the water jet impact). The process was tested for two parameter settings: abrasive flow rate 50 at cutting speed v_f = 5–140 mm/min, and abrasive flow rate 100% (0.5 kg/min) at v_f = 5–180 mm/min. The results demonstrate a significant effect of the abrasive flow rate and the jet feed velocity on the quality of machined surface (surface roughness and irregularities). In addition, selected 2D surface roughness parameters were modelled using artificial neural networks (radial basis function and multi-layered perceptron). It has been shown that neural networks are a suitable tool for prediction of surface roughness parameters in abrasive water jet machining (AWJM).

Surface roughness is among the key indicators describing the quality of machined surfaces. Although it is an aggregate of several factors, the condition of the surface is largely determined by the type of tool and the operational parameters of machining. This study sought to examine the effect that particular machining parameters have on the quality of the surface. The investigated operation was the high-speed dry milling of a magnesium alloy with a polycrystalline diamond (PCD) cutting tool dedicated for light metal applications. Magnesium alloys have low density, and thus are commonly used in the aerospace or automotive industries. The state of the Mg surfaces was assessed using the 2D surface roughness parameters, measured on the lateral and the end face of the specimens, and the end-face 3D area roughness parameters. The description of the surfaces was complemented with the surface topography maps and the Abbott–Firestone curves of the specimens. Most 2D roughness parameters were to a limited extent affected by the changes in the cutting speed and the axial depth of cut, therefore, the results from the measurements were subjected to statistical analysis. From the data comparison, it emerged that PCD-tipped tools are resilient to changes in the cutting parameters and produce a high-quality surface finish.

This paper presents the results of experimental study of the AZ31 magnesium alloy milling process. Dry milling was carried out under high-speed machining conditions. First, a stability lobe diagram was determined using CutPro software. Next, experimental studies were carried out to verify the stability lobe diagram. The tests were carried out for different feed per tooth and cutting speed values using two types of tools. During the experimental investigations, cutting forces in three directions were recorded. The obtained time series were subjected to general analysis and analysis using composite multiscale entropy. Modelling and prediction were performed using Statistica Neural Network software, in which two types of neural networks were applied: multi-layered perceptron and radial basis function. It was observed that milling with high cutting speed values allows for component values of cutting force to be lowered as a result of the transition into the high-speed machining conditions range. In most cases, the highest values for the analysed parameters were recorded for the component F _x , whereas the lowest were recorded for F _y . Additionally, the paper shows that a prediction (with the use of artificial neural networks) of the components of cutting force can be made, both for the amplitudes of components of cutting force F _amp and for root mean square F _rms .

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.

Measurements of forces during machining, especially thin-walled structures typical of the aviation industry, are important in the aspect of instability caused by vibration. One of the last stages of manufacturing by machining is the finishing treatment and deburring of the product’s edges. Brushes with ceramic fibres are often employed in deburring, especially for large-sized elements specific to the aviation industry due to the possibility of automatic machining directly on machining centres. This study set out to analyse the effect of variable brushing conditions on axial forces and the selected surface layer properties of AW-7075 aluminium alloy. Experimental studies have examined factors such as surface roughness and topography, axial cutting force in ceramic brush treatment and surface free energy in the aspect of adhesive joints. The tested variable process parameters were the fibre material and the adjustment sleeve spring stiffness. Based on the tests, it was found that the axial force applied by the brush was more strongly connected with the spring stiffness rather than the type of bristle. For most cases, an increase in the value of free surface energy after brushing was observed compared to the initial machining which was milling.