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 .

This paper is an attempt to determine the effect of technological parameters of milling on the surface roughness of the AZ91D magnesium alloy. The cutting tool used in tests was a TiAlN-coated carbide milling cutter. The milling tests were carried out at variable cutting speed, feed per tooth and...

This paper analyses the effect of the abrasive waterjet cutting parameters’ modification on the condition of the workpiece surface layer. The post-machined surface of casting aluminium alloys, AlSi10Mg and AlSi21CuNi, was characterised in terms of surface roughness and irregularities, chamfering, and microhardness in order to reveal the effect that variable jet feed rate, abrasive flow rate, and sample height (thickness of the cut material) have on the quality of surface finish. From the analysis of the results, it emerges that the surface roughness remains largely unaffected by changes in the sample height h or the abrasive flow rate m_a, whereas it is highly susceptible to the increase in the jet feed rate v_f. It has been shown that, in principle, the machining does not produce the strengthening effect, that is, an increase in microhardness. Owing to the irregularities that are typically found on the workpieces cut with higher jet feed rates v_f, additional surface finish operations may prove necessary. In addition, chamfering was found to occur throughout the entire range of speeds v_f. The statistical significance of individual variables on the 2D surface roughness parameters, Ra/Rz/RSm, was determined using factorial analysis of variance (ANOVA). The results were verified by means of artificial neural network (ANN) modelling (radial basis function and multi-layered perceptron), which was employed to predict the surface roughness parameters under consideration. The obtained correlation coefficients show that ANNs exhibit satisfying predictive capacity, and are thus a suitable tool for the prediction of surface roughness parameters in abrasive waterjet (AWJ) technology.

The article presents the results of an analysis of the influence of the technological parameters related to tool holder types on the vibrations occurring during the milling of AZ91D magnesium alloy. Magnesium alloys are very low-density materials and, therefore, are increasingly being considered as replacement materials for the more commonly used aluminium alloys. The tool used in the study was a carbide end mill with TiAlN coating, clamped in three different types of tool holder: ER collet, heat shrink, and Tendo E hydraulic. The milling tests used straight toolpaths at varied cutting speeds and feed per tooth values. Based on the vibration displacement and acceleration signals recorded during the machining tests, the following were analysed: maximum value, amplitude, and root mean square (RMS) value of the vibrations. As part of the study, composite multiscale entropy (CMSE) analysis was also performed, describing the level of disorderliness of the obtained vibration signals. The increase in machining parameters caused an increase in the values characterising the displacement and acceleration of the vibrations. It was noted that multiscale entropy might be an important parameter describing the vibration signal (both displacement and acceleration).

This paper shows the surface quality results after finishing milling of AZ91D and AZ31 magnesium alloys. The study was performed for variable technological parameters: cutting speed, feed per tooth, axial depth of cut and radial depth of cut. The tools used in the study were two carbide cutters with a different tool cutting edge helix angle. The measurement of the research results presented the surface roughness parameters was made on the lateral faces and the end faces of the specimens. Statistical analysis and simulations using artificial neural networks were carried out with the Statistica software. The normality of the distribution was examined, and the hypotheses of the equality of mean values and variance were verified. For the AZ91D magnesium alloy on the lateral and the end faces (Ra, Rz parameters), simulations were carried out. Two types of ANN were used: MLP (Multi-layered perceptron) and RBF (Radial Basis Function). To increase the machining stability and to obtain a high surface finish, the more suitable tool for finishing milling is the tool with a helix angle of λ_s = 20°. Artificial neural networks have been shown to be a good tool for predicting surface roughness parameters of magnesium alloys after finishing milling.