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Effect of technological parameters on the surface roughness of aluminium alloys after turning

100%

Zagórski I. , Warda T.

tom Vol. 12, no 2

144--149

The paper analyses the effect of technological parameters of turning (vc and f) on the 2D surface roughness parameters of aluminium alloys. Turning was carried out with SVJBL 2525 M11 shank tool, with VCGT 110302FN-25P insert, dedicated for machining light alloys. Three aluminium alloy types were examined in the study: 7075, 2024, 6082. The present study is focused on selected 2D surface texture parameters, and concludes that feed exerts considerable impact on the surface quality. Simultaneously, the findings of the study indicate that from the technological perspective the change of cutting speed vc does not impact the quality of the machined surface.

Vibration analysis during AZ31 magnesium alloy milling with the use of different toolholder types

100%

Korpysa J. , Zagórski I.

tom Vol. 24, no. 3

489--501

Machining vibrations are an important issue as they occur in all types of machining processes. Due to its negative impact on machining results, this phenomenon is undesirable, and so there have been continuous efforts to find solutions that will minimise it, and thus improve the stability and safety of the machining process. The paper attempts to determine the impact of toolholder type and cutting condition on the vibrations generated while milling an AZ31 magnesium alloy. The tests were performed using the three most common types of toolholders: ER, Shrink Fit and hydraulic. The vibration displacement and acceleration signals were analysed based on parameters such as Peak-to-Peak, Peak, and Root Mean Square. Composite Multiscale Entropy was also applied to check the stability of cutting processes and define the level of signal irregularity. To determine the frequencies of vibrations and to detect chatter vibrations Fast Fourier Transform was performed. This provides information on the stability and enables vibrations to be minimized by avoiding unfavourable cutting conditions.

State of residual stresses after the process of milling selected aluminium alloys

80%

Kuczmaszewski J. , Łogin W. , Pieśko P. , Zagórski I.

Advances in Science and Technology. Research Journal

2018

tom Vol. 12, no 1

63--73

The purpose of this paper was to analyse the structure and the state of residual stresses in the samples made of rolled AW-2024 and AW-7075 aluminium alloy plates, prior to machine cutting and after the process of milling under various machining conditions. The state of stresses was evaluated on the basis of measurements by means of sin2vF X-ray diffractometry and layer removal method. Furthermore, top layer microstructures were compared in half-finished product, machined product, and core material samples.

2D Geometric Surface Structure ANN Modeling after Milling of the AZ91D Magnesium Alloy

80%

Kulisz M. , Zagórski I. , Józwik J.

2022

tom Vol. 16, no 2

131--140

The paper presents the results of modeling 2D surface roughness parameters in milling by means of an artificial neural network (ANN). The AZ91D magnesium alloy was used. A HSS milling cutter was employed in the research. The main aim of the study was to obtain the lowest possible surface roughness (good quality) using a commonly available HSS cutter. The results of the research work were presented in the form of bar charts, surface charts and graphs depicting the quality of artificial neural networks. The conducted research shows that it is possible to carry out the machining processes that enable obtaining an average surface quality (defined by roughness parameters Ra, Rz, RSm, Rsk). The Ra, Rz, RSm parameters increase along with the machining parameters (fz, ap), as expected. The Rsk parameter takes (in most cases) negative values, which may indicate a surface with more intense friction and indicative of flat-topped distribution. On the other hand, the results of modeling the selected parameters – Ra, Rz, RSm – with the use of artificial neural networks allow concluding that the obtained network models show satisfactory predictive ability (R = 0.99), and thus are an appropriate tool for the prediction of surface roughness parameters.