This paper set out to investigate the effect of cutting speed vc and trochoidal step str 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: vc = 400–1200 m/min and str = 5–30%. The results demonstrate a significant effect of cutting data modification on the parameter under scrutiny—the increase in vc 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 vf = 5–140 mm/min, and abrasive flow rate 100% (0.5 kg/min) at vf = 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).
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