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 ma = 8 g/s in the range of 60–140 mm/min. It was also observed that higher vibration values occurred at ma = 8 g/s. The input parameters for creating an artificial neural network (ANN) model used in this study were the cutting speed vf and the mass flow rate ma. 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.

Due to the wide application of Carbon Fiber Reinforced Polymer (CFRP) composites in various industries, more and more attention is paid to machining these materials. One of the most popular way of machining composites is the milling. Milling of composite materials (CM) is a diffi cult technology due to their anisotropic and heterogeneous structure and the fact that the reinforcing fi bers have an intense abrasive eff ect on the tool edge during machining. The appropriate selection of technological cutting parameters as well as the type and geometry of the tool can signifi cantly aff ect the value of cutting forces during milling and the quality of the surface after machining. The aim of the paper is to assess the infl uence of used tools (diff ering in the number of cutting edges) and various technological parameters of surface milling of CFRP composites on the cutting forces occurring during machining and on the surface quality after machining. Cutting forces were measured during the milling process on a special stand produced by Kistler and the roughness measurements and surface structure were analyzed using the Alicona Infi niteFocusG5 3D optical microscope. On the basis of performed research it was found that 14 edge tool gives lower values of F x and F y components of the cutting forces comparing to 2 edge tool, which is especially noticeable at higher cutting speed values v c = 160 m/min, where the values of F x and F y components decreased by about 43% at f z = 0.0030 mm/tooth. This tool gives also lower values of the Sa roughness parameter 1.65 µm.

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 vc and feed per tooth fz. 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 vc and fz was simulated with the use of artificial neural network modeling. For all tested GFRP materials, an increase in fz led to an increase in the amplitude of cutting-force component Fz. The lowest values of the amplitude of cutting-force component Fz 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 Fz. It was also found that the delamination factor increased with an increase in fz for all tested GFRP materials. A comparison of the lowest and the highest values of fz 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 vc and fz. The study has shown that the use of the special pressing device during the manufacturing of composite materials has a positive effect on delamination.

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