T. Deepan Bharathi Kannan, B. Suresh Kumar, G. Rajesh Kannan, M. Umar and Mohammad Chand Khan
This work is aimed at developing relations between the pertinent variables that affect drilling process of stainless steel using artificial neural network. The experiments were conducted on vertical CNC machining centre. The parameters used were spindle speed and feed rate. The effect of machining parameters on entry burr height, exit burr height and surface roughness was experimentally evaluated for different spindle speeds and feed rates. A model was established between the drilling parameters and experimentally obtained data using ANN. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the burr height and surface roughness in drilling of stainless steel. Genetic algorithm (GA) technique was used in this work to identify the optimized drilling parameters. Confirmation test was conducted with the optimized parameters and it was found that confirmation test results were similar to that of GA-predicted output values.
An optimization study using the design of experiment technique is described, in which the surface profile height of a freeform surface, determined in coordinate measurements, is the response variable. The control factors are coordinate sampling parameters, i.e. the sampling grid size and the measuring tip diameter. As a result of the research, an optimal combination of these parameters was found for surface mapping with acceptable measurement uncertainty. The presented study is the first stage of optimization of machining error correction for the freeform surface and was intended to take into account mechanical-geometric filtration of surface irregularities caused by these geometrical parameters. The tests were carried out on a freeform workpiece milled with specific machining parameters, Ra of the surface roughness was 1.62 μm. The search for the optimal combination of parameters was conducted using Statistica software.
Ondrej Kubala, Viktória Mezencevová, Michal Šajgalík, Mário Drbúl, Igor Daniš, Juraj Martinček and Jozef Pustay
Experiment includes the study and identification of monolithic ceramic mining tools by machining the Domex 700 steel that belongs to the group of tough metals with high tensile strength. During the process of machining, the substandard high cutting speeds were used. Identification of ceramic mining tools included the analysis of cutting edge wear and the construction of map of surface roughness parameter Rz dependence on the change of cutting conditions for a given range (machine and material).