Otimização de processos na indústria de manufatura: uso da Krigagem para a redução do número de ensaios em experimentos de usinagem - Corrêa Junior, Gilberto de Almeida (2013)
The analysis of the economic aspects inherent to the production processes and the positioning of the industries regarding their efficiency makes apparent the importance of increasing their productivity and improve resource utilization. In the mechanical manufacturing industries, in particular, increase the efficiency of machining processes is very important. In this context, only be able to select tools for a particular component is not sufficient to meet the economic needs, the ideal tool selected should be the most efficient of all available in a real environment. The influence of this phenomenon on the cutting machining process is traditionally the focus of research linking the cutting parameters; however, the study of the life of cutting tools is the subject of research since the beginning of production processes. Some of the main difficulties related to the research study of the life of cutting tools relate to material cost and time involved in obtaining the experimental data. The use of the method presented by traditional Kuljanic (1980) for obtaining the equation to Taylor tool life consumes much time and resources. As an alternative to the traditional experimental approach, the Kriging, which is a linear interpolation method developed in the 1950's, has shown promising results in the treatment of experimental data (GUNES, 2008). This study aims to investigate the behavior of this interpolation technique to obtain the curve of life of the cutting tool in terms of machining parameters: cutting speed, feed per rotation, and depth of cut. We use data obtained by simulation from a function known tool life (plus a random disturbance), in order to evaluate the results of interpolation technique and investigate its use to reduce the number of experiments necessary to obtain the curve tool life. These results show that it is possible to reduce the number of tests without loss of significance is relevant.