Intelligent Wear Identification Based on Sensory Inline Information for a Stamping Process

In recent years, the use of integrated sensors in stamping tools has strongly increased. Due to this, the opportunity rises to expand monitoring systems from providing simple differentiations between sufficient and insufficient process conditions or the detection of tool failure towards a detailed process understanding based on characteristic parameters describing the current process condition. Therefore, it is necessary to correlate sensor signals with tool and process parameters. However, most of these correlations are not investigated for stamping processes. Due to this, an investigation of correlations is essential to increase the benefits of current process monitoring systems. The aim of this paper is to establish further gradations for the current process condition to increase process stability. Furthermore, process disturbances or tool failures can be detected and upcoming tool changes will be predictable. A first approach is to evaluate correlations between sensor information, like force measurements, and wear mechanisms. This paper presents an approach for the determination of characteristic wear parameters by using the information provided by integrated force sensors. Furthermore, the parameters are established and correlated with wear phenomena on the tool. For the correlation analysis of the measurement and the current state of wear, the active elements of the tool are removed and the surface is measured by a scanning electron microscope (SEM) and a confocal white light microscope to investigate the 3D-surface properties. The experimental investigations are executed with a single pilot shear cutting tool on a high speed stamping press.