Abstract
Abstract
Shear cutting processes are characterised by physical parameters that change in time and space because of influencing process effects. Those parameters partwise can be measured and recorded by sensors and assigned to causal process effects through detailed evaluation based on analytical models. If the analytical model can classify the causal process effects in detail, the quality of the processed sheet metal component can be monitored on the basis of knowledge about tool wear influences and so a permanent part quality only can be ensured by a preventive tool change. In detail, the quality of the cut surface of a shear cut component to be assessed in this process is influenced by both the tool wear effect of the upper and lower tool, whereby both in fact show a significant influence on the different dimensional and surface characteristics of the cut surface. Analytical models for such analysis often are not available, or, if available, it may deliver only proximate results that needs improvement for the respective process effects. Previous procedures for such prediction purpose are frequently based on large and complex neural networks that require as well as process a huge amount of data.
In this regard, a novel evaluation method has shown great potential for data-driven quantification of tool wear effects and looks capable of far outperforming previous process evaluation methods in its performance under different process conditions. In previous studies, specific upper and/or lower tool wear was not considered in a separate manner and a detailed inspection of the finished cut surface there for could not be carried out. This paper investigates how a data-driven approach can be used to evaluate specific wear effects of upper and lower tools in a differentiated manner, and in which degree of detail the main quality characteristics of the cutting surface in a shearing process thus can be assessed.