Punching is a wide-spread production process, applied when massive amounts of the ever-same cheap parts are needed. The punching process is sensitive to a multitude of parameters. Unfortunately, the precise dependencies are often unknown. A prerequisite for optimal, reproducible and transparent process alignment is the knowledge of how exactly parameters influence the quality of a punching part, which in turn requires a quantitative description of the quality of a part. We developed an optical inline monitoring system, which consists of a combined imaging and triangulation sensor as well as subsequent image processing. We show that it is possible to capture images of the cutting surface for every part within production. We automatically derive quality parameters using the example of the burnish height from 2D images. In addition, the 3D parameters are calculated and verified from the triangulation images. As an application, we show that the status of tool wear can be inferred by monitoring the burnish height, with immediate consequences for predictive maintenance. Although limited by slow images processing in our prototype, we conclude that connecting machine and process parameters with quality metrics in real time for every single part enables data-driven process modelling and ultimately the implementation of intelligent punching machines.

Stamping is a wide-spread production process, applied when massive amounts of the ever-same cheap parts are needed. For this reason, a highly efficient process is crucial. The cutting process is sensitive to a multitude of parameters. A process that is not correctly adjusted is subject to considerable wear and therefore not efficient. Unfortunately, the precise dependencies are often unknown. A prerequisite for optimal, reproducible and transparent process alignment is the knowledge of how exactly parameters influence the quality of a cutting part, which in turn requires a quantitative description of the quality of a part. A data driven approach allows to meet this challenge and quantify these influences. We developed an optical inline monitoring system, which consists of a image capturing, triangulation and image processing, that is capable of deriving quality metrics from 2D images and triangulation data of the cutting surface, directly inside the machine and without affecting the process. We identify features that can be automatically turned into quality metrics, like fraction of the burnish surface or the cut surface inclination. As an application, we show that the status of tool wear can be inferred by monitoring the burnish surface, with immediate consequences for predictive maintenance. Furthermore, we conclude that connecting machine and process parameters with quality metrics in real time for every single part enables data driven process modelling and ultimately the implementation of intelligent stamping machines.