Laser triangulation for quality monitoring in automated series forging processes
A method for evaluating the component quality feature 'flash'
- verfasst von
- Claudia Glaubitz, Marcel Rothgänger, Eduard Ortlieb, Julius Peddinghaus, Kai Brunotte
- Abstract
Flash formation is a characteristic feature of impression die forging, resulting from the expulsion of excess material through the gap between the upper and lower dies. This expulsion is a consequence of the backpressure generated by the material flow, which ensures complete filling of the die cavity. However, this increases material consumption and requires additional post-processing to remove the flash. Flash formation is influenced by process parameters such as die closure, workpiece temperature, forming speed, forming force and lubrication. Improper control of these parameters can lead to excessive or uneven flash formation and incomplete die filling. Finite element method (FEM) simulations show that different flash geometries require varying press forces to fully form the forged parts. The ratio between flash width and thickness affects the contact stresses in the flash land zone, which in turn influence tool wear and energy costs in the forging process. In this work, a method for automated in-line monitoring of flash formation in a serial forging process using laser triangulation is presented. The study aims to explore a potential correlation between the flash contour length and flash thickness, grounded in the principle of volume constancy, using a demonstrator forging component as a case study. To quantify this interaction, a metric is developed to assess die filling and process quality for application in real-time monitoring. Changes in this metric during serial forging processes provide insights into process parameters and identify possible interactions with these factors. Beyond real-time monitoring, the acquired sensor data can serve as a basis for data-driven process modelling. The findings of this study contribute to the development of an improved process model by integrating sensor-based laser triangulation data into adaptive control strategies. Future work will focus on leveraging artificial intelligence (AI) to analyse complex parameter interactions, detect process fluctuations, and optimize forging operations. This approach paves the way for intelligent, self-adaptive process control, reducing material waste and improving efficiency in serial forging applications.
- Organisationseinheit(en)
-
Institut für Umformtechnik und Umformmaschinen
- Typ
- Aufsatz in Konferenzband
- Seiten
- 917-926
- Anzahl der Seiten
- 10
- Publikationsdatum
- 07.05.2025
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Allgemeine Materialwissenschaften
- Elektronische Version(en)
-
https://doi.org/10.21741/9781644903599-98 (Zugang:
Offen)
