Laser triangulation for quality monitoring in automated series forging processes: A method for evaluating the component quality feature ‘flash’

Laser triangulation for quality monitoring in automated series forging processes: A method for evaluating the component quality feature ‘flash’

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.

Keywords
Laser Triangulation, Flash Formation, Process Monitoring, Forging Quality

Published online 5/7/2025, 10 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Claudia GLAUBITZ, Marcel ROTHGÄNGER, Eduard ORTLIEB, Julius PEDDINGHAUS, Kai BRUNOTTE, Laser triangulation for quality monitoring in automated series forging processes: A method for evaluating the component quality feature ‘flash’, Materials Research Proceedings, Vol. 54, pp 917-926, 2025

DOI: https://doi.org/10.21741/9781644903599-98

The article was published as article 98 of the book Material Forming

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

References
[1] A. Küster Simic and J. Schönfeldt, Branchenanalyse Schmiedeindustrie: Struktur, Entwicklung und Zukunftschancen [Analysis of the Forging Industry: Structure, Development, and Future Prospects], Working Paper Forschungsförderung No. 306, Hans-Böckler-Stiftung, 2023. [Online]. Available: https://hdl.handle.net/10419/279559
[2] IEA, Electricity Market Report – Update 2023, International Energy Agency, Paris, 2023. [Online]. Available: https://www.iea.org/reports/electricity-market-report-update-2023. [Accessed: Sep. 22, 2024].
[3] S. Sharma, M. Sharma, V. Gupta, and J. Singh, “A systematic review of factors affecting the process parameters and various measurement techniques in forging processes,” Steel Research International, vol. 94, no. 1, p. 2200529, 2023. https://doi.org/10.1002/srin.202200529
[4] S. Kitayama, “Technical review on design optimization in forging,” International Journal of Advanced Manufacturing Technology, vol. 132, pp. 4161–4189, 2024. https://doi.org/10.1007/s00170-024-13593-w
[5] F. Klocke, Massive Forming, in Manufacturing Processes 4, RWTHedition, Springer, Berlin, Heidelberg, 2013. https://doi.org/10.1007/978-3-642-36772-4_3.
[6] DIN 8583-4:2003-09, Manufacturing processes forming under compressive conditions – Part 4: Die forming; Classification, subdivision, terms and definitions, Beuth Verlag, Berlin, 2003.
[7] B. Tomov, R. Radev, and V. Gagov, “Influence of flash design upon process parameters of hot die forging,” J. Mater. Process. Technol., vol. 157-158, pp. 620–623, 2004. https://doi.org/10.1016/j.jmatprotec.2004.07.124
[8] M. G. Rathi and N. A. Jakhade, “An overview of forging processes with their defects,” Int. J. Sci. Res. Publ., vol. 4, no. 6, pp. 1-6, 2018. [Online]. Available: http://www.ijsrp.org/research-paper-0614.php?rp=P302759
[9] F. Fereshteh-Saniee and A. H. Hosseini, “The effects of flash allowance and bar size on forming load and metal flow in closed die forging,” J. Mater. Process. Technol., vol. 177, no. 1–3, pp. 261–265, 2006. https://doi.org/10.1016/j.jmatprotec.2006.04.046
[10] S. Sharma, M. Sharma, V. Gupta, and J. Singh, “A systematic review of factors affecting the process parameters and various measurement techniques in forging processes,” Steel Research International, vol. 94, no. 4, p. 2200529, 2023. https://doi.org/10.1002/srin.202200529
[11] K. Brunotte and B.-A. Behrens, Increasing the Tool Life of Hot Forging Tools through the Use of Local Load-Adapted Wear Resistant Treatments, Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, TEWISS Verlag, 2021. [Online]. Available: https://d-nb.info/124134664X
[12] C. Glaubitz, M. Rothgänger, H. Monke, E. Ortlieb, J. Peddinghaus, and K. Brunotte, “Digital Process Chains in Die Forging: Integration of Sensors and Data Mining Potentials,” at-Automatisierungstechnik, Sonderheft: Datengetriebene Modellierung in der Umformtechnik, 2025, to be published.
[13] Kumar, K., Kalita, H., Zindani, D., Davim, J.P. (2019). Forming. In: Materials and Manufacturing Processes. Materials Forming, Machining and Tribology. Springer, Cham. https://doi.org/10.1007/978-3-030-21066-3_4
[14] R. Rame, P. Purwanto, and S. Sudarno, “Industry 5.0 and sustainability: An overview of emerging trends and challenges for a green future,” Innovation and Green Development, vol. 3, no. 4, p. 100173, 2024. https://doi.org/10.1016/j.igd.2024.100173
[15] S. J. Mirahmadi and M. Hamedi, “Flash gap optimization in precision blade forging,” Int. J. Mech. Eng. Robot. Res., vol. 6, pp. 200–205, 2017. https://doi.org/10.18178/ijmerr.6.3.200-205