Material-data-driven prediction of sheared surface tears of fine blanked parts

Material-data-driven prediction of sheared surface tears of fine blanked parts

ORTJOHANN Lucia, BECKER Marco, NIEMIETZ Philipp, BERGS Thomas

download PDF

Abstract. Fine blanking is a cost-effective manufacturing technology for mass-producing sheet-metal components with high sheared surface quality. For steels with higher carbon content, the quality of fine blanking products is significantly influenced by microstructural characteristics, such as the morphology and distribution of carbides, which can be controlled through heat treatment [1]. Precisely, there is a relationship between spheroidization of carbides and the occurrence of tears at the sheared surface of fine blanked parts especially at the tip of a gear [2]. Furthermore, material characteristics can vary both along a sheet-metal coil and from coil to coil despite tight tolerances [3] leading to unpredictable tearing. To monitor the fluctuation of material characteristics at regular intervals along sheet-metal, non-destructive testing (NDT) is used before the process providing information about the microstructure. While in the state of the art, the data from NDT was used to calculate the mechanical properties and to optimize the process based on these properties [4], in this paper, NDT data is used to predict the sheared surface tears of fine blanked parts, without the reduction of the content-rich data to mechanical properties. For this purpose, an experiment was conducted on the fine blanking of the steel 42CrMo4+A to produce components resembling a gear shape. Prior to the manufacturing process, the material was measured using an eddy current sensor, and subsequently the tearing of the fine blanked parts was evaluated. For the prediction of sheared surface tears, linear regression methods were used, and a feature selection was done to find the excitation frequencies of the eddy current sensor with the highest impact on the tearing. It was shown that the eddy current measurements along the coil contain valuable information about the tearing of the fine blanked part.

Keywords
Fine Blanking, Non-Destructive Testing, NDT, Eddy Current, Quality Prediction

Published online 4/24/2024, 10 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: ORTJOHANN Lucia, BECKER Marco, NIEMIETZ Philipp, BERGS Thomas, Material-data-driven prediction of sheared surface tears of fine blanked parts, Materials Research Proceedings, Vol. 41, pp 1416-1425, 2024

DOI: https://doi.org/10.21741/9781644903131-157

The article was published as article 157 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] Q. Zheng, X. Zhuang, Z. Zhao, State-of-the-art and future challenge in fine-blanking technology, Prod. Eng. Res. Devel. 13 (2019) 61–70. https://doi.org/10.1007/s11740-018-0839-7
[2] R.-A. Schmidt, Cold Forming and Fineblanking: A Handbook on Cold Processing Steel Material Properties Component Design, Carl Hanser, 2007.
[3] D. Harsch, P. Fischer, B. Berisha, J. Heingärtner, Y. Renkci, P. Hora, Considering fluctuations of material properties, stainless steel 1.4301, on manufacturability of kitchen sinks, IOP Conf. Ser.: Mater. Sci. Eng. 418 (2018) 12113. https://doi.org/10.1088/1757-899X/418/1/012113
[4] L. Ortjohann, M. Becker, P. Niemietz, T. Bergs, Monitoring of fluctuating material properties for optimizing sheet-metal forming processes: a systematic literature review, in: Materials Research Forum LLC, 2023. https://doi.org/10.21741/9781644902479-222
[5] J.M. Allwood, S.R. Duncan, J. Cao, P. Groche, G. Hirt, B. Kinsey, T. Kuboki, M. Liewald, A. Sterzing, A.E. Tekkaya, Closed-loop control of product properties in metal forming, CIRP Annals 65 (2016) 573–596. https://doi.org/10.1016/j.cirp.2016.06.002
[6] D. Banabic, G. Dragos, I. Bichis, Influence of Variability of Mechanical Data on Forming Limit Curves, Metal Forming 3 (2013) 858–863.
[7] V. Sturm, Einfluss von Chargenschwankungen auf die Verarbeitungsgrenzen von Stahlwerkstoffen. Zugl.: Erlangen-Nürnberg, Univ., Techn. Fak., Meisenbach, Bamberg, 2013.
[8] K. Gupta, N.K. Jain, R. Laubscher, Advanced Gear Manufacturing and Finishing: Classical and Modern Processes, Elsevier Academic Press, 2017. https://doi.org/10.1016/B978-0-12-804460-5.00004-3
[9] F. Klocke, Fertigungsverfahren 4, 6th ed., Springer Vieweg Berlin, Heidelberg, 2017. https://doi.org/10.1007/978-3-662-54714-4
[10] W. Volk, J. Stahl, Shear Cutting, in: L. Laperrière, G. Reinhart (Eds.), CIRP Encyclopedia of Production Engineering, Springer Berlin, Heidelberg, 2014. https://doi.org/10.1007/978-3-642-35950-7_16823-1
[11] F. Schweinshaupt, I.F. Weiser, T. Herrig, T. Bergs, Investigation of Combined Flat Coining and Fine Blanking of 16MnCr5 to Influence the Die Roll Formation, in: B.-A. Behrens, A. Brosius, W.-G. Drossel, W. Hintze, S. Ihlenfeldt, P. Nyhuis (Eds.), Production at the Leading Edge of Technology, Springer International Publishing, Cham, 2022, 112–121. https://doi.org/10.1007/978-3-030-78424-9_13
[12] H. Hoffmann, R. Neugebauer, G. Spur (Eds.), Handbuch Umformen, 2nd ed., Carl Hanser, München, 2012. https://doi.org/10.1007/978-3-446-43004-4
[13] M.D. Gram, R.H. Wagoner, Fineblanking of high strength steels: Control of material properties for tool life, Journal of Materials Processing Technology 211 (2011) 717–728. https://doi.org/10.1016/j.jmatprotec.2010.12.005
[14] J. Heingärtner, M. Born, P. Hora, Online Acquisition of Mechanical Material Properties of Sheet Metal for the Prediction of Product Quality by Eddy Current, 10th European Conference on Non-Destructive Testing, Moscow, Russia, 2010.
[15] B. Wolter, Y. Gabi, C. Conrad, Nondestructive Testing with 3MA—An Overview of Principles and Applications, Appl. Sci. 9 (2019) 1068. https://doi.org/10.3390/app9061068
[16] K. Herrmann, M. Irle, IMPOC: an online material properties measurement system, in: Flat- Rolled Steel Processes: Advanced Technologies, 2009.
[17] K. Lee, C. Hong, E.H. Lee, W. Yang, Comparison of Artificial Intelligence Methods for Prediction of Mechanical Properties, IOP Conf. Ser.: Mater. Sci. Eng. 967 (2020) 12031. https://doi.org/10.1088/1757-899X/967/1/012031
[18] A. Zoesch, T. Wiener, M. Kuhl, Zero Defect Manufacturing: Detection of Cracks and Thinning of Material during Deep Drawing Processes, Procedia CIRP 33 (2015) 179–184. https://doi.org/10.1016/j.procir.2015.06.033
[19] S.H. Khan, F. Ali, A. Nusair Khan, M.A. Iqbal, Eddy current detection of changes in stainless steel after cold reduction, Computational Materials Science 43 (2008) 623–628. https://doi.org/10.1016/j.commatsci.2008.01.034
[20] P. Fischer, D. Harsch, J. Heingärtner, Y. Renkci, P. Hora, A knowledge-based control system for the robust manufacturing of deep drawn parts, Procedia Eng. 207 (2017) 42–47. https://doi.org/10.1016/j.proeng.2017.10.735
[21] P. Fischer, J. Heingärtner, W. Aichholzer, D. Hortig, P. Hora, Feedback control in deep drawing based on experimental datasets, J. Phys.: Conf. Ser. 896 (2017) 12035. https://doi.org/10.1088/1742-6596/896/1/012035
[22] J. Heingärtner, P. Fischer, D. Harsch, Y. Renkci, P. Hora, Q-Guard – an intelligent process control system, J. Phys.: Conf. Ser. 896 (2017) 12032. https://doi.org/10.1088/1742-6596/896/1/012032
[23] H. Kim, J.C. Gu, L. Zoller, Control of the servo-press in stamping considering the variation of the incoming material properties, IOP Conf. Ser.: Mater. Sci. Eng. 651 (2019) 12062. https://doi.org/10.1088/1757-899X/651/1/012062
[24] S. Purr, Datenerfassung für die Anwendung lernender Algorithmen bei der Herstellung von Blechformteilen, FAU Studien aus dem Maschinenbau 338, 2020.
[25] Foerster Group, MAGNATEST D-HZP: Magnetinduktive Prüfung von Werkstoffen auf magnetische und elektrische Eigenschaften (2018).
[26] G. James, D. Witten, T. Hastie, R. Tibshirani, An introduction to statistical learning: With applications in R, Springer New York, NY, 2021. https://doi.org/10.1007/978-1-0716-1418-1
[27] T. Hastie, R. Tibshirani, J.H. Friedman, The elements of statistical learning: Data Mining, Inference, and Prediction, 2nd ed., Springer New York, NY, 2009. https://doi.org/10.1007/978-0-387-84858-7
[28] A. Agresti, Foundations of Linear and Generalized Linear Models, John Wiley & Sons, 2015.