Abstract
The future mobility presents new safety requirements. In addition to the passenger compartment, batteries and hydrogen tanks need also to be intensively protected. High-strength, hot-formed steels, die-cast components and new material pairings pose new challenges for selecting and parametrization the used joining technology. For this reason, the Coupled Process Analysis (CPA) method, for supporting the design and monitoring of joining processes, is presented. From as early as 12 data sets from simulation or experiment (micrograph), it is possible to map interdependencies between the quality criteria of the join and the acting process parameters. The resulting possibilities are illustrated by means of the numerical design of a clinch and a riveted joint as well as the experimental, image-based sampling of a weld seam. Finally a supplemented by a presentation of the potentials in quality monitoring in series operation of the automotive process chain will be done. The combination of high flexibility with respect to the in- and output variables (visual images, FE-meshes, process curves, discrete values), low modeling effort and the image based representation of the interdependencies makes the approach suitable even for employees with lower qualification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Acherjee, B., Misra, D., Bose, D., Venkadeshwaran, K.: Prediction of weld strength and seam width for laser transmission welding of thermoplastic using response surface methodology. Opt. Laser Technol. 41(8), 956–967 (2009). https://doi.org/10.1016/j.optlastec.2009.04.007
Acherjee, B., Mondal, S., Tudu, B., Misra, D.: Application of artificial neural network for predicting weld quality in laser transmission welding of thermoplastics. Appl. Soft Comput. 11(2), 2548–2555 (2011). https://doi.org/10.1016/j.asoc.2010.10.005
Acherjee, B., Kuar, A.S., Mitra, S., Misra, D.: Modeling and analysis of simultaneous laser transmission welding of polycarbonates using an FEM and RSM combined approach. Opt. Laser Technol. 44(4), 995–1006 (2012). https://doi.org/10.1016/j.optlastec.2011.10.018
Anawa, E.M., Olabi, A.G.: Using Taguchi method to optimize welding pool of dissimilar laser-welded components. Opt. Laser Technol. 40(2), 379–388 (2008). https://doi.org/10.1016/j.optlastec.2007.07.001
Bäume, T., Zorn, W., Drossel, W.-G., Rupp, G.: Iterative process control and sensor evaluation for deep drawing tools with integrated piezoelectric actuators. Manuf. Rev. 3(H3) (2016). https://doi.org/10.1051/mfreview/2016002
Behrens, B.-A., Groche, P., Krüger, J., Wulfsberg, J.P.: WGP-Standpunkt Industriearbeitsplatz 2025. Technical Report, WGP Wissenschaftliche Gesellschaft für Produktionstechnik e.V., Hannover (2018)
Benyounis, K.Y., Olabi, A.G.: Optimization of different welding processes using statistical and numerical approaches—a reference guide. Adv. Eng. Softw. 39(6), 483–496 (2008). https://doi.org/10.1016/j.advengsoft.2007.03.012
Breckweg, A.: Automatisiertes und prozessüberwachtes Radialclinchen höherfester Blechwerkstoffe. Doctoral thesis, University of Stuttgart (2006)
Datta, S., Nandi, G., Bandyopadhyay, A., Kumar Pal, P.: Application of PCA-based hybrid Taguchi method for correlated multicriteria optimization of sub-merged arc weld. A case study. Int. J. Adv. Manuf. Technol. 45(3–4), 276–286 (2009). https://doi.org/10.1007/s00170-009-1976-0
Dhas, J.E.R., Dhas, S.J.H.: A review on optimization of welding process. Procedia Eng. 38, 544–554 (2012). https://doi.org/10.1016/j.proeng.2012.06.068
Drossel, W.-G., Falk, T., Israel, M., Jesche, F.: Unerring planning of clinching processes through the use of mathematical methods. KEM 611–612, 1437–1444 (2014). https://doi.org/10.4028/www.scientific.net/KEM.611-612.1437
Eşme, U.: Application of Taguchi method for the optimization of resistance spot welding process. Arabian J. Sci. Eng. 34(2B) (2009)
Gunaraj, V., Murugan, N.: Application of response surface methodology for predicting weld bead quality in submerged arc welding of pipes. J. Mater. Process. Technol. 88(1–3), 266–275 (1999). https://doi.org/10.1016/S0924-0136(98)00405-1
Hahn, O., Tan, Y.: Vorhersage des Tragverhaltens von Clinchverbindungen unter quasi-statischer Scherzugbelastung mittels eines neuronalen Netzes. Schweißen Und Schneiden 4, 138–143 (2003)
Hahn, O., Tan, Y., Voight, H.-M.: Maschinelles Lernen zur Vorhersage der Tragfähigkeit von Clinchverbindungen. UTF Sci. 1 (2005)
Hu, M., Lin, Z., Lai, X., Ni, J.: Simulation and analysis of assembly processes considering compliant, non-ideal parts and tooling variations. Int. J. Mach. Tools Manuf. 41(15), 2233–2243 (2001). https://doi.org/10.1016/S0890-6955(01)00044-X
Ismail, M.I.S., Okamoto, Y., Okada, A.: Neural network modeling for prediction of weld bead geometry in laser microwelding. Adv. Opt. Technol. 5, 1–7 (2013). https://doi.org/10.1155/2013/415837
Jäckel, M., Falk, T., Landgrebe, D.: Concept for further development of self-pierce riveting by using cyber physical systems. Procedia CIRP 44, 293–297 (2016). https://doi.org/10.1016/j.procir.2016.02.073
Jiang, P., Cao, L., Zhou, Q., Gao, Z., Rong, Y., Shao, X.: Optimization of welding process parameters by combining Kriging surrogate with particle swarm optimization algorithm. Int. J. Adv. Manuf. Technol. 86(9–12), 2473–2483 (2016). https://doi.org/10.1007/s00170-016-8382-1
Kriechenbauer, S., Müller, P., Mauermann, R., Drossel, W.-G.: Extension of process limits with bidirectional deep drawing: In: Behrens, B.A., Brosius, A., Hintze, W., Ihlenfeldt, S., Wulfsberg, J.P. (eds.) Production at the Leading Edge of Technology. WGP 2020. Lecture Notes in Production Engineering. Springer, Berlin, Heidelberg (2020). https://doi.org/10.1007/978-3-662-62138-7_10
Lambiase, F., Di Ilio, A.: Optimization of the clinching tools by means of integrated FE modeling and artificial intelligence techniques. Procedia CIRP 12, 163–168 (2013). https://doi.org/10.1016/j.procir.2013.09.029
Liao, B., Shi, Y., Cui, Y., Cui, S., Jiang, Z., Yi, Y.: Mathematical model for prediction and optimization of weld bead geometry in all-position automatic welding of Pipes. Metals 8(10), 756 (2018). https://doi.org/10.3390/met8100756
Lightfoot, M.P., Bruce, G.J., McPherson, N.A., Woods, K.: The application of artificial neural networks to weld-induced deformation in ship plate. Welding J. 23–30 (2004)
Mirapeix, J., García-Allende, P.B., Cobo, A., Conde, O.M., López-Higuera, J.M.: Real-time arc-welding defect detection and classification with principal component analysis and artificial neural networks. NDT & E Int. 40(4), 315–323 (2007). https://doi.org/10.1016/j.ndteint.2006.12.001
Oudjene, M., Ben-Ayed, L.: On the parametrical study of clinch joining of metallic sheets using the Taguchi method. Eng. Struct. 30(6), 1782–1788 (2008). https://doi.org/10.1016/j.engstruct.2007.10.017
Oudjene, M., Ben-Ayed, L., Delamézière, A., Batoz, J.: Shape optimization of clinching tools using the response surface methodology with moving least-square approximation. J. Mater. Process. Technol. 209(1), 289–296 (2009). https://doi.org/10.1016/j.jmatprotec.2008.02.030
Padmanaban, G., Balasubramanian, V.: Optimization of laser beam welding process parameters to attain maximum tensile strength in AZ31B magnesium alloy. Opt. Laser Technol. 42(8), 1253–1260 (2010). https://doi.org/10.1016/j.optlastec.2010.03.019
Pan, L.K., Wang, C.C., Hsiao, Y.C., Ho, K.C.: Optimization of Nd. YAG laser welding onto magnesium alloy via Taguchi analysis. Opt. Laser Technol. 37(1), 33–42 (2005). https://doi.org/10.1016/j.optlastec.2004.02.007
Purr, S., Meinhardt, J., Moelzl, K., Ostermair, M., Hagenah, H., Merklein, M.: Stamping plant 4.0—data mining for investigation and prediction of quality issues in manufacturing car body parts. In: Landgrebe, D., Drossel, W.-G., Putz, M. (eds.) 5th International Conference on Accuracy in Forming Technology and 22nd Saxon Conference on Forming Technology SFU 2015, pp. 623–642. Verlag Wissenschaftliche Scripten, Tagungsband; Auerbach (2015). https://doi.org/10.5120/12106-8375
Rao, P.S., Gupta, O.P., Murty, S.S.N., Rao, A.B.K.: Effect of process para-meters and mathematical model for the prediction of bead geometry in pulsed GMA welding. Int. J. Adv. Manuf. Technol. 45(5–6), 496–505 (2009). https://doi.org/10.1007/s00170-009-1991-1
Roux, E., Bouchard, P.: Kriging metamodel global optimization of clinching joining processes accounting for ductile damage. J. Mater. Process. Technol. 213(7), 1038–1047 (2013). https://doi.org/10.1016/j.jmatprotec.2013.01.018
Schwarz, C., Ackert, P., Mauermann, R.: Principal component analysis and singular value decomposition used for a numerical sensitivity analysis of a complex drawn part. Int. J. Adv. Manuf. Technol. 894, 2255–2265 (2018). https://doi.org/10.1007/s00170-017-0980-z
Schwarz, C., Kropp, T., Kraus, C., Drossel, W.-G.: Optimization of thick sheet clinching tools using principal component analysis. Int. J. Adv. Manuf. Technol. 106, 471–479 (2020). https://doi.org/10.1007/s00170-019-04512-5
Schwarz, C., Link, P., Ihlenfeldt, S., Drossel, W.-G.: Application of Fourier-related data reduction methods in sheet metal forming. Procedia CIRP 99, 206–265 (2021). https://doi.org/10.1016/j.procir.2021.03.038
Sivaraos Milkey, K.R., Samsudin, A.R., Dubey, A.K., Kidd, P.: Comparison between Taguchi method and response surface methodology (RSM) in modelling CO2 laser machining. Jordan J. Mech. Indust. Eng. 8(1), 35–42 (2014)
Srivastava, S., Garg, R.K.: Process parameter optimization of gas metal arc welding on IS:2062 mild steel using response surface methodology. J. Manuf. Process. 25, 296–305 (2017). https://doi.org/10.1016/j.jmapro.2016.12.016
Tan, Y., Hahn, O., Du, F., Voigt, H.: Non-destructive determination of the loading capacities of clinched DC 04 joints by kNN-regression. Mater. Test. 48(5), 211 (2006)
Thoms, V., Kalich, J.: Prozessvorhersage beim Stanznieten mit neuronalen Netzen, EFB-Forschungsbericht Nr. 179. EFB 2002, Hannover (2002)
Wahl, M., Schulz, F., Altermann, T., Eckert, A.: Die Prozesskettensimulation - Ein Beitrag zum virtuellen Karosseriebau. In: Neugebauer, Reimund, (eds) Karosserien fertigen - nachhaltig und effizient, pp. 259–269. Verlag Wissenschaftliche Scripten, Tagungsband; Zwickau (2011)
Wang, M., Xiao, G., Li, Z., Wang, J.: Shape optimization methodology of clinching tools based on Bezier curve. Int. J. Adv. Manuf. Technol. 24(1) (2017). https://doi.org/10.1007/s00170-017-0987-5
Xu, W.H., Lin, S.B., Fan, C.L., Zhuo, X.Q., Yang, C.L.: Statistical modelling of weld bead geometry in oscillating arc narrow gap all-position GMA welding. Int. J. Adv. Manuf. Technol. 72(9–12), 1705–1716 (2014). https://doi.org/10.1007/s00170-014-5799-2
Yang, Y., Gao, Z., Cao, L.: Identifying optimal process parameters in deep penetration laser welding by adopting Hierarchical-Kriging model. Infrared Phys. Technol. 92, 443–453 (2018). https://doi.org/10.1016/j.infrared.2018.07.006
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Schwarz, C., Ackert, P., Falk, T., Puschmann, M., Mauermann, R., Drossel, WG. (2022). Model-Based Joining Process Design for the Body Shop Process Chain. In: da Silva, L.F.M., Martins, P.A.F., Reisgen, U. (eds) 2nd International Conference on Advanced Joining Processes (AJP 2021). Proceedings in Engineering Mechanics. Springer, Cham. https://doi.org/10.1007/978-3-030-95463-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-95463-5_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95462-8
Online ISBN: 978-3-030-95463-5
eBook Packages: EngineeringEngineering (R0)