Closed-loop control of product geometry by using an artificial neural network in incremental sheet forming with active medium
Source abstract
Abstract
Abstract A strategy to adjust the product geometry autonomously through an online control of the manufacturing process in incremental sheet forming with active medium is presented. An axial force sensor and a laser distance sensor are integrated into the process setup to measure the forming force and the product height, respectively. Experiments are conducted to estimate the bulging behavior for different pre-determined tool paths. An artificial neural network is consequently trained based on the experimental data to continuously predict the pressure levels required to control the final product height. The predicted pressure is part of a closed-loop control to improve the geometrical accuracy of formed parts. Finally, experiments were conducted to verify the results, where truncated cones with different dimensions were formed with and without the closed-loop control. The results indicate that this strategy enhances the geometrical accuracy of the parts and can potentially be expanded to be implemented for different types of material and geometries.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Surface Polishing Techniques · Laser and Thermal Forming Techniques · Metal Forming Simulation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (27)
Show source-provided references
1. Ben Khalifa N, Thiery S (2019) Incremental sheet forming with active medium. CIRP Ann Manuf Technol 68(1):313–316. https://doi.org/10.1016/j.cirp.2019.04.04310.1016/j.cirp.2019.04.043 ↗
2. Kumar Y, Kumar S (2018) Analysis of pressure assisted incremental sheet forming process through simulation. Int J Mech Prod Eng Res Dev 8:921–932. https://doi.org/10.24247/ijmperdjun20189810.24247/ijmperdjun201898 ↗
3. McLoughlin K, Cognot A, Quigley E (2003) Dieless manufacturing of sheet metal components with non rigid support. Proc SheMet 2003:123–130
4. Kumar Y, Kumar S (2019) Experimental and analytical evaluation of incremental sheet hydro-forming strategies to produce high forming angle sheets. Heliyon 5(6):e01801. https://doi.org/10.1016/j.heliyon.2019.e0180110.1016/j.heliyon.2019.e01801 ↗
5. Junk S (2003) Inkrementelle Blechumformung mit CNC-Werkzeugmaschinen: Verfahrensgrenzen und Umformstrategien. Saarland University, Saarbruecken
6. Ndip-Agbor E, Ehmann K, Cao J (2018) Automated flexible forming strategy for geometries with multiple features in double-sided incremental forming. J Manuf Sci Eng 140(3):88. https://doi.org/10.1115/1.403851110.1115/1.4038511 ↗
7. Bambach M (2008) Process strategies and modelling approaches for asymmetric incremental sheet forming. RWTH Aachen, Aachen
8. Hirt G, Ames J, Bambach M, Kopp R, Kopp R (2004) Forming strategies and process modelling for CNC incremental sheet forming. CIRP Ann Manuf Technol 53(1):203–206. https://doi.org/10.1016/S0007-8506(07)60679-910.1016/S0007-8506(07)60679-9 ↗
9. Ambrogio G, Costantino I, de Napoli L, Filice L, Fratini L, Muzzupappa M (2004) Influence of some relevant process parameters on the dimensional accuracy in incremental forming: a numerical and experimental investigation. J Mater Process Technol 153-154:501–507. https://doi.org/10.1016/j.jmatprotec.2004.04.13910.1016/j.jmatprotec.2004.04.139 ↗
10. Endelt B (2017) Design strategy for optimal iterative learning control applied on a deep drawing process. Int J Adv Manuf Technol 88(1-4):3–18. https://doi.org/10.1007/s00170-016-8501-z10.1007/s00170-016-8501-z ↗
11. Calmano S, Hesse D, Hoppe F, Groche P (2015) Evaluation of control strategies in forming processes. MATEC Web Conf 21:4002. https://doi.org/10.1051/matecconf/2015210400210.1051/matecconf/20152104002 ↗
12. Allwood JM, Duncan SR, Cao J, Groche P, Hirt G, Kinsey B, Kuboki T, Liewald M, Sterzing A, Tekkaya AE (2016) Closed-loop control of product properties in metal forming. CIRP Ann Manuf Technol 65(2):573–596. https://doi.org/10.1016/j.cirp.2016.06.00210.1016/j.cirp.2016.06.002 ↗
13. Allwood JM, Music O, Raithathna A, Duncan SR (2009) Closed-loop feedback control of product properties in flexible metal forming processes with mobile tools. CIRP Ann Manuf Technol 58(1):287–290. https://doi.org/10.1016/j.cirp.2009.03.06510.1016/j.cirp.2009.03.065 ↗
14. Filice L, Ambrogio G, Micari F (2006) On-line control of single point incremental forming operations through punch force monitoring. CIRP Ann Manuf Technol 55(1):245–248. https://doi.org/10.1016/S0007-8506(07)60408-910.1016/S0007-8506(07)60408-9 ↗
15. Tao F, Qi Q, Liu A, Kusiak A (2018) Data-driven smart manufacturing. J Manuf Syst 48:157–169. https://doi.org/10.1016/j.jmsy.2018.01.00610.1016/j.jmsy.2018.01.006 ↗
16. Chen JC, Chen JC (2005) An artificial-neural-networks-based in-process tool wear prediction system in milling operations. Int J Adv Manuf Technol 25(5-6):427–434. https://doi.org/10.1007/s00170-003-1848-y10.1007/s00170-003-1848-y ↗
17. Pal S, Heyns PS, Freyer BH, Theron NJ, Pal SK (2011) Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties. J Intell Manuf 22(4):491–504. https://doi.org/10.1007/s10845-009-0310-x10.1007/s10845-009-0310-x ↗
18. Kar AK (2015) A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network. J Comput Sci 6:23–33. https://doi.org/10.1016/j.jocs.2014.11.00210.1016/j.jocs.2014.11.002 ↗
19. Radetzky M, Rosebrock C, Bracke S (2019) Approach to adapt manufacturing process parameters systematically based on machine learning algorithms. IFAC-PapersOnLine 52:1773–1778. https://doi.org/10.1016/j.ifacol.2019.11.45810.1016/j.ifacol.2019.11.458 ↗
20. Essid O, Laga H, Samir C (2018) Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks. PLoS One 13(11):e0203192. https://doi.org/10.1371/journal.pone.020319210.1371/journal.pone.0203192 ↗
21. Heger J, Zein El Abdine M (2019) Using data mining techniques to investigate the correlation between surface cracks and flange lengths in deep drawn sheet metals. IFAC-PapersOnLine 52(13):851–856. https://doi.org/10.1016/j.ifacol.2019.11.23610.1016/j.ifacol.2019.11.236 ↗
22. Khan MS, Coenen F, Dixon C, el-Salhi S, Penalva M, Rivero A (2015) An intelligent process model: predicting springback in single point incremental forming. Int J Adv Manuf Technol 76(9-12):2071–2082. https://doi.org/10.1007/s00170-014-6431-110.1007/s00170-014-6431-1 ↗
23. Hartmann C, Opritescu D, Volk W (2019) An artificial neural network approach for tool path generation in incremental sheet metal free-forming. J Intell Manuf 30(2):757–770. https://doi.org/10.1007/s10845-016-1279-x10.1007/s10845-016-1279-x ↗
24. Kurra S, Hifzur Rahman N, Regalla SP, Gupta AK (2015) Modeling and optimization of surface roughness in single point incremental forming process. J Mater Res Technol 4(3):304–313. https://doi.org/10.1016/j.jmrt.2015.01.00310.1016/j.jmrt.2015.01.003 ↗
25. Ambrogio G, Filice L, Guerriero F, Guido R, Umbrello D (2011) Prediction of incremental sheet forming process performance by using a neural network approach. Int J Adv Manuf Technol 54(9-12):921–930. https://doi.org/10.1007/s00170-010-3011-x10.1007/s00170-010-3011-x ↗
26. Schwarzer M, Rogan B, Ruan Y, Song Z, Lee DY, Percus AG, Chau VT, Moore BA, Rougier E, Viswanathan HS, Srinivasan G (2019) Learning to fail: predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks. Comput Mater Sci 162:322–332. https://doi.org/10.1016/j.commatsci.2019.02.04610.1016/j.commatsci.2019.02.046 ↗
27. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90. https://doi.org/10.1145/306538610.1145/3065386 ↗