Prediction of tool failure in metal hot extrusion process using artificial neural networks
Mosa Almutahhar, Ali Alhajeri, Rashid Ali Laghari, Syed Sohail Akhtar, Usman Ali
download PDFAbstract. The variation of tool performance and nonuniform process parameters in metal forming are some of the factors that complicate the tool life modeling and analysis of such processes. In this work, a brief discussion about machine learning in analyzing metal extrusion process as well as tool life modeling, and an implemented work of using machine learning to predict failure modes for H13 Steel die used in 6063 Aluminum hot extrusion process is presented. The analysis is conducted on a set of steel dies used in 6063 aluminum hot extrusion process. The data for the failed dies used in this work is collected from a local hot extrusion manufacturer. Using artificial neural network, the prediction of the die failure modes was modeled. Moreover, the model’s accuracy and improvement recommendations are presented.
Keywords
Hot Extrusion, Die Failure, H13 Steel, 6063 Aluminum, Artificial Neural Network
Published online 9/25/2023, 8 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Mosa Almutahhar, Ali Alhajeri, Rashid Ali Laghari, Syed Sohail Akhtar, Usman Ali, Prediction of tool failure in metal hot extrusion process using artificial neural networks, Materials Research Proceedings, Vol. 36, pp 8-15, 2023
DOI: https://doi.org/10.21741/9781644902790-2
The article was published as article 2 of the book AToMech1-2023 Supplement
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] R. Sathish, S. Vasanthakumar, S. Sasikumar, and M. Yuvapparasath, “A Metal Forming By Hot Extrusion Process,” International Research Journal of Engineering and Technology, vol. 5, no. 7, pp. 1712–1714, 2018, [Online]. Available: www.irjet.net
[2] A. S. Chahare and K. H. Inamdar, “Optimization of Aluminium Extrusion Process using Taguchi Method,” IOSR Journal of Mechanical and Civil Engineering, vol. 17, no. 01, pp. 61–65, Mar. 2017. https://doi.org/10.9790/1684-17010016165
[3] S. Chen and X. Jiang, “A Review of Modeling and Control for Aluminum Extrusion,” in International Conference on Artificial Intelligence and Computer Science (AICS 2016), 2016, pp. 565–573.
[4] M. M. Marín, A. M. Camacho, and J. A. Pérez, “Influence of the temperature on AA6061 aluminum alloy in a hot extrusion process,” in Manufacturing Engineering Society International Conference 2017, MESIC 2017, 2017, vol. 13, pp. 327–334. https://doi.org/10.1016/j.promfg.2017.09.084
[5] S. N. A. Rahim, M. A. Lajis, and S. Ariffin, “Effect of extrusion speed and temperature on hot extrusion process of 6061 aluminum alloy chip,” ARPN Journal of Engineering and Applied Sciences, vol. 11, no. 4, pp. 2272–2277, 2016, [Online]. Available: https://www.researchgate.net/publication/306215537
[6] S. Jajimoggala, R. Dhananjay, V. Lakshmi, and Shabana, “Multi-response optimization of hot extrusion process parameters using FEM and Grey relation based Taguchi method,” in International Conference on Advances in Materials and Manufacturing Engineering, ICAMME-2018, 2019, vol. 18, pp. 389–401. [Online]. Available: www.sciencedirect.comwww.materialstoday.com/proceedings
[7] A. Medvedev, A. Bevacqua, A. Molotnikov, R. Axe, and R. Lapovok, “Innovative aluminium extrusion: Increased productivity through simulation,” in 18th International Conference Metal Forming, 2020, vol. 50, pp. 469–474. doi: 10.1016/j.promfg.2020.08.085.
[8] K. Lange, L. Cser, M. Geiger, and J. A. G. Kals, “Tool Life and Tool Quality in Bulk Metal Forming,” CIRP Ann Manuf Technol, vol. 41, no. 2, pp. 667–675, 1992. https://doi.org/10.1016/S0007-8506(07)63253-3
[9] S. S. Akhtar, A. F. M. Arif, and A. K. Sheikh, “Influence of Billet Quality on Hot Extrusion Die Life and its Relationship with Process Parameters,” in Advanced Materials Research, 2010, vol. 83–86, pp. 866–873. https://doi.org/10.4028/www.scientific.net/AMR.83-86.866
[10] S. Z. Qamar, “Fracture life prediction and sensitivity analysis for hollow extrusion dies,” Fatigue Fract Eng Mater Struct, vol. 38, no. 4, pp. 434–444, Apr. 2015. https://doi.org/10.1111/ffe.12244
[11] S. Z. Qamar, A. K. Sheikh, A. F. M. Arif, M. Younas, and T. Pervez, “Monte Carlo simulation of extrusion die life,” J Mater Process Technol, vol. 202, no. 1–3, pp. 96–106, Jun. 2008. https://doi.org/10.1016/j.jmatprotec.2007.08.062
[12] S. S. Akhtar and A. F. M. Arif, “Fatigue Failure of Extrusion Dies: Effect of Process Parameters and Design Features on Die Life,” Journal of Failure Analysis and Prevention, vol. 10, no. 1. pp. 38–49, Feb. 2010. https://doi.org/10.1007/s11668-009-9304-4
[13] T. Li, G. Zhao, C. Zhang, Y. Guan, X. Sun, and H. Li, “Effect of Process Parameters on Die Wear Behavior of Aluminum Alloy Rod Extrusion,” Materials and Manufacturing Processes, vol. 28, no. 3, pp. 312–318, Mar. 2013. https://doi.org/10.1080/10426914.2012.675536
[14] C. Redl et al., “Investigation and Numerical Modelling of Extrusion Tool Life Time,” in 7th International Tooling Conference, 2006, pp. 589–596.
[15] U. Ali, W. Muhammad, A. Brahme, O. Skiba, and K. Inal, “Application of artificial neural networks in micromechanics for polycrystalline metals,” Int. J. Plast., 2019.
[16] H. K. D. H. Bhadeshia, “Neural Networks in Materials Science,” ISIJ Int., vol. 39, no. 10, pp. 966–979, 1999.
[17] Y. C. Lin, J. Zhang, and J. Zhong, “Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel,” Comput. Mater. Sci., vol. 43, no. 4, pp. 752–758, 2008.
[18] Y. C. Lin, X. Fang, and Y. P. Wang, “Prediction of metadynamic softening in a multi-pass hot deformed low alloy steel using artificial neural network,” J. Mater. Sci., vol. 43, no. 16, pp. 5508–5515, 2008.
[19] W. Zhang, Z. Bao, S. Jiang, and J. He, “An artificial neural network-based algorithm for evaluation of fatigue crack propagation considering nonlinear damage accumulation,” Materials (Basel), 2016.
[20] A. Alshaiji, J. Albinmousa, M. Peron, B. AlMangour, and U. Ali, “Analyzing quasi-static fracture of notched magnesium ZK60 using notch fracture toughness and support vector machine,” Theoretical and Applied Fracture Mechanics, vol. 21, 2022.
[21] H. Baseri, M. Bakhshi-Jooybari, and B. Rahmani, “Modeling of spring-back in V-die bending process by using fuzzy learning back-propagation algorithm,” Expert Syst Appl, vol. 38, pp. 8894–8900, 2011.
[22] F. R. Biglari, N. P. O’Dowd, and R. T. Fenner, “Optimum design of forging dies using fuzzy logic in conjunction with the backward deformation method,” Int J Mach Tools Manuf, vol. 38, no. 8, pp. 981–1000, 1998.
[23] F. R. Bittencout and L. E. Zarate, “Hybrid structure based on previous knowledge and GA to search the ideal neurons quantity for the hidden layer of MLP-Application in the cold rolling process,” Appl Soft Comput, vol. 11, pp. 2460–2471, 2011.
[24] I. Zohourkari, S. Assarzadeh, and M. Zohoor, “Modeling and Analysis of Hot Extrusion Metal Forming Process Using Artificial Neural Network and ANOVA,” in 10th Biennial Conference on Engineering Systems Design and Analysis, 2010. [Online]. Available: https://proceedings.asmedigitalcollection.asme.org/pdfaccess.ashx?url=/data/conferences/esda2010/72271/
[25] S. Nanne Saheb and S. Kumanan, “Modeling of Hot extrusion process using Artificial Neural Networks implanted with Genetic Algorithm,” in National Symposium on Advances in Metal Forming, 2003.
[26] K. H. Raj, R. S. Sharma, S. Srivastava, and C. Patvardhan, “Optimization of Hot Extrusion using Single Objective Neuro Stochastic Search Technique,” in Proceedings of IEEE International Conference on Industrial Technology, 2000. https://doi.org/10.1109/ICIT.2000.854248
[27] G. Zhao, H. Chen, C. Zhang, and Y. Guan, “Multiobjective optimization design of porthole extrusion die using Pareto-based genetic algorithm,” International Journal of Advanced Manufacturing Technology, vol. 69, no. 5–8, pp. 1547–1556, Nov. 2013. https://doi.org/10.1007/s00170-013-5124-5
[28] S. Butdee and S. Tichkiewitch, “Case-Based Reasoning for Adaptive Aluminum Extrusion Die Design Together with Parameters by Neural Networks,” in Global Product Development – Proceedings of the 20th CIRP Design Conference, 2011, pp. 491–496. https://doi.org/10.1007/978-3-642-15973-2_50
[29] A. F. M. Arif, A. K. Sheikh, and S. Z. Qamar, “A study of die failure mechanisms in aluminum extrusion,” J Mater Process Technol, vol. 134, no. 3, pp. 318–328, Mar. 2003. https://doi.org/10.1016/S0924-0136(02)01116-0
[30] S. Z. Qamar, A. K. Sheikh, T. Pervez, and A. F. M. Arif, “Using Monte Carlo Simulation for Prediction of Tool Life,” in Applications of Monte Carlo Method in Science and Engineering, Prof. Shaul Mordechai, Ed. InTech, 2011, pp. 881–900. [Online]. Available: www.intechopen.com
[31] D. Lepadatu, R. Hambli, A. Kobi, and A. Barreau, “Tool Life Prediction in Metal Forming Processes Using Numerical Analysis,” IFAC Proceedings Volumes, vol. 37, no. 15, pp. 287–291, 2004. https://doi.org/10.1016/s1474-6670(17)31038-8
[32] B. Mahesh, “Machine Learning Algorithms – A Review,” International Journal of Science and Research (IJSR), vol. 9, no. 1, 2020. https://doi.org/10.21275/ART20203995
[33] D. Y. Pimenov, A. Bustillo, S. Wojciechowski, V. S. Sharma, M. K. Gupta, and M. Kuntoğlu, “Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review,” Journal of Intelligent Manufacturing. Springer, 2022. https://doi.org/10.1007/s10845-022-01923-2
[34] C. C. Antonio, C. F. Castro, and L. C. Sousa, “Eliminating Forging Defects Using Genetic Algorithms,” Materials and Manufacturing Processes, vol. 20, no. 3, pp. 509–522, 2005.
[35] J. Karandikar, “Machine learning classification for tool life modeling using production shop-floor tool wear data,” in 47th SME North American Manufacturing Research Conference, NAMRC 47, 2019, vol. 34, pp. 446–454. https://doi.org/10.1016/j.promfg.2019.06.192
[36] L. N. Pattanaik, “Applications of Soft computing tools in Metal forming: A state-of-art review,” Journal of Machining & Forming Technologies, vol. 5, 2013.
[37] T. Wuest, D. Weimer, C. Irgens, and K. D. Thoben, “Machine learning in manufacturing: advantages, challenges, and applications,” Prod Manuf Res, vol. 4, no. 1, pp. 23–45, Jun. 2016. https://doi.org/10.1080/21693277.2016.1192517
