Estimation of friction stir welding parameters of 3D printed sheets using ANN

Year 2024, , 176 - 187, 15.01.2024

Nergizhan Anaç , Oğuz Koçar , Erhan Baysal

https://doi.org/10.28948/ngumuh.1295673

Abstract

In this study, sheets were printed from PLA Wood and PLA-CF filaments using a 3D printer. The prepared sheets were welded using the FSW method. The process parameters were selected as different mixed tip geometries, tool feed rate, and rotational tool speed. The data set obtained from the experiments was used to train and test artificial neural networks. To optimize the ANN model, 30 models were created by changing the training algorithm, transfer function, and the number of neurons in the hidden layer. The training and test results of the models were statistically evaluated. As a result, in the best prediction model (trainlm, number of neurons 14 and tansig), the training result was 96% accurate and the test result was 99% accurate. The training and testing results of the ANN model show that ANN can be used to estimate the value of process parameters in the FSW method.

Keywords

Additive manufacturing, 3D printing, Thermoplastic material, Filler reinforced filament, Friction stir welding, Artificial neural networks (ANN)

3B yazıcıda üretilen plakaların sürtünme karıştırma kaynak parametrelerinin YSA ile tahmini

Abstract

Bu çalışmada 3B yazıcı kullanılarak PLA Wood (Ahşap katkılı polilaktik Asit) ve PLA-CF (karbon fiber katkılı polilaktik Asit) filamentlerden plakalar basılmıştır. Hazırlanan plakalar SKK metodu kullanılarak birleştirilmiştir. SKK metodunda işlem parametreleri farklı karıştırıcı uç geometrisi (kare, üçgen ve vida), takım ilerleme hızı (20, 40 ve 60 mm/dk) ve takım dönme hızı (1250, 1750 ve 2250 dev/dk) olarak seçilmiştir. Yapılan deneyler sonucunda elde edilen veri seti (54 deney) yapay sinir ağlarının eğitim ve testi için kullanılmıştır. YSA modelinin optimizasyonu için eğitim algoritması, transfer fonksiyonu ve gizli katmandaki nöron sayısı değiştirilerek 30 model oluşturulmuştur. Modellerin eğitim ve test sonuçları istatistiksel olarak değerlendirilmiştir. Sonuç olarak en iyi tahmin modelinde (trainlm, nöron sayısı 14 ve tansig) eğitim sonucu %99, test sonucu %96 doğrulukla yapılmıştır. YSA modelinin eğitim ve test sonuçları, YSA'nın SKK metodunda işlem parametrelerinin değerini tahmin etmek için kullanılabileceğini göstermektedir.

Keywords

Eklemeli imalat, 3B yazdırma, Termoplastik malzeme, Dolgu takviyeli filament, Sürtünme karıştırma kaynağı, Yapay sinir ağları (YSA)

References

• M. R. Hajideh, M. Farahani, S. A. D Alavi, N. M. Ramezani, Investigation on the effects of tool geometry on the microstructure and the mechanical properties of dissimilar friction stir welded polyethylene and polypropylene sheets. Journal of Manufacturing Processes, 26, 269-279, 2017. https://doi.org/10.1016/j.jmapro.2017.02.018
• S.H. Dashatan, T. Azdast, S. R. Ahmadi, A. Bagheri, Friction stir spot welding of dissimilar polymethyl methacrylate and acrylonitrile butadiene styrene sheets. Materials & Design, 45, 135-141, 2013. https://doi.org/10.1016/j.matdes.2012.08.071
• P. J. Bates, C. Dyck, and M. Osti, Vibration welding of nylon 6 to nylon 66. Polymer Engineering & Science, 44(4), 760-771, 2004. https://doi.org/10.1002/pen.20068
• M. Farahani, I. S. Far, D. Akbari and R. Alderliesten, Numerical and experimental investigations of effects of residual stresses on crack behavior in Aluminum 6082-T6. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 226(9), 2178-2191, 2012. https://doi.org/10.1177/095440621143266
• M. Tabasi, M. Farahani, M. K. B. Givi, M. Farzami and A. Moharami, Dissimilar friction stir welding of 7075 aluminum alloy to AZ31 magnesium alloy using SiC nanoparticles. The International Journal of Advanced Manufacturing Technology, 86, 705-715, 2016. https://doi.org/10.1007/s00170-015-8211-y
• H. M. Jamalian, M. Farahani, M. K. B. Givi and M. A. Vafaei, Study on the effects of friction stir welding process parameters on the microstructure and mechanical properties of 5086-H34 aluminum welded joints. The International Journal of Advanced Manufacturing Technology, 83, p. 611-621, 2016. https://doi.org/10.1007/s00170-015-7581-5
• V. Jaiganesh, B. Maruthu, and E. Gopinath, Optimization of process parameters on friction stir welding of high density polypropylene plate. Procedia Engineering, 97, 1957-1965, 2014. https://doi.org/10.1016/j.proeng.2014.12.350
• W. M. Thomas, Friction stir butt welding. International patent application No.9125978.8, 6 December 1991.
• W. K. Kim, B. C. Goo and S. T. Won, Optimal design of friction stir welding process to improve tensile force of the joint of A6005 extrusion. Materials and Manufacturing Processes, 25(7), 637-643, 2010. https://doi.org/10.1080/10426910903365745
• A. Mirjalili, S. Serajzadeh, H. J. Aval and A. H. Kokabi, Modeling and experimental study on friction stir welding of artificially aged AA2017 plates. Materials and Manufacturing Processes, 28(6), 683-688, 2013. https://doi.org/10.1080/10426914.2012.746782
• Y. Bozkurt, The optimization of friction stir welding process parameters to achieve maximum tensile strength in polyethylene sheets. Materials & Design, 35, 440-445, 2012. https://doi.org/10.1016/j.matdes.2011.09.008
• S. Saeedy and M. K. B. Givi, Experimental application of friction stir welding (FSW) on thermo plastic medium density polyethylene blanks. ASME 2010 10th Biennal Conference on Engineering Systems Design and Analysis, pp. 841-844, Istanbul, Turkey, 2010. https://doi.org/10.1115/ESDA2010-25344
• A. K. R. Sharma, M. R. Choudhury and K. Debnath, Experimental investigation of friction stir welding of PLA. Welding in The World, 64(6), 1011-1021, 2020. https://doi.org/10.1007/s40194-020-00890-7
• R. Kumar, N. Ranjan, V. Kumar, J. S. Chokan, A. Yadav, N. Piyush, S. Sharma, C. Prakash, S. Singh, C. Li, Characterization of friction stir-welded polylactic acid/aluminum composite primed through fused filament fabrication. Journal of Materials Engineering and Performance, 31(3), 2391-2409, 2022. https://doi.org/10.1007/s11665-021-06329-4
• S. M. Senthil and M.B. Kumar, Effect of tool rotational speed and traverse speed on friction stir welding of 3d-printed polylactic acid material. Applied Science and Engineering Progress, 15(1), 2022. https://doi.org/10.14416/j.asep.2021.09.002
• N. Vidakis, M. Petousis, N. Mountakis and J. D. Kechagias, Material extrusion 3D printing and friction stir welding: an insight into the weldability of polylactic acid plates based on a full factorial design. The International Journal of Advanced Manufacturing Technology, 121(5-6), 3817-3839, 2022. https://doi.org/10.1007/s00170-022-09595-1
• M. A. Rezgui, M. Ayadi, A. Cherouat, K. Hamrouni, A. Zghal and S. Bejaoui, Application of Taguchi approach to optimize friction stir welding parameters of polyethylene. EPJ Web of Conferences 6, 07003 pp. 1-8, Poitiers, France, 2010. http://dx.doi.org/10.1051/epjconf/20100607003
• E. Raouache, Z. Boumerzoug, S. Rajakumar and F. Khalfallah, Effect of FSW process parameters on strength and peak temperature for joining high-density polyethylene (HDPE) sheets. Revue des Composites et des Materiaux Avances, 28(2), 149, 2018. https://doi.org/10.3166/RCMA.28.149-160
• M. R. Hajideh, M. Farahani, S. A. D. Alavi and N. M. Ramezani, Investigation on the effects of tool geometry on the microstructure and the mechanical properties of dissimilar friction stir welded polyethylene and polypropylene sheets. Journal of Manufacturing Processes, 26, 269-279, 2017. https://doi.org/10.1016/j.jmapro.2017.02.018
• F. Kordestani, F. A. Ghasemi and N. B. M. Arab, Effect of pin geometry on the mechanical strength of friction-stir-welded polypropylene composite plates. Mechanics of Composite Materials, 53(4), 525-532, 2017. https://doi.org/10.1007/s11029-017-9682-8
• Incorporated. P.S. 3 Types of Plastic Used in 3D Printing.https://www.polymersolutions.com/blog/plastic-in-3d-printing/, Accessed 12 February 2023.
• H. Kyutoku, N. Maeda, H. Sakamoto, H. Nishimura and K. Yamada, Effect of surface treatment of cellulose fiber (CF) on durability of PLA/CF bio-composites. Carbohydrate Polymers, 203, 95-102, 2019. https://doi.org/10.1016/j.carbpol.2018.09.033
• R.M. Rasal, A.V. Janorkar and D.E. Hirt, Poly (lactic acid) modifications. Progress in Polymer Science, 35(3), 338-356, 2010. https://doi.org/10.1016/j.progpolymsci.2009.12.003
• D. Garlotta, A literature review of poly (lactic acid). Journal of Polymers and The Environment, 9, 63-84, 2001. https://doi.org/10.1023/A:1020200822435
• L. T. Lim, R. Auras and M. Rubino, Processing technologies for poly (lactic acid). Progress in Polymer Science, 33(8), 820-852, 2008. https://doi.org/10.1016/j.progpolymsci.2008.05.004
• A. E. Magri, K. E. Mabrouk, S. Vaudreuil and M. E. Touhami, Mechanical properties of CF-reinforced PLA parts manufactured by fused deposition modeling. Journal of Thermoplastic Composite Materials, 34(5), 581-595, 2021. https://doi.org/10.1177/0892705719847244
• N. Ayrilmis, M. Kariz, J. H. Kwon and M. K. Kuzman, Effect of printing layer thickness on water absorption and mechanical properties of 3D-printed wood/PLA composite materials. The International Journal of Advanced Manufacturing Technology, 102, 2195-2200, 2019. https://doi.org/10.1007/s00170-019-03299-9
• Filameon PLA-CF 15 Filament. https://www.filameon.com/urun/filameon-pla-cf-15-filament/#%20, Accessed 6 September 2022.
• Filameon PLA Wood. https://www.filameon.com/urun/filameon-pla-wood-filament/, Accessed 5 November 2022.
• B. Erdoğan, M. Bağatur, G. Göktürk, S. O. Yavuz, T. B. Yılmaz, O. Koçar and O. Özdamar, Experimental investigation of tensile strength and thermal conductivity of nanoparticle reinforcement composite materials. Journal of Materials and Manufacturing, 1(1), 14-22, 2022. https://doi.org/10.5281/zenodo.7107345
• V. Devuri, M. M. Mahapatra, S. P. Harsha and N. R. Mandal, Effect of shoulder surface dimension and geometries on FSW of AA7039. Journal for Manufacturing Science and Production, 14(3), 183-194, 2014. https://doi.org/10.1515/jmsp-2014-0008
• N. R. J. Hynes and P. S. Velu, Effect of rotational speed on Ti-6Al-4V-AA 6061 friction welded joints. Journal of Manufacturing Processes, 32, 288-297, 2018. https://doi.org/10.1016/j.jmapro.2018.02.014
• H. Lombard, D. G. Hatting, A. Steuwer and M. N. James, Effect of process parameters on the residual stresses in AA5083-H321 friction stir welds. Materials Science and Engineering: A, 501(1-2), 119-124, 2009. https://doi.org/10.1016/j.msea.2008.09.078
• S. Rajakumar, C. Muralidharan and V. Balasubramanian, Influence of friction stir welding process and tool parameters on strength properties of AA7075-T6 aluminium alloy joints. Materials & Design, 32(2), 535-549, 2011. https://doi.org/10.1016/j.matdes.2010.08.025
• T. Sun, A. P. Reynolds, M. J. Roy, P. J. Withers and P. B. Prangnell, The effect of shoulder coupling on the residual stress and hardness distribution in AA7050 friction stir butt welds. Materials Science and Engineering: A, 735, 218-227, 2018. https://doi.org/10.1016/j.msea.2017.12.018
• N. Sharma, A. N. Siddiquee, Z. A. Khan and M. T. Mohammed, Material stirring during FSW of Al–Cu: Effect of pin profile. Materials and Manufacturing Processes, 33(7), 786-794, 2018. https://doi.org/10.1080/10426914.2017.1388526
• P. S. Kumar and M. S. Chander, Effect of tool pin geometry on FSW dissimilar aluminum alloys-(AA5083 & AA6061). Materials Today: Proceedings, 39, 472-477, 2021. https://doi.org/10.1016/j.matpr.2020.08.204
• A. Arici and S. Selale, Effects of tool tilt angle on tensile strength and fracture locations of friction stir welding of polyethylene. Science and Technology of Welding and Joining, 12(6), 536-539, 2007. https://doi.org/10.1179/174329307X173706
• J. P. Davim, V. N. Gaitonde and S. R. Karnik, Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models. Journal of Materials Processing Technology, 205(1-3), 16-23, 2008. https://doi.org/10.1016/j.jmatprotec.2007.11.082
• C. Hamzaçebi, Forecasting of Turkey's net electricity energy consumption on sectoral bases. Energy Policy, 35(3), 2009-2016, 2007. https://doi.org/10.1016/j.enpol.2006.03.014
• C. Hamzaçebi, Improving artificial neural networks’ performance in seasonal time series forecasting. Information Sciences, 178(23), 4550-4559, 2008. https://doi.org/10.1016/j.ins.2008.07.024
• G. Serin, M. Kahya, M. Özbayoğlu ve H. Ö. Ünver, Ti6Al4V malzemesinin tornalama işleminde özgül kesme enerjisi ve yüzey pürüzlüğünün incelenmesi ve yapay sinir ağlari temelli tahmin modeli geliştirilmesi. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 24(2), 517-536, 2019. https://doi.org/10.17482/uumfd.301128
• E. N. Dizdar and O. Koçar, İş sağlığı ve güvenliği yönetim sistemlerinde risklerin yapay sinir ağlarıyla değerlendirilmesi. Academic Platform-Journal of Engineering and Science, 6(3), 73-83, 2018. https://doi.org/10.21541/apjes.426502
• H. R. Maier, A. Jain, G. C. Dandy and K. P. Sudheer, Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental Modelling & Software, 25(8), 891-909, 2010. https://doi.org/10.1016/j.envsoft.2010.02.003
• S. Razavi and B.A. Tolson, A new formulation for feedforward neural networks. IEEE Transactions on Neural Networks, 22(10), 1588-1598, 2011. doi: 10.1109/TNN.2011.2163169.
• A. B. Colak, Experimental study for thermal conductivity of water‐based zirconium oxide nanofluid: developing optimal artificial neural network and proposing new correlation. International Journal of Energy Research, 45(2), 2912-2930, 2021. https://doi.org/10.1002/er.5988
• H. K. Sezer, O. Eren, ve H. R. Börklü, Ergiyik biriktirme yöntemi ile karbon fiber takviyeli plastik kompozitlerin eklemeli imalatı: İşlem parametrelerinin çekme özelliklerine etkisi. 2nd International Symposium on 3D Printing Technologies, İstanbul, Türkiye, 3-4 April 2017.
• G. Faludi, G. Dora, K. Renner, J. Móczó, B. Pukánszky, Improving interfacial adhesion in pla/wood biocomposites. Composites Science and Technology, 89, 77-82, 2013. https://doi.org/10.1016/j.compscitech.2013.09.009.
• Á. Csikós, G. Faludi, A. Domján, K. Renner, J. Móczó and B. Pukánszky, Modification of interfacial adhesion with a functionalized polymer in PLA/wood composites. European Polymer Journal, 68, 592-600, 2015. https://doi.org/ 10.1016 /j.eurpolymj.2015.03.032.
• Z. Liu, Q. Lei and S. Xing, Mechanical characteristics of wood, ceramic, metal and carbon fiber-based PLA composites fabricated by FDM. Journal of Materials Research and Technology, 8 (5), 3741 - 3751, 2019. https://doi.org/ 10.1016/j.jmrt.2019.06.034.
• A. Abdullah ve M. Karabatak, Veri seti-sınıflandırma ilişkisinde performansa etki eden faktörlerin değerlendirilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32(2), 531-540, 2020. https://doi.org/10.35234/fumbd.738007
• H. Okuyucu, A. Kurt and E. Arcaklioglu, Artificial neural network application to the friction stir welding of aluminum plates. Materials & Design, 28 (1), 78-84, 2007. https://doi.org/10.1016/j.matdes.2005.06.003.
• M. Krishnan, J. Maniraj, R. Deepak and K. Anganan, Prediction of optimum welding parameters for FSW of aluminium alloys AA6063 and A319 using RSM and ANN. Materials Today: Proceedings, 5 (1), 716-723, 2018. https://doi.org/10.1016/j.matpr.2017.11.138.
• A.K. Lakshminarayanan, V. Balasubramanian, Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints. Transactions of Nonferrous Metals Society of China, 19 (1), 9-18, 2009. https://doi.org/10.1016/S1003-6326(08)60221-6.
• B. Eren, M.A. Guvenc and S. Mistikoglu, Artificial Intelligence Applications for Friction Stir Welding: A Review. Metals and Materials International, 27, 193–219, 2021. https://doi.org/10.1007/s12540-020-00854-y.