Information Retrieval in Friction Stir Welding of Aluminum Alloys by using Natural Language Processing based Algorithms
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Abstrakt
Text summarization is a technique for condensing a big piece of text into a few key elements that give a general impression of the content. When someone requires a quick and precise summary of a large amount of information, it becomes vital. If done manually, summarizing text can be costly and time-consuming. Natural Language Processing (NLP) is the sub-division of Artificial Intelligence that narrows down the gap between technology and human cognition by extracting the relevant information from the pile of data. In the present work, scientific information regarding the Friction Stir Welding of Aluminium alloys was collected from the abstract of scholarly research papers. For extracting the relevant information from these research abstracts four Natural Language Processing based algorithms i.e. Latent Semantic Analysis (LSA), Luhn Algorithm, Lex Rank Algorithm, and KL-Algorithm were used. In order to evaluate the accuracy score of these algorithms, Recall-Oriented Understudy for Gisting Evaluation (ROUGE) was used. The results showed that the Luhn Algorithm resulted in the highest f1-Score of 0.413 in comparison to other algorithms.
Pobrania
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Artykuły czasopisma Welding Technology Review (Przegląd Spawalnictwa) publikowane są w otwartym dostępie na licencji CC BY (licencja Creative Commons Uznanie autorstwa 4.0 Międzynarodowe). Licencja CC BY jest najbardziej otwartą dostępną licencją i uważaną za „złoty standard” w formule otwartego dostępu; jest również preferowany przez wielu fundatorów badań. Licencja ta umożliwia czytelnikom kopiowanie i redystrybucję materiału na dowolnym nośniku i w dowolnym formacie, a także zmienianie, przekształcanie lub budowanie na nim materiału, w tym do użytku komercyjnego, pod warunkiem wskazania oryginalnego autora.
Bibliografia
Goli, A.; Zare, H.K.; Tavakkoli-Moghaddam, R.; Sadeghieh, A. Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm. Numer. Algebr. Control Optim. 2019, 9, 187–209, doi:10.3934/naco.2019014
Goli, A.; Tirkolaee, E.B.; Aydin, N.S. Fuzzy Integrated Cell Formation and Production Scheduling Considering Automated Guided Vehicles and Human Factors. IEEE Trans. Fuzzy Syst. 2021, 29, 3686–3695, doi:10.1109/TFUZZ.2021.3053838
Goli, A.; Malmir, B. A Covering Tour Approach for Disaster Relief Locating and Routing with Fuzzy Demand. Int. J. Intell. Transp. Syst. Res. 2020, 18, 140–152, doi:10.1007/s13177-019-00185-2
Pahlevan, S.M.; Hosseini, S.M.S.; Goli, A. Sustainable supply chain network design using products’ life cycle in the aluminum industry. Environ. Sci. Pollut. Res. 2021, doi:10.1007/s11356-020-12150-8
Goli, A.; Khademi-Zare, H.; Tavakkoli-Moghaddam, R.; Sadeghieh, A.; Sasanian, M.; Malekalipour Kordestanizadeh, R. An integrated approach based on artificial intelligence and novel meta-heuristic algorithms to predict demand for dairy products: a case study. Netw. Comput. Neural Syst. 2021, 32, 1–35, doi:10.1080/0954898X.2020.1849841
Chowdhury, G.G. Natural language processing. Annu. Rev. Inf. Sci. Technol. 2003, 37, 51–89, doi:10.1002/aris.1440370103
Nadkarni, P.M.; Ohno-Machado, L.; Chapman, W.W. Natural language processing: An introduction. J. Am. Med. Informatics Assoc. 2011, doi:10.1136/amiajnl-2011-000464.
Manning, C.D.; Surdeanu, M.; Bauer, J.; Finkel, J.; Bethard, S.J.; McClosky, D. The stanford CoreNLP natural language processing toolkit. In Proceedings of the Proceedings of the Annual Meeting of the Association for Computational Linguistics; 2014.
Steven, B.; Ewan, K.; Edward, L. Natural Language Processing with Python; University of Melbourne, 2010; ISBN ISBN 978-0-596-51649-9
Meystre, S.; Haug, P.J. Automation of a problem list using natural language processing. BMC Med. Inform. Decis. Mak. 2005, 5, doi:10.1186/1472-6947-5-30
Shetty, P.; Ramprasad, R. Automated knowledge extraction from polymer literature using natural language processing. iScience 2021, 24, 101922, doi:10.1016/j.isci.2020.101922
Venugopal, V.; Sahoo, S.; Zaki, M.; Agarwal, M.; Gosvami, N.N.; Krishnan, N.M.A. Looking through glass: Knowledge discovery from materials science literature using natural language processing. Patterns 2021, doi:10.1016/j.patter.2021.100290
Rhodes, C.G.; Mahoney, M.W.; Bingel, W.H.; Spurling, R.A.; Bampton, C.C. Effects of friction stir welding on microstructure of 7075 aluminum. Scr. Mater. 1997, 36, 69–75, doi:10.1016/S1359-6462(96)00344-2
Sato, Y.S.; Kokawa, H.; Enomoto, M.; Jogan, S. Microstructural evolution of 6063 aluminum during friction-stir welding. Metall. Mater. Trans. A Phys. Metall. Mater. Sci. 1999, 30, 2429–2437, doi:10.1007/s11661-999-0251-1
Watanabe, T.; Takayama, H.; Yanagisawa, A. Joining of aluminum alloy to steel by friction stir welding. J. Mater. Process. Technol. 2006, 178, 342–349, doi:10.1016/j.jmatprotec.2006.04.117
Kwon, Y.J.; Shigematsu, I.; Saito, N. Dissimilar friction stir welding between magnesium and aluminum alloys. Mater. Lett. 2008, 62, 3827–3829, doi:10.1016/j.matlet.2008.04.080
Benavides, S.; Li, Y.; Murr, L.E.; Brown, D.; McClure, J.C. Low-temperature friction-stir welding of 2024 aluminum. Scr. Mater. 1999, 41, 809–815, doi:10.1016/S1359-6462(99)00226-2
Skowrońska, B.; Chmielewski, T.; Zasada, D. Assessment of Selected Structural Properties of High-Speed Friction Welded Joints Made of Unalloyed Structural Steel. Materials (Basel). 2023, 16, 93, doi:10.3390/ma16010093
Hu, Z.L.; Wang, X.S.; Pang, Q.; Huang, F.; Qin, X.P.; Hua, L. The effect of postprocessing on tensile property and microstructure evolution of friction stir welding aluminum alloy joint. Mater. Charact. 2015, 99, 180–187, doi:10.1016/j.matchar.2014.11.015
Kwon, Y.J.; Shim, S.B.; Park, D.H. Friction stir welding of 5052 aluminum alloy plates. Trans. Nonferrous Met. Soc. China (English Ed. 2009, 19, 23–27, doi:10.1016/S1003-6326(10)60239-7
Chen, Y.; Liu, H.; Feng, J. Friction stir welding characteristics of different heat-treated-state 2219 aluminum alloy plates. Mater. Sci. Eng. A 2006, 420, 21–25, doi:10.1016/j.msea.2006.01.029
Chen, H. Bin; Yan, K.; Lin, T.; Chen, S. Ben; Jiang, C.Y.; Zhao, Y. The investigation of typical welding defects for 5456 aluminum alloy friction stir welds. Mater. Sci. Eng. A 2006, 433, 64–69, doi:10.1016/j.msea.2006.06.056
Chen, Y.C.; Feng, J.C.; Liu, H.J. Precipitate evolution in friction stir welding of 2219-T6 aluminum alloys. Mater. Charact. 2009, 60, 476–481, doi:10.1016/j.matchar.2008.12.002
SHARMA, N.; KHAN, Z.A.; SIDDIQUEE, A.N. Friction stir welding of aluminum to copper—An overview. Trans. Nonferrous Met. Soc. China (English Ed. 2017, 27, 2113–2136, doi:10.1016/S1003-6326(17)60238-3
Amini, S.; Amiri, M.R.; Barani, A. Investigation of the effect of tool geometry on friction stir welding of 5083-O aluminum alloy. Int. J. Adv. Manuf. Technol. 2015, 76, 255–261, doi:10.1007/s00170-014-6277-6
Hirata, T.; Oguri, T.; Hagino, H.; Tanaka, T.; Chung, S.W.; Takigawa, Y.; Higashi, K. Influence of friction stir welding parameters on grain size and formability in 5083 aluminum alloy. Mater. Sci. Eng. A 2007, 456, 344–349, doi:10.1016/j.msea.2006.12.079
Li, Y.; Murr, L.E.; McClure, J.C. Flow visualization and residual microstructures associated with the friction-stir welding of 2024 aluminum to 6061 aluminum. Mater. Sci. Eng. A 1999, 271, 213–223, doi:10.1016/S0921-5093(99)00204-X
Sato, Y.S.; Urata, M.; Kokawa, H. Parameters controlling microstructure and hardness during friction-stir welding of precipitation-hardenable aluminum alloy 6063. Metall. Mater. Trans. A 2002, 33, 625–635, doi:10.1007/s11661-002-0124-3
Elrefaey, A.; Gouda, M.; Takahashi, M.; Ikeuchi, K. Characterization of aluminum/steel lap joint by friction stir welding. J. Mater. Eng. Perform. 2005, 14, 10–17, doi:10.1361/10599490522310
Yu, P.; Wu, C.S.; Shi, L. Analysis and characterization of dynamic recrystallization and grain structure evolution in friction stir welding of aluminum plates. Acta Mater. 2021, 207, 116692, doi:10.1016/j.actamat.2021.116692
Abdollahzadeh, A.; Bagheri, B.; Abbasi, M.; Sharifi, F.; Mirsalehi, S.E.; Moghaddam, A.O. A modified version of friction stir welding process of aluminum alloys: Analyzing the thermal treatment and wear behavior. Proc. Inst. Mech. Eng. Part L J. Mater. Des. Appl. 2021, 235, 2291–2309, doi:10.1177/14644207211023987