{"_shards":{"failed":0,"skipped":0,"successful":3,"total":3},"hits":{"hits":[{"_id":"1302666","_index":"publication","_score":11.584565,"_source":{"abstracts":[{"abstract":"Bu derleme, makine öğrenimi (ML) ve yapay sinir ağlarının (YSA), önemli bir eklemeli üretim süreci olan Laser Engineered Net Shaping (LENS) içinde alaşım üretim modellemesi ve baskı kontrolünü optimize etmek amacıyla entegrasyonunu incelemektedir. Süreç verimliliğini artırmak, ürün kalitesini iyileştirmek ve üretim döngülerini hızlandırmak için teorik temeller, metodolojiler, vaka çalışmaları ve yeni ortaya çıkan trendler araştırılmıştır. Akademik veri tabanları ve endüstri raporları üzerinde kapsamlı bir literatür taraması gerçekleştirilmiş, “makine öğrenimi”, “yapay sinir ağları” ve “Laser Engineered Net Shaping” gibi anahtar kelimeler kullanılmıştır. Konuya dengeli bir bakış açısı sunmak amacıyla hem teorik hem de deneysel çalışmalar analiz edilmiştir. Bulgular, ML ve YSA modellerinin alaşım üretim süreçlerini daha iyi anlamayı sağladığını, konfigürasyonları optimize ettiğini ve kusurları azalttığını göstermektedir. Gerçek zamanlı ML tabanlı optimizasyon, işlem parametrelerinin adaptif olarak ayarlanmasını sağlayarak kaliteyi ve doğruluğu artırır. YSA'lar, alaşım mikro yapısına ilişkin temel özellikleri başarılı bir şekilde tahmin ederek bilinçli karar alma ve süreç iyileştirmeye katkıda bulunur. ML ve YSA'ların LENS'e entegrasyonu, değişen koşullara ve alaşım bileşimlerine dinamik olarak uyum sağlayan adaptif üretimi mümkün kılar.","id":1229477,"keywords":["Yapay sinir ağları","lazerle tasarlanmış net şekillendirme","3d baskı kontrolü","süreç optimizasyonu"],"language":"TUR","title":"YAPAY SİNİR AĞLARI KULLANILARAK KATMANLI ÜRETİMDE LAZERLE TASARLANMIŞ AĞ ŞEKİLLENDİRME ÜZERİNE BİR LİTERATÜR İNCELEMESİ"},{"abstract":"This review explores the integration of machine learning (ML) and artificial neural networks (ANNs) in optimizing alloy production modeling and print control within Laser Engineered Net Shaping (LENS), a key additive manufacturing process. It investigates theoretical foundations, methodologies, case studies, and emerging trends to enhance process efficiency, improve product quality, and accelerate production cycles. A comprehensive literature review was conducted across academic databases and industry reports using keywords such as “machine learning,” “artificial neural networks,” and “Laser Engineered Net Shaping.” Both theoretical and experimental perspectives were analyzed to provide a well-rounded discussion. Findings indicate that ML and ANN models enhance understanding of alloy production, optimizing configurations and reducing defects. Real-time ML-driven optimization enables adaptive adjustments to process parameters, ensuring improved quality and accuracy. ANNs effectively predict key alloy microstructure properties, supporting informed decision-making and process refinement. Integrating ML and ANNs into LENS facilitates adaptive manufacturing, dynamically responding to changing conditions and alloy compositions.","id":1229476,"keywords":["Artificial neural networks","laser engineered net shaping","3d print control","process optimization"],"language":"ENG","title":"A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS"}],"accessType":"OPEN","attachments":null,"authors":[{"authorId":979336,"duty":"AUTHOR","inPublicationName":"Filiz Karaömerlioğlu","institution":{"city":["MERSİN"],"code":["Mzk2MzM1"],"country":[""],"fullTitle":["MERSİN ÜNİVERSİTESİ \u003e DİŞ HEKİMLİĞİ UYGULAMA VE ARAŞTIRMA MERKEZİ"],"rootCode":["MzYwNDQz"],"rootTitle":["MERSİN ÜNİVERSİTESİ"],"status":["KAMU"],"title":["DİŞ HEKİMLİĞİ UYGULAMA VE ARAŞTIRMA MERKEZİ"],"type":[""]},"institutionName":["Department of Electrical Electronics Engineering, Mersin University, Mersin, Turkey"],"isVerified":false,"name":"Filiz Karaomerlioglu","orcid":"0000-0002-4677-4365","order":1,"relationId":3279961,"userId":""},{"authorId":1078892,"duty":"AUTHOR","inPublicationName":"Mustafa Ucar","institution":{"city":["MERSİN"],"code":["MzU4Mzg1"],"country":[""],"fullTitle":["MERSİN ÜNİVERSİTESİ \u003e FEN BİLİMLERİ ENSTİTÜSÜ \u003e NANOTEKNOLOJİ VE İLERİ MALZEMELER ANABİLİM DALI"],"rootCode":["MzYwNDQz"],"rootTitle":["MERSİN ÜNİVERSİTESİ"],"status":["KAMU"],"title":["NANOTEKNOLOJİ VE İLERİ MALZEMELER ANABİLİM DALI"],"type":[""]},"institutionName":["Department of Nanotechnology and Advanced Materials, Mersin University, Mersin, Turkey"],"isVerified":false,"name":"Mustafa Uçar","orcid":"0000-0002-1851-2317","order":2,"relationId":3279962,"userId":""}],"citedReferences":null,"createdBy":"f0487b17-60dd-4158-9083-7f874073a3c5","databases":["SCIENCE"],"docType":"PAPER","doi":"10.17780/ksujes.1594930","downloadCount":0,"endDate":null,"endPage":"582","facetAuthorCity":["MERSİN"],"facetAuthorCountry":[""],"facetAuthorInstitution":["MERSİN ÜNİVERSİTESİ"],"firstIndexDate":"2025-07-03T13:37:20.261166Z","id":1302666,"indexDate":"2026-04-04T19:46:24.970618Z","indexedBy":"indexer","issue":{"id":86398,"isSpecial":false,"month":["3"],"number":"1","publishDate":"2025-03-03T00:00:00","volume":"28","year":"2025"},"journal":{"eissn":"1309-1751","id":"1386","issn":"","name":"KSÜ Mühendislik Bilimleri Dergisi"},"language":"ENG","orderCitationCount":0,"orderTitle":"ALITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS","otherCitations":[{"count":2,"origin":"CrossRef"},{"count":0,"origin":"OpenAlex"},{"count":0,"origin":"OpenCitations"}],"pdf":"e27a4f50-c5cc-4308-8c44-1f9dee021a62","projectGroup":null,"projectnumber":null,"publicationProduct":null,"publicationType":"COMPILATION","publicationYear":2025,"references":[{"authors":null,"context":"Ahlers, D., Wasserfall, F., Hendrich, N., \u0026 Zhang, J. 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Simple method to construct process maps for additive manufacturing using a support vector machine. Additive Manufacturing, 27, 353–362. https://doi.org/10.1016/J.ADDMA.2019.03.013","id":22482919,"journalCode":"","order":4,"targetPublication":0,"year":""},{"authors":null,"context":"Banga, S., Gehani, H., \u0026 Bhilare, S. (2018). 3D topology optimization using convolutional neural networks. ArxivOrg. https://doi.org/10.48550/arXiv.1808.07440","id":22482920,"journalCode":"","order":5,"targetPublication":0,"year":""},{"authors":null,"context":"Bendsoe, M. (1999). Material interpolation schemes in topology optimization. Amsterdam: Springer. https://doi.org/10.1007/s004190050248","id":22482921,"journalCode":"","order":6,"targetPublication":0,"year":""},{"authors":null,"context":"Bennett, J., Dudas, R., Jian Cao, J. and Ehmann, K. (2016). “Control of heating and cooling for direct laser deposition repair of cast iron components.” Northwestern University, Evanston, IL uluslararası esnek otomasyon sempozyumu (ISFA) , IEEE (2016). https://doi.org/10.1109/ISFA.2016.7790166","id":22482922,"journalCode":"","order":7,"targetPublication":0,"year":""},{"authors":null,"context":"Caggiano, A., Zhang, J., Alfieri, V., Caiazzo, F., Gao, R., \u0026 Teti, R. (2019). Machine learning-based image processing for on-line defect recognition in additive manufacturing. CIRP Annals, 68, 451–454.  https://doi.org/10.1016/J.CIRP.2019.03.021","id":22482923,"journalCode":"","order":8,"targetPublication":0,"year":""},{"authors":null,"context":"Caiazzo, F., \u0026 Caggiano, A. (2018). Laser Direct metal deposition of 2024 al alloy: Trace geometry prediction via machine learning. Materials, 11, 444. https://doi.org/10.3390/MA11030444","id":22482924,"journalCode":"","order":9,"targetPublication":0,"year":""},{"authors":null,"context":"Chan, S., \u0026 Lu, Y. (2018). Data-driven cost estimation for additive manufacturing in cybermanufacturing. Amsterdam: Elsevier. https://doi.org/10.1016/j.jmsy.2017.12.001","id":22482925,"journalCode":"","order":10,"targetPublication":0,"year":""},{"authors":null,"context":"Charalampous, P., Kostavelis, I., Kontodina, T., \u0026 Tzovaras, D. (2021). Learning-based error modeling in FDM 3D printing process. Rapid Prototyping Journal, 27, 507–517. https://doi.org/10.1108/RPJ-03-2020-0046","id":22482926,"journalCode":"","order":11,"targetPublication":0,"year":""},{"authors":null,"context":"Chen, C., Chen, G.X., Yu, Z.H. and Wang, Z.H. (2014). “A new method for reproducing oil paintings based on 3D printing.” Appl. Mech. Mater. 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Neural Network-Based Model Design for Short-Term Load Forecast in Distribution Systems. IEEE Transactions on Power Systems, Volume: 31, Issue: 1, Page(s): 72 – 81. https://doi.org/10.1109/TPWRS.2015.2390132","id":22482930,"journalCode":"","order":15,"targetPublication":0,"year":""},{"authors":null,"context":"Dowling, L., Kennedy, J., O’Shaughnessy, S., \u0026 Trimble, D. (2020). A review of critical repeatability and reproducibility issues in powder bed fusion. Materials and Design, 186, 108346.  https://doi.org/10.1016/j.matdes.2019.108346","id":22482931,"journalCode":"","order":16,"targetPublication":0,"year":""},{"authors":null,"context":"Dumais, S. T. (2004). Latent semantic analysis. Annual Review of Information Science and Technology, 38, 188–230. https://doi.org/10.1002/aris.1440380105","id":22482932,"journalCode":"","order":17,"targetPublication":0,"year":""},{"authors":null,"context":"Everton, S.K., Hirsch, M., Stavroulakis, P.I., Leach, R.K. \u0026 Clare, A.T. Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing Mater. Des., 95 (2016), pp. 431-445. https://doi.org/10.1016/j.matdes.2016.01.099","id":22482933,"journalCode":"","order":18,"targetPublication":0,"year":""},{"authors":null,"context":"Francis, J., \u0026 Letters, L.B.-M. (2019). Deep learning for distortion prediction in laser-based additive manufacturing using big data. Amsterdam: Elsevier. https://doi.org/10.1016/j.mfglet.2019.02.001","id":22482934,"journalCode":"","order":19,"targetPublication":0,"year":""},{"authors":null,"context":"Frazier, W. E. (2014). Metal additive manufacturing: A review. Journal of Materials Engineering and Performance, 23, 1917–1928. https://doi.org/10.1007/S11665-014-0958-Z/FIGURES/9","id":22482935,"journalCode":"","order":20,"targetPublication":0,"year":""},{"authors":null,"context":"Fu, F., Zhang, Y. Chang, G. and Dai, J. (2016). “Analysis on the physical mechanism of laser cladding crack and its influence factors.” Optik Volume 127, Issue 1, January 2016, pp. 200-202. https://doi.org/10.1016/j.ijleo.2015.10.043","id":22482936,"journalCode":"","order":21,"targetPublication":0,"year":""},{"authors":null,"context":"Gobert, C., Reutzel, E. W., Petrich, J., Nassar, A. R., \u0026 Phoha, S. (2018). Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging. Additive Manufacturing, 21, 517–528. https://doi.org/10.1016/J.ADDMA.2018.04.005","id":22482937,"journalCode":"","order":22,"targetPublication":0,"year":""},{"authors":null,"context":"Gorunov, A. (2018) “Complex refurbishment of titanium turbine blades by applying heat-resistant coatings by direct metal deposition.” Eng Fail Anal, 86 (2018), pp. 115-130. https://doi.org/10.1016/j.engfailanal.2018.01.001","id":22482938,"journalCode":"","order":23,"targetPublication":0,"year":""},{"authors":null,"context":"Grasso, M. and Colosimo, B.M. (2017). “Process defects and in situ monitoring methods in metal powder bed fusion.” a review[J] Meas Sci Technol, 28 (4) (2017), Article 044005. https://doi.org/10.1088/1361-6501/aa5c4f","id":22482939,"journalCode":"","order":24,"targetPublication":0,"year":""},{"authors":null,"context":"Grasso, M., Demir, A. G., Previtali, B., \u0026 Colosimo, B. M. (2018). In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume. Robot Computer Integrating Manufacturing, 49, 229–239. https://doi.org/10.1016/J.RCIM.2017.07.001","id":22482940,"journalCode":"","order":25,"targetPublication":0,"year":""},{"authors":null,"context":"Grasso, M., Laguzza, V., Semeraro, Q., \u0026 Colosimo, B. M. (2017). In-process monitoring of selective laser melting: spatial detection of defects via image data analysis. American Society Mechanical Engineering, 2017, 139(5). https://doi.org/10.1115/1.4034715","id":22482941,"journalCode":"","order":26,"targetPublication":0,"year":""},{"authors":null,"context":"Grierson, D. R., \u0026 Quayle, S. D. (2021). Machine learning for additive manufacturing. Encyclopedia, 3, 1541–1556. https://doi.org/10.1016/j.matt.2020.08.023","id":22482942,"journalCode":"","order":27,"targetPublication":0,"year":""},{"authors":null,"context":"Gu, G. X., Chen, C. T., Richmond, D. J., \u0026 Buehler, M. J. (2018). Bioinspired hierarchical composite design using machine learning: Simulation, additive manufacturing, and experiment. Material Horizons, 5, 939–945. https://doi.org/10.1039/C8MH00653A","id":22482943,"journalCode":"","order":28,"targetPublication":0,"year":""},{"authors":null,"context":"Gunther, D., Pirehgalin, M. F., Weis, I., Vogel-Heuser, B. (2020). Condition monitoring for the Binder Jetting AM-process with machine learning approaches. Proceedings - 2020 IEEE Conference Industrial Cyberphysical Systems ICPS 2020 2020:417–20. https://doi.org/10.1109/ICPS48405.2020.9274716","id":22482944,"journalCode":"","order":29,"targetPublication":0,"year":""},{"authors":null,"context":"Guo, N., \u0026 Leu, M. C. (2013). Additive manufacturing: Technology, applications and research needs. Frontiers of Mechanical Engineering, 8, 215–243. https://doi.org/10.1007/s11465-013-0248-8","id":22482945,"journalCode":"","order":30,"targetPublication":0,"year":""},{"authors":null,"context":"Heralić, A., Christiansson, A.K. \u0026 Lennartson, B. (2012). “Height control of laser metal-wire deposition based on iterative learning control and 3D scanning”. Optics and Lasers in Engineering. Volume 50, Issue 9, September 2012, Pages 1230-1241. https://doi.org/10.1016/j.optlaseng.2012.03.016","id":22482946,"journalCode":"","order":31,"targetPublication":0,"year":""},{"authors":null,"context":"Hojjati, A., Adhikari, A., Struckmann, K., Chou, E. J., Ngoc, T., Nguyen, T., Madan, K., Winslett, M.S., Gunter, C.A., King, W.P., (2016). Leave Your Phone at the Door: Side Channels that Reveal Factory Floor Secrets. In: Proceedings of 2016 ACM SIGSAC Conference on Computer Communications Security. https://doi.org/10.1145/2976749","id":22482947,"journalCode":"","order":32,"targetPublication":0,"year":""},{"authors":null,"context":"Jia, C. B., Liu, X. F., Zhang, G. K., Zhang, Y., Yu, C. H., \u0026 Wu, C. S. (2021). Penetration/keyhole status prediction and model visualization based on deep learning algorithm in plasma arc welding. International Journal of Advanced Manufacturing Technology, 117, 3577–3597. https://doi.org/10.1007/s00170-021-07903-9","id":22482948,"journalCode":"","order":33,"targetPublication":0,"year":""},{"authors":null,"context":"Jin, Z., Zhang, Z., Demir, K., \u0026 Gu, G. X. (2020). Machine learning for advanced additive manufacturing. Matter, 3, 1541–1556. https://doi.org/10.1016/j.matt.2020.08.023","id":22482949,"journalCode":"","order":34,"targetPublication":0,"year":""},{"authors":null,"context":"Kang, H. S., Lee, J. Y., Choi, S., Kim, H., Park, J. H., Son, J. Y., et al. 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Singapore: Springer. https://doi.org/10.1007/978-981-16-3184-9_3","id":22482955,"journalCode":"","order":40,"targetPublication":0,"year":""},{"authors":null,"context":"Kumar, S., \u0026 Wu, C. (2021a). Strengthening effects of tool-mounted ultrasonic vibrations during friction stir lap welding of Al and Mg alloys. Metallurgical and Materials Transactions a, Physical Metallurgy and Materials Science, 52, 2909–2925. https://doi.org/10.1007/s11661-021-06282-w","id":22482956,"journalCode":"","order":41,"targetPublication":0,"year":""},{"authors":null,"context":"Kumar, S., \u0026 Wu, C. (2021b). Eliminating intermetallic compounds via Ni interlayer during friction stir welding of dissimilar Mg/Al alloys. Journal of Material Research and Technology, 15, 4353–4369. https://doi.org/10.1016/J.JMRT.2021.10.065","id":22482957,"journalCode":"","order":42,"targetPublication":0,"year":""},{"authors":null,"context":"Kumar, S., \u0026 Wu, C. S. (2018). 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Metallurgical and Materials Transactions a: Physical Metallurgy and Materials Science, 51, 5725–5742. https://doi.org/10.1007/s11661-020-05982-z","id":22482960,"journalCode":"","order":45,"targetPublication":0,"year":""},{"authors":null,"context":"Kumar, S., Wu, C. S., \u0026 Song, G. (2020c). Process parametric dependency of axial downward force and macro- and microstructural morphologies in ultrasonically assisted friction stir welding of Al/Mg alloys. Metallurgical and Materials Transactions a: Physical Metallurgy and Materials Science, 51, 2863–2881. https://doi.org/10.1007/s11661-020-05716-1","id":22482961,"journalCode":"","order":46,"targetPublication":0,"year":""},{"authors":null,"context":"Kumar, S., Wu, C. S., Padhy, G. K., \u0026 Ding, W. (2017). Application of ultrasonic vibrations in welding and metal processing: A status review. Journal of Manufacturing Processes, 26, 295–322. https://doi.org/10.1016/j.jmapro.2017.02.027","id":22482962,"journalCode":"","order":47,"targetPublication":0,"year":""},{"authors":null,"context":"Kumar, S., Wu, C. S., Sun, Z., \u0026 Ding, W. (2019). Effect of ultrasonic vibration on welding load, macrostructure, and mechanical properties of Al/Mg alloy joints fabricated by friction stir lap welding. International Journal of Advanced Manufacturing Technology, 100, 1787–1799. https://doi.org/10.1007/s00170-018-2717-z","id":22482963,"journalCode":"","order":48,"targetPublication":0,"year":""},{"authors":null,"context":"Kumar, S., Gopi, T., Harikeerthana, N., Gupta, M.K., Gaur, V., Krolczyk, G. \u0026 Wu, C.S. (2022). Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control. Journal of Intelligent Manufacturing, Volume 34, pages 21–55. https://doi.org/10.1007/s10845-022-02029-5","id":22482964,"journalCode":"","order":49,"targetPublication":0,"year":""},{"authors":null,"context":"Kwon, O., Kim, H. G., Ham, M. J., Kim, W., Kim, G.-H., Cho, J.-H., et al. (2018). A deep neural network for classification of melt-pool images in metal additive manufacturing. Journal of Intelligence Manufacturing, 31, 375–386. https://doi.org/10.1007/S10845-018-1451-6","id":22482965,"journalCode":"","order":50,"targetPublication":0,"year":""},{"authors":null,"context":"Meng, L., McWilliams, B., Jarosinski, W., Park, H. Y., Jung, Y. G., Lee, J., et al. (2020). Machine learning in additive manufacturing: a review. 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