Derleme
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YAPAY SİNİR AĞLARI KULLANILARAK KATMANLI ÜRETİMDE LAZERLE TASARLANMIŞ AĞ ŞEKİLLENDİRME ÜZERİNE BİR LİTERATÜR İNCELEMESİ

Yıl 2025, Cilt: 28 Sayı: 1, 551 - 582, 03.03.2025

Filiz Karaömerlioğlu , Mustafa Ucar

Öz

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.

Anahtar Kelimeler

Yapay sinir ağları lazerle tasarlanmış net şekillendirme 3d baskı kontrolü süreç optimizasyonu

Kaynakça

  • Ahlers, D., Wasserfall, F., Hendrich, N., & Zhang, J. (2019). 3D printing of nonplanar layers for smooth surface generation. IEEE International Conference Automative Science Enginerring. https://doi.org/10.1109/COASE.2019.8843116
  • Al Faruque, M. A., Chhetri, S. R., Canedo, A., & Wan, J. (2016). Acoustic Side-Channel Attacks on Additive Manufacturing Systems. 2016 ACM/IEEE 7th Int Conf Cyber-Physical Syst ICCPS 2016-Proceedings 2016. https://doi.org/10.1109/ICCPS.2016.7479068
  • Alabi, M. O. (2018). Big data, 3D printing technology, and industry of the future. International Journal of Big Data and Anal Healthcare, 2, 1–20. https://doi.org/10.4018/ijbdah.2017070101
  • Aoyagi, K., Wang, H., Sudo, H., & Chiba, A. (2019). 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
  • Banga, S., Gehani, H., & Bhilare, S. (2018). 3D topology optimization using convolutional neural networks. ArxivOrg. https://doi.org/10.48550/arXiv.1808.07440
  • Bendsoe, M. (1999). Material interpolation schemes in topology optimization. Amsterdam: Springer. https://doi.org/10.1007/s004190050248
  • 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
  • Caggiano, A., Zhang, J., Alfieri, V., Caiazzo, F., Gao, R., & 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
  • Caiazzo, F., & 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
  • Chan, S., & Lu, Y. (2018). Data-driven cost estimation for additive manufacturing in cybermanufacturing. Amsterdam: Elsevier. https://doi.org/10.1016/j.jmsy.2017.12.001
  • Charalampous, P., Kostavelis, I., Kontodina, T., & 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
  • 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. Pages 644–650, pp. 2386–2389. https://doi.org/10.3390/polym12112536
  • Chen, Q., Guillemot, G., Gandin, C.A. and Bellet, M. (2017). “Three-dimensional finite element thermomechanical modeling of additive manufacturing by selective laser melting for ceramic materials.” Addit. Manuf. 2017, 16, pp. 124–137. https://doi.org/10.1016/j.addma.2017.02.005
  • Chowdhury, S., Mhapsekar, K., & Anand, S. (2018). Part build orientation optimization and neural network-based geometry compensation for additive manufacturing process. Journal of Manufacturing Science and Engineering, 1, 140. https://doi.org/10.1115/1.4038293
  • Ding, N., Benoit, C., Foggia, G. Bésanger, Y. & Wurtz, F. (2016). 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
  • Dowling, L., Kennedy, J., O’Shaughnessy, S., & 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
  • Dumais, S. T. (2004). Latent semantic analysis. Annual Review of Information Science and Technology, 38, 188–230. https://doi.org/10.1002/aris.1440380105
  • Everton, S.K., Hirsch, M., Stavroulakis, P.I., Leach, R.K. & 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
  • Francis, J., & 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
  • 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
  • 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
  • Gobert, C., Reutzel, E. W., Petrich, J., Nassar, A. R., & 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
  • 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
  • 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
  • Grasso, M., Demir, A. G., Previtali, B., & 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
  • Grasso, M., Laguzza, V., Semeraro, Q., & 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
  • Grierson, D. R., & Quayle, S. D. (2021). Machine learning for additive manufacturing. Encyclopedia, 3, 1541–1556. https://doi.org/10.1016/j.matt.2020.08.023
  • Gu, G. X., Chen, C. T., Richmond, D. J., & 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
  • 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
  • Guo, N., & 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
  • Heralić, A., Christiansson, A.K. & 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
  • 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
  • Jia, C. B., Liu, X. F., Zhang, G. K., Zhang, Y., Yu, C. H., & 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
  • Jin, Z., Zhang, Z., Demir, K., & Gu, G. X. (2020). Machine learning for advanced additive manufacturing. Matter, 3, 1541–1556. https://doi.org/10.1016/j.matt.2020.08.023
  • Kang, H. S., Lee, J. Y., Choi, S., Kim, H., Park, J. H., Son, J. Y., et al. (2016). Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering Manufacturing - Green Technology, 3, 111–128. https://doi.org/10.1007/s40684-016-0015-5
  • Khanzadeh, M., Rao, P., Jafari-Marandi, R., Smith, B. K., Tschopp, M. A., & Bian, L. (2018). Quantifying geometric accuracy with unsupervised machine learning: Using self-organizing map on fused filament fabrication additive manufacturing parts. Journal of Manufacturing Science and Engineering. https://doi.org/10.1115/1.4038598
  • Kulkarni, P., Marsan, A., & Dutta, D. (2000). Review of process planning techniques in layered manufacturing. Rapid Prototyp J, 6, 18–35. https://doi.org/10.1108/13552540010309859
  • Kumar, S. (2016). Ultrasonic assisted friction stir processing of 6063 aluminum alloy. Archives of Civil and Mechanical Engineering, 16, 473–484. https://doi.org/10.1016/j.acme.2016.03.002
  • Kumar, S., & Kar, A. (2021). A review of solid-state additive manufacturing processes. Transactions on Indian Natational Academic Engineering, 6, 955–973. https://doi.org/10.1007/S41403-021-00270-7
  • Kumar, S., & Kishor, B. (2021). Ultrasound added additive manufacturing for metals and composites: Process and control. Singapore: Springer. https://doi.org/10.1007/978-981-16-3184-9_3
  • Kumar, S., & 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
  • Kumar, S., & 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
  • Kumar, S., & Wu, C. S. (2018). A novel technique to join Al and Mg alloys: Ultrasonic vibration assisted linear friction stir welding. Materials Today Proceedings, 5, 18142–18151. https://doi.org/10.1016/j.matpr.2018.06.150
  • Kumar, S., & Wu, C. S. (2020a). Suppression of intermetallic reaction layer by ultrasonic assistance during friction stir welding of Al and Mg based alloys. Journal of Alloys and Compounds, 827, 154343. https://doi.org/10.1016/j.jallcom.2020.154343
  • Kumar, S., Wu, C. S., & Shi, L. (2020b). Intermetallic diminution during friction stir welding of dissimilar Al/Mg alloys in lap configuration via ultrasonic assistance. Metallurgical and Materials Transactions a: Physical Metallurgy and Materials Science, 51, 5725–5742. https://doi.org/10.1007/s11661-020-05982-z
  • Kumar, S., Wu, C. S., & 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
  • Kumar, S., Wu, C. S., Padhy, G. K., & 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
  • Kumar, S., Wu, C. S., Sun, Z., & 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
  • Kumar, S., Gopi, T., Harikeerthana, N., Gupta, M.K., Gaur, V., Krolczyk, G. & 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
  • 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
  • Meng, L., McWilliams, B., Jarosinski, W., Park, H. Y., Jung, Y. G., Lee, J., et al. (2020). Machine learning in additive manufacturing: a review. JOM Journal of the Minerals Metals and Materials Society, 72, 1. https://doi.org/10.1007/s11837-020-04155-y
  • Mozaffar, M., Paul, A., Al-Bahrani, R., Wolff, S., Choudhary, A., Agrawal, A., et al. (2018). Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks. Manufacturing Letters, 18, 35–39. https://doi.org/10.1016/J.MFGLET.2018.10.002
  • Noriega, A., Blanco, D., Alvarez, B. J., & Garcia, A. (2013). Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm. International Journal of Advanced Manufacturing Technology, 69, 2301–2313. https://doi.org/10.1007/S00170-013-5196-2
  • Qi, X., Chen, G., Li, Y., Cheng, X., & Li, C. (2019). Applying neural-network-based machine learning to additive manufacturing: Current applications, challenges, and future perspectives. Engineering, 5, 721–729. https://doi.org/10.1016/J.ENG.2019.04.012
  • Rawat, S., & Shen, M. H. H. (2018). A novel topology design approach using an integrated deep learning network architecture. https://doi.org/10.48550/arXiv.1808.02334
  • Razvi, S. S., Feng, S., Narayanan, A., Lee, Y. T. T., & Witherell, P. (2019). A review of machine learning applications in additive manufacturing. Proceedings ASME Design Engineering Technical Conference. https://doi.org/10.1115/DETC2019-98415
  • Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386–408. https://doi.org/10.1037/h0042519
  • Sames, W.J. and Pannala, S. (2016). “The metallurgy and processing science of metal additive manufacturing” [J] Int Mater Rev, 61 (5), pp. 315 – 360. https://doi.org/10.1080/09506608.2015.1116649
  • Scime, L., & Beuth, J. (2018a). Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Additive Manufacturing, 19, 114–126. https://doi.org/10.1016/J.ADDMA.2017.11.009
  • Scime, L., & Beuth, J. (2018b). A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process. Additive Manufacturing, 24, 273–286. https://doi.org/10.1016/J.ADDMA.2018.09.034
  • Scime, L., & Beuth, J. (2019). Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Additive Manufacturing, 25, 151–165. https://doi.org/10.1016/J.ADDMA.2018.11.010
  • Shinde, P. P., & Shah, S. (2018). A Review of Machine Learning and Deep Learning Applications. In: Proceedings - 2018 4th International Conference Computer Communication Control Autom ICCUBEA 2018. https://doi.org/10.1109/ICCUBEA.2018.8697857
  • Singh, S., Ramakrishna, S., & Singh, R. (2017). Material issues in additive manufacturing: A review. Journal of Manufacturing Processes, 25, 185–200. https://doi.org/10.1016/j.jmapro.2016.11.006
  • Sterling , A.J., Torries, B., Shamsaei, N., Thompson, S.M. and W.Seely, D.W. (2016). “Fatigue behavior and failure mechanisms of direct laser deposited Ti–6Al–4V.” Materials Science and Engineering: A Volume 655, 8 February 2016, pp. 100-112. https://doi.org/10.1016/j.msea.2015.12.026
  • Sutton, R. S., & Barto, A. G. (2015). Reinforcement Learning (2nd ed.). New York: The MIT Press.
  • Tapia, G., Elwany, A. H., & Sang, H. (2016). Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models. Additive Manufacturing, 12, 282–290. https://doi.org/10.1016/J.ADDMA.2016.05.009
  • Tapia, G., Khairallah, S., Matthews, M., King, W. E., & Elwany, A. (2017). Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel. International Journal of Advanced Manufacturing Technology, 94, 3591–3603. https://doi.org/10.1007/S00170-017-1045-Z
  • Thompson, M. K., Moroni, G., Vaneker, T., Fadel, G., Campbell, R. I., Gibson, I., et al. (2016). Design for additive manufacturing: Trends, opportunities, considerations, and constraints. CIRP Annals - Manufacturing Technology, 65, 737–760. https://doi.org/10.1016/j.cirp.2016.05.004
  • Vaezi, M., Seitz, H. & Yang, S. (2013). A review on 3D micro-additive manufacturing technologies. The International Journal of Advanced Manufacturing Technology. Volume 67, pages 1721–1754. https://doi.org/10.1007/s00170-012-4605-2
  • Wang, C., Tan, X. P., Tor, S. B., & Lim, C. S. (2020). Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing, 36, 101538. https://doi.org/10.1016/j.addma.2020.101538
  • Wang, C., Tan, X., Liu, E., & Tor, S. B. (2018b). Process parameter optimization and mechanical properties for additively manufactured stainless steel 316L parts by selective electron beam melting. Materials and Design, 147, 157–166. https://doi.org/10.1016/J.MATDES.2018.03.035
  • Wang, L., Törngren, M., & Onori, M. (2015). Current status and advancement of cyber-physical systems in manufacturing. Journal of Manufacturing Systems, 37, 517–527. https://doi.org/10.1016/J.JMSY.2015.04.008
  • Wang, T., Kwok, T. H., Zhou, C., & Vader, S. (2018a). In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing. Journal of Manufacturing Systems, 47, 83–92. https://doi.org/10.1016/J.JMSY.2018.04.003
  • Wang, Y. and Li, X. (2020). “An accurate finite element approach for programming 4D-printed self-morphing structures produced by fused deposition modeling.” Mech. Mater. 2020, 151, pp. 103628. https://doi.org/10.1016/j.mechmat.2020.103628
  • Weiss, S. M., Dhurandhar, A., Baseman, R. J., White, B. F., Logan, R., Winslow, J. K., et al. (2014). Continuous prediction of manufacturing performance throughout the production lifecycle. Journal of Intelligent Manufacturing, 27, 751–763. https://doi.org/10.1007/S10845-014-0911-X
  • Widrow, B., & Lehr, M. A. (1990). 30 years of adaptive neural networks: perceptron, madaline, and backpropagation. Proceedings of the IEEE, 78, 1415–1442. https://doi.org/10.1109/5.58323
  • Wu, D., Jennings, C., Terpenny, J., Gao, R. X., & Kumara, S. (2017). A comparative study on machine learning algorithms for smart manufacturing: Tool wear prediction using random forests. Journal of Manufacturing Science and Engineering Transactions on ASME, 2017, 139. https://doi.org/10.1115/1.4036350
  • Wu, D., Rosen, D. W., Wang, L., & Schaefer, D. (2015a). Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation. Computer Design, 59, 1–14. https://doi.org/10.1016/J.CAD.2014.07.006
  • Wu, H., Wang, Y., & Yu, Z. (2015b). In situ monitoring of FDM machine condition via acoustic emission. International Journal of Advanced Manufacturing Technology, 84, 1483–1495. https://doi.org/10.1007/S00170-015-7809-4
  • Wu, H., Yu, Z., & Wang, Y. (2016a). Real-time FDM machine condition monitoring and diagnosis based on acoustic emission and hidden semi-Markov model. International Journal of Advanced Manufacturing Technology, 90, 2027–2036. https://doi.org/10.1007/S00170-016-9548-6
  • Wu, H., Yu, Z., et al. (2016b). A new approach for online monitoring of additive manufacturing based on acoustic emission. Asmedigitalcollection. https://doi.org/10.1115/MSEC2016-8551
  • Wu, M., Phoha, V. V., Moon, Y. B., & Belman, A. K. (2016). Detecting malicious defects in 3D printing process using machine learning and image classification. ASME International Mechanical Engineering & Congress and Exposition Proceedings. https://doi.org/10.1115/IMECE201667641
  • Wu, M., Song, Z., & Moon, Y. B. (2017). Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods. Journal of Intelligence and Manufacturing, 30, 1111–1123. https://doi.org/10.1007/S10845-017-1315-5
  • Wu, X., Xu, C., Zhang, Z. and Guo, C. (2020). “Modeling and visualization of layered curing conversion profile in ceramic mask projection stereolithography process.” Ceram. Int. 2020, 46, pp. 25750–25757. https://doi.org/10.1016/j.ceramint.2020.07.053
  • Xing, C., Jia, C., Han, Y., Dong, S., Yang, J., & Wu, C. (2020). Numerical analysis of the metal transfer and welding arc behaviors in underwater flux-cored arc welding. International Journal of Heat and Mass Transfer, 153, 119570. https://doi.org/10.1016/J.IJHEATMASSTRANSFER.2020.119570
  • Yang, J., Li, F., Wang, Z. and Zeng, X. (2015). “Cracking behavior and control of Rene 104 superalloy produced by direct laser fabrication.” Journal of Materials Processing Technology Volume 225, November 2015, Pages 229-239. https://doi.org/10.1016/j.jmatprotec.2015.06.002
  • Yao, X., Moon, S. K., & Bi, G. (2017). A hybrid machine learning approach for additive manufacturing design feature recommendation. Rapid Prototyping Journal, 23, 983–997. https://doi.org/10.1108/RPJ-03-2016-0041
  • Zhang, H.-C., & Huang, S. H. (1995). Applications of neural networks in manufacturing: A state-of-the-art survey. International Journal of Production Research, 33, 705–728. https://doi.org/10.1080/00207549508930175
  • Zhang, Y. and Chou, Y.K. (2006). “Three-dimensional finite element analysis simulations of the fused deposition modelling process.” Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., 220, pp. 1663–1671. https://doi.org/10.1243/09544054JEM572
  • Zhang, Y., Hong, G. S., Ye, D., Zhu, K., & Fuh, J. Y. H. (2018b). Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring. Materials and Design, 156, 458–469. https://doi.org/10.1016/J.MATDES.2018.07.002
  • Zhang, Y., Jarosinski, W., Jung, Y.G. and Zhang, J. (2018). “Additive manufacturing processes and equipment. In Additive Manufacturing: Materials, Processes, Quantifications and Applications.” Elsevier: Amsterdam, The Netherlands. pp. 39–51. https://doi.org/10.1016/B978-0-12-812155-9.00002-5
  • Zhang, Z., Liu, Z., & Wu, D. (2021). Prediction of melt pool temperature in directed energy deposition using machine learning. Additive Manufacturing, 37, 101692. https://doi.org/10.1016/J.ADDMA.2020.101692

A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS

Yıl 2025, Cilt: 28 Sayı: 1, 551 - 582, 03.03.2025

Filiz Karaömerlioğlu , Mustafa Ucar

Öz

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.

Anahtar Kelimeler

Artificial neural networks laser engineered net shaping 3d print control process optimization

Kaynakça

  • Ahlers, D., Wasserfall, F., Hendrich, N., & Zhang, J. (2019). 3D printing of nonplanar layers for smooth surface generation. IEEE International Conference Automative Science Enginerring. https://doi.org/10.1109/COASE.2019.8843116
  • Al Faruque, M. A., Chhetri, S. R., Canedo, A., & Wan, J. (2016). Acoustic Side-Channel Attacks on Additive Manufacturing Systems. 2016 ACM/IEEE 7th Int Conf Cyber-Physical Syst ICCPS 2016-Proceedings 2016. https://doi.org/10.1109/ICCPS.2016.7479068
  • Alabi, M. O. (2018). Big data, 3D printing technology, and industry of the future. International Journal of Big Data and Anal Healthcare, 2, 1–20. https://doi.org/10.4018/ijbdah.2017070101
  • Aoyagi, K., Wang, H., Sudo, H., & Chiba, A. (2019). 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
  • Banga, S., Gehani, H., & Bhilare, S. (2018). 3D topology optimization using convolutional neural networks. ArxivOrg. https://doi.org/10.48550/arXiv.1808.07440
  • Bendsoe, M. (1999). Material interpolation schemes in topology optimization. Amsterdam: Springer. https://doi.org/10.1007/s004190050248
  • 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
  • Caggiano, A., Zhang, J., Alfieri, V., Caiazzo, F., Gao, R., & 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
  • Caiazzo, F., & 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
  • Chan, S., & Lu, Y. (2018). Data-driven cost estimation for additive manufacturing in cybermanufacturing. Amsterdam: Elsevier. https://doi.org/10.1016/j.jmsy.2017.12.001
  • Charalampous, P., Kostavelis, I., Kontodina, T., & 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
  • 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. Pages 644–650, pp. 2386–2389. https://doi.org/10.3390/polym12112536
  • Chen, Q., Guillemot, G., Gandin, C.A. and Bellet, M. (2017). “Three-dimensional finite element thermomechanical modeling of additive manufacturing by selective laser melting for ceramic materials.” Addit. Manuf. 2017, 16, pp. 124–137. https://doi.org/10.1016/j.addma.2017.02.005
  • Chowdhury, S., Mhapsekar, K., & Anand, S. (2018). Part build orientation optimization and neural network-based geometry compensation for additive manufacturing process. Journal of Manufacturing Science and Engineering, 1, 140. https://doi.org/10.1115/1.4038293
  • Ding, N., Benoit, C., Foggia, G. Bésanger, Y. & Wurtz, F. (2016). 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
  • Dowling, L., Kennedy, J., O’Shaughnessy, S., & 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
  • Dumais, S. T. (2004). Latent semantic analysis. Annual Review of Information Science and Technology, 38, 188–230. https://doi.org/10.1002/aris.1440380105
  • Everton, S.K., Hirsch, M., Stavroulakis, P.I., Leach, R.K. & 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
  • Francis, J., & 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
  • 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
  • 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
  • Gobert, C., Reutzel, E. W., Petrich, J., Nassar, A. R., & 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
  • 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
  • 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
  • Grasso, M., Demir, A. G., Previtali, B., & 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
  • Grasso, M., Laguzza, V., Semeraro, Q., & 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
  • Grierson, D. R., & Quayle, S. D. (2021). Machine learning for additive manufacturing. Encyclopedia, 3, 1541–1556. https://doi.org/10.1016/j.matt.2020.08.023
  • Gu, G. X., Chen, C. T., Richmond, D. J., & 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
  • 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
  • Guo, N., & 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
  • Heralić, A., Christiansson, A.K. & 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
  • 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
  • Jia, C. B., Liu, X. F., Zhang, G. K., Zhang, Y., Yu, C. H., & 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
  • Jin, Z., Zhang, Z., Demir, K., & Gu, G. X. (2020). Machine learning for advanced additive manufacturing. Matter, 3, 1541–1556. https://doi.org/10.1016/j.matt.2020.08.023
  • Kang, H. S., Lee, J. Y., Choi, S., Kim, H., Park, J. H., Son, J. Y., et al. (2016). Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering Manufacturing - Green Technology, 3, 111–128. https://doi.org/10.1007/s40684-016-0015-5
  • Khanzadeh, M., Rao, P., Jafari-Marandi, R., Smith, B. K., Tschopp, M. A., & Bian, L. (2018). Quantifying geometric accuracy with unsupervised machine learning: Using self-organizing map on fused filament fabrication additive manufacturing parts. Journal of Manufacturing Science and Engineering. https://doi.org/10.1115/1.4038598
  • Kulkarni, P., Marsan, A., & Dutta, D. (2000). Review of process planning techniques in layered manufacturing. Rapid Prototyp J, 6, 18–35. https://doi.org/10.1108/13552540010309859
  • Kumar, S. (2016). Ultrasonic assisted friction stir processing of 6063 aluminum alloy. Archives of Civil and Mechanical Engineering, 16, 473–484. https://doi.org/10.1016/j.acme.2016.03.002
  • Kumar, S., & Kar, A. (2021). A review of solid-state additive manufacturing processes. Transactions on Indian Natational Academic Engineering, 6, 955–973. https://doi.org/10.1007/S41403-021-00270-7
  • Kumar, S., & Kishor, B. (2021). Ultrasound added additive manufacturing for metals and composites: Process and control. Singapore: Springer. https://doi.org/10.1007/978-981-16-3184-9_3
  • Kumar, S., & 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
  • Kumar, S., & 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
  • Kumar, S., & Wu, C. S. (2018). A novel technique to join Al and Mg alloys: Ultrasonic vibration assisted linear friction stir welding. Materials Today Proceedings, 5, 18142–18151. https://doi.org/10.1016/j.matpr.2018.06.150
  • Kumar, S., & Wu, C. S. (2020a). Suppression of intermetallic reaction layer by ultrasonic assistance during friction stir welding of Al and Mg based alloys. Journal of Alloys and Compounds, 827, 154343. https://doi.org/10.1016/j.jallcom.2020.154343
  • Kumar, S., Wu, C. S., & Shi, L. (2020b). Intermetallic diminution during friction stir welding of dissimilar Al/Mg alloys in lap configuration via ultrasonic assistance. Metallurgical and Materials Transactions a: Physical Metallurgy and Materials Science, 51, 5725–5742. https://doi.org/10.1007/s11661-020-05982-z
  • Kumar, S., Wu, C. S., & 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
  • Kumar, S., Wu, C. S., Padhy, G. K., & 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
  • Kumar, S., Wu, C. S., Sun, Z., & 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
  • Kumar, S., Gopi, T., Harikeerthana, N., Gupta, M.K., Gaur, V., Krolczyk, G. & 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
  • 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
  • Meng, L., McWilliams, B., Jarosinski, W., Park, H. Y., Jung, Y. G., Lee, J., et al. (2020). Machine learning in additive manufacturing: a review. JOM Journal of the Minerals Metals and Materials Society, 72, 1. https://doi.org/10.1007/s11837-020-04155-y
  • Mozaffar, M., Paul, A., Al-Bahrani, R., Wolff, S., Choudhary, A., Agrawal, A., et al. (2018). Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks. Manufacturing Letters, 18, 35–39. https://doi.org/10.1016/J.MFGLET.2018.10.002
  • Noriega, A., Blanco, D., Alvarez, B. J., & Garcia, A. (2013). Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm. International Journal of Advanced Manufacturing Technology, 69, 2301–2313. https://doi.org/10.1007/S00170-013-5196-2
  • Qi, X., Chen, G., Li, Y., Cheng, X., & Li, C. (2019). Applying neural-network-based machine learning to additive manufacturing: Current applications, challenges, and future perspectives. Engineering, 5, 721–729. https://doi.org/10.1016/J.ENG.2019.04.012
  • Rawat, S., & Shen, M. H. H. (2018). A novel topology design approach using an integrated deep learning network architecture. https://doi.org/10.48550/arXiv.1808.02334
  • Razvi, S. S., Feng, S., Narayanan, A., Lee, Y. T. T., & Witherell, P. (2019). A review of machine learning applications in additive manufacturing. Proceedings ASME Design Engineering Technical Conference. https://doi.org/10.1115/DETC2019-98415
  • Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386–408. https://doi.org/10.1037/h0042519
  • Sames, W.J. and Pannala, S. (2016). “The metallurgy and processing science of metal additive manufacturing” [J] Int Mater Rev, 61 (5), pp. 315 – 360. https://doi.org/10.1080/09506608.2015.1116649
  • Scime, L., & Beuth, J. (2018a). Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Additive Manufacturing, 19, 114–126. https://doi.org/10.1016/J.ADDMA.2017.11.009
  • Scime, L., & Beuth, J. (2018b). A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process. Additive Manufacturing, 24, 273–286. https://doi.org/10.1016/J.ADDMA.2018.09.034
  • Scime, L., & Beuth, J. (2019). Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Additive Manufacturing, 25, 151–165. https://doi.org/10.1016/J.ADDMA.2018.11.010
  • Shinde, P. P., & Shah, S. (2018). A Review of Machine Learning and Deep Learning Applications. In: Proceedings - 2018 4th International Conference Computer Communication Control Autom ICCUBEA 2018. https://doi.org/10.1109/ICCUBEA.2018.8697857
  • Singh, S., Ramakrishna, S., & Singh, R. (2017). Material issues in additive manufacturing: A review. Journal of Manufacturing Processes, 25, 185–200. https://doi.org/10.1016/j.jmapro.2016.11.006
  • Sterling , A.J., Torries, B., Shamsaei, N., Thompson, S.M. and W.Seely, D.W. (2016). “Fatigue behavior and failure mechanisms of direct laser deposited Ti–6Al–4V.” Materials Science and Engineering: A Volume 655, 8 February 2016, pp. 100-112. https://doi.org/10.1016/j.msea.2015.12.026
  • Sutton, R. S., & Barto, A. G. (2015). Reinforcement Learning (2nd ed.). New York: The MIT Press.
  • Tapia, G., Elwany, A. H., & Sang, H. (2016). Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models. Additive Manufacturing, 12, 282–290. https://doi.org/10.1016/J.ADDMA.2016.05.009
  • Tapia, G., Khairallah, S., Matthews, M., King, W. E., & Elwany, A. (2017). Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel. International Journal of Advanced Manufacturing Technology, 94, 3591–3603. https://doi.org/10.1007/S00170-017-1045-Z
  • Thompson, M. K., Moroni, G., Vaneker, T., Fadel, G., Campbell, R. I., Gibson, I., et al. (2016). Design for additive manufacturing: Trends, opportunities, considerations, and constraints. CIRP Annals - Manufacturing Technology, 65, 737–760. https://doi.org/10.1016/j.cirp.2016.05.004
  • Vaezi, M., Seitz, H. & Yang, S. (2013). A review on 3D micro-additive manufacturing technologies. The International Journal of Advanced Manufacturing Technology. Volume 67, pages 1721–1754. https://doi.org/10.1007/s00170-012-4605-2
  • Wang, C., Tan, X. P., Tor, S. B., & Lim, C. S. (2020). Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing, 36, 101538. https://doi.org/10.1016/j.addma.2020.101538
  • Wang, C., Tan, X., Liu, E., & Tor, S. B. (2018b). Process parameter optimization and mechanical properties for additively manufactured stainless steel 316L parts by selective electron beam melting. Materials and Design, 147, 157–166. https://doi.org/10.1016/J.MATDES.2018.03.035
  • Wang, L., Törngren, M., & Onori, M. (2015). Current status and advancement of cyber-physical systems in manufacturing. Journal of Manufacturing Systems, 37, 517–527. https://doi.org/10.1016/J.JMSY.2015.04.008
  • Wang, T., Kwok, T. H., Zhou, C., & Vader, S. (2018a). In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing. Journal of Manufacturing Systems, 47, 83–92. https://doi.org/10.1016/J.JMSY.2018.04.003
  • Wang, Y. and Li, X. (2020). “An accurate finite element approach for programming 4D-printed self-morphing structures produced by fused deposition modeling.” Mech. Mater. 2020, 151, pp. 103628. https://doi.org/10.1016/j.mechmat.2020.103628
  • Weiss, S. M., Dhurandhar, A., Baseman, R. J., White, B. F., Logan, R., Winslow, J. K., et al. (2014). Continuous prediction of manufacturing performance throughout the production lifecycle. Journal of Intelligent Manufacturing, 27, 751–763. https://doi.org/10.1007/S10845-014-0911-X
  • Widrow, B., & Lehr, M. A. (1990). 30 years of adaptive neural networks: perceptron, madaline, and backpropagation. Proceedings of the IEEE, 78, 1415–1442. https://doi.org/10.1109/5.58323
  • Wu, D., Jennings, C., Terpenny, J., Gao, R. X., & Kumara, S. (2017). A comparative study on machine learning algorithms for smart manufacturing: Tool wear prediction using random forests. Journal of Manufacturing Science and Engineering Transactions on ASME, 2017, 139. https://doi.org/10.1115/1.4036350
  • Wu, D., Rosen, D. W., Wang, L., & Schaefer, D. (2015a). Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation. Computer Design, 59, 1–14. https://doi.org/10.1016/J.CAD.2014.07.006
  • Wu, H., Wang, Y., & Yu, Z. (2015b). In situ monitoring of FDM machine condition via acoustic emission. International Journal of Advanced Manufacturing Technology, 84, 1483–1495. https://doi.org/10.1007/S00170-015-7809-4
  • Wu, H., Yu, Z., & Wang, Y. (2016a). Real-time FDM machine condition monitoring and diagnosis based on acoustic emission and hidden semi-Markov model. International Journal of Advanced Manufacturing Technology, 90, 2027–2036. https://doi.org/10.1007/S00170-016-9548-6
  • Wu, H., Yu, Z., et al. (2016b). A new approach for online monitoring of additive manufacturing based on acoustic emission. Asmedigitalcollection. https://doi.org/10.1115/MSEC2016-8551
  • Wu, M., Phoha, V. V., Moon, Y. B., & Belman, A. K. (2016). Detecting malicious defects in 3D printing process using machine learning and image classification. ASME International Mechanical Engineering & Congress and Exposition Proceedings. https://doi.org/10.1115/IMECE201667641
  • Wu, M., Song, Z., & Moon, Y. B. (2017). Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods. Journal of Intelligence and Manufacturing, 30, 1111–1123. https://doi.org/10.1007/S10845-017-1315-5
  • Wu, X., Xu, C., Zhang, Z. and Guo, C. (2020). “Modeling and visualization of layered curing conversion profile in ceramic mask projection stereolithography process.” Ceram. Int. 2020, 46, pp. 25750–25757. https://doi.org/10.1016/j.ceramint.2020.07.053
  • Xing, C., Jia, C., Han, Y., Dong, S., Yang, J., & Wu, C. (2020). Numerical analysis of the metal transfer and welding arc behaviors in underwater flux-cored arc welding. International Journal of Heat and Mass Transfer, 153, 119570. https://doi.org/10.1016/J.IJHEATMASSTRANSFER.2020.119570
  • Yang, J., Li, F., Wang, Z. and Zeng, X. (2015). “Cracking behavior and control of Rene 104 superalloy produced by direct laser fabrication.” Journal of Materials Processing Technology Volume 225, November 2015, Pages 229-239. https://doi.org/10.1016/j.jmatprotec.2015.06.002
  • Yao, X., Moon, S. K., & Bi, G. (2017). A hybrid machine learning approach for additive manufacturing design feature recommendation. Rapid Prototyping Journal, 23, 983–997. https://doi.org/10.1108/RPJ-03-2016-0041
  • Zhang, H.-C., & Huang, S. H. (1995). Applications of neural networks in manufacturing: A state-of-the-art survey. International Journal of Production Research, 33, 705–728. https://doi.org/10.1080/00207549508930175
  • Zhang, Y. and Chou, Y.K. (2006). “Three-dimensional finite element analysis simulations of the fused deposition modelling process.” Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., 220, pp. 1663–1671. https://doi.org/10.1243/09544054JEM572
  • Zhang, Y., Hong, G. S., Ye, D., Zhu, K., & Fuh, J. Y. H. (2018b). Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring. Materials and Design, 156, 458–469. https://doi.org/10.1016/J.MATDES.2018.07.002
  • Zhang, Y., Jarosinski, W., Jung, Y.G. and Zhang, J. (2018). “Additive manufacturing processes and equipment. In Additive Manufacturing: Materials, Processes, Quantifications and Applications.” Elsevier: Amsterdam, The Netherlands. pp. 39–51. https://doi.org/10.1016/B978-0-12-812155-9.00002-5
  • Zhang, Z., Liu, Z., & Wu, D. (2021). Prediction of melt pool temperature in directed energy deposition using machine learning. Additive Manufacturing, 37, 101692. https://doi.org/10.1016/J.ADDMA.2020.101692
Toplam 92 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Nöral Ağlar, Katmanlı Üretim
Bölüm Derleme
Yazarlar

Filiz Karaömerlioğlu 0000-0002-4677-4365

Mustafa Ucar 0000-0002-1851-2317

Yayımlanma Tarihi 3 Mart 2025
Gönderilme Tarihi 3 Aralık 2024
Kabul Tarihi 27 Aralık 2024
Yayımlandığı Sayı Yıl 2025Cilt: 28 Sayı: 1

Kaynak Göster

APA Karaömerlioğlu, F., & Ucar, M. (2025). A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 551-582.
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