Analiza zastosowania sztucznej inteligencji w produkcji spawalniczej. Cz. I

• Autorzy: Klimpel Andrzej
• Języki publikacji: PL

Abstrakty

PL

Artykuł opisuje podstawy zastosowania sztucznej inteligencji, wykorzystującej nowoczesne systemy komputerowe do budowy cyfrowych bliźniaków, uczenie maszynowe, głębokie uczenie wraz z architekturą głębokich sieci neuronowych, w celu zwiększenia wydajności, ekonomiczności i jakości produkcji spawalniczej.

Słowa kluczowe

PL

sztuczna inteligencja w spawaniu; uczenie maszynowe; cyfrowy bliźniak

• Wydawca: ELAMED Media Group
• Czasopismo: Stal, Metale & Nowe Technologie
• Rocznik: 2023
• Tom: nr 3-4
• Strony: 36--39
• Opis fizyczny: Bibliogr. 34 poz., rys., wykr.

Twórcy

autor

Klimpel Andrzej

• Wydział Mechaniczny Technologiczny, Politechnika Śląska

Bibliografia

• 1. Sue Troy, 2020 State of AI Report, ITProToday.com.
• 2. Shi He, et al.: Study on the intelligent model database modeling the laser welding for aerospace aluminum alloy. Journal of Manufacturing Processes 63 (2021) 121–129. https://doi.org/10.1016/j.jmapro.2020.04.043.
• 3. Johannes Günther, at al.: Intelligent laser welding through representation, prediction, and control learning: An architecture with deep neural networks and reinforcement learning. Mechatronics 34 (2016) 1–11. http://dx.doi.org/10.1016/j.mechatronics.2015.09.004.
• 4. Ch. Knaak, at al.: Machine learning as a comparative tool to determine the relevance of signal features in laser welding. Procedia CIRP 74 (2018) 623–627. 10th CIRP Conference on Photonic Technologies [LANE 2018].
• 5. Emmanuel Afrane Gyasi, at al.: Survey on artificial intelligence (AI) applied in welding: A future scenario of the influence of AI on technological, economic, educational and social changes. Procedia Manufacturing 38 (2019) 702–714.
• 6. Qiyue Wang at al.: Deep learning-empowered digital twin for visualized weld joint growth monitoring and penetration control. Journal of Manufacturing Systems 57 (2020) 429–439. https://doi.org/10.1016/j.jmsy.2020.10.002.
• 7. Baicun Wanga, at al.: Intelligent welding system technologies: State-of-the-art review and perspectives. Journal of Manufacturing Systems 56 (2020) 373–391.
• 8. Wang Cai, at al.: Real-time monitoring of laser keyhole welding penetration state based on deep belief network. Journal of Manufacturing Processes 72 (2021) 203–214. https://doi.org/10.1016/j.jmapro.2021.10.027.
• 9. Syed Quadir Moinuddin, at al.: A study on weld defects classification in gas metal arc welding process using machine learning techniques. Materials Today: Proceedings 43 (2021) 623–628. https://doi.org/10.1016/j.matpr.2020.12.159.
• 10. Chao Chen, at al.: Prediction of welding quality characteristics during pulsed GTAW process of aluminum alloy by multisensory fusion and hybrid network model. Journal of Manufacturing Processes 68 (2021) 209–224. https://doi.org/10.1016/j.jmapro.2020.08.028
• 11. Rishikesh Mahadevan R, at al.: Intelligent welding by using machine learning techniques. Materials Today: Proceedings 46 (2021) 7402–7410. https://doi.org/10.1016/j.matpr.2020.12.1149.
• 12. Rogfel Thompson Martínez, at al.: Analysis of GMAW process with deep learning and machine learning techniques, Journal of Manufacturing Processes 62 (2021) 695–703.https://doi.org/10.1016/j.jmapro.2020.12.052.
• 13. Fengjing Xu, at al.: Application of sensing technology in intelligent robotic arc welding: A review. Journal of Manufacturing Processes 79 (2022) 854–880. https://doi.org/10.1016/j.jmapro.2022.05.029.
• 14. Xiaoji Ma, YuMing Zhang. Reflection of illumination laser from gasmetal arc weld pool surface. Measurement Science and Technology 20 (2009) 115105 (8pp). doi:10.1088/0957-0233/20/11/115105.
• 15. Gabriel Rodewald. Autoenkodery. Podstawy budowy wydajnych modeli uczenia maszynowego. Zeszyty Naukowe WWSI, No 26, Vol. 16, 2022, pp. 21-60 DOI: 10.26348/znwwsi.26.21.
• 16. R. Olsson and A. F. H. Kaplan: ‘Challenges to the interpretation of the electromagnetic feedback from laser welding’, Opt. Laser Eng., 2011, 49, 188–194.
• 17. You, X. Gao and S. Katayama: ‘Multiple-optics sensing of high-brightness disk laser welding process’, NDT & E Int., 2013, 60, 32–39.
• 18. https://www.ibm.com/cloud/learn/what-is-artificial-intelligence
• 19. https://learn.microsoft.com/pl-pl/azure/machine-learning/concept-deep-learning-vs-machine-learning
• 20. https://resources.arcmachines.com/the-role-of-smart-welding-in-automating-manufacturing-systems-ami/.https://www.fronius.com/en/welding-technology/info-centre/press/intelligent-welding-process-management-170521
• 21. https://www.kunststoff-cluster.at/partnerunternehmen/partnernews/detail/news/smart-welding-solutions
• 22. https://siemens.mindsphere.io/en/solutions/partner-use-cases/livnsense-use-case-smart-welding-management
• 23. A. Klimpel: Podstawy teoretyczne cięcia laserowego metali. Przegląd Spawalnictwa. Nr 6/2012, s. 2-7.
• 24. A. Klimpel: Jakość cięcia laserowego metali. STAL. Metale & Nowe Technologie. MAJ-CZERWIEC 2012, s. 48-54.
• 25. A. Klimpel, et al.: Experimental investigations of the influence of laser beam and plasma arc cutting parameters on edge quality of HSLA strips and plates. Journal of Materials Processing Technology. 2016.
• 26. Benjamin Mills, James A. Grant‐Jacob: Lasers that learn: The interface of laser machining and machine learning. IET Optoelectronics, 2021;15:207–224. DOI:10.1049/ote2.12039
• 27. Leonie Tatzel, et al.: Image-based modelling and visualisation of the relationship between laser-cut edge and process parameters. Optics and Laser Technology 141 (2021) 107028. https://doi.org/10.1016/j.optlastec.2021.107028.
• 28. Yan-liang Zhang,Jun-hui Lei: Prediction of Laser Cutting Roughness in Intelligent Manufacturing Mode Based on ANFIS. Procedia Engineering 174 ( 2017 ) 82 – 89.
• 29. Muhamad Nur Rohman, et al. : Prediction and optimization of geometrical quality for pulsed laser cutting of non-oriented electrical steel sheet. Optics & Laser Technology 149 (2022) 107847. https://doi.org/10.1016/j.optlastec.2022.107847.
• 30. Hua Ding, Zongcheng Wang, Yicheng Guo: Multi-objective optimization of fiber laser cutting based on generalized regression neural network and non-dominated sorting genetic algorithm. Infrared Physics & Technology, 108, 2020, https://doi.org/10.1016/j.infrared.2020.103337.
• 31. Nikita Levichev, et al.: Real-time monitoring of fiber laser cutting of thick plates by means of photodiodes. 11th CIRP Conference on Photonic Technologies [LANE 2020] on September 7-10, 2020. Procedia CIRP 94 (2020) 499–504.
• 32. https://www.lorch.eu/downloads-public/broschueren-flyer-kataloge/913.1183.1-EN-Lorch-SmartWelding-V6.pdf.
• 33. https://ttpsc.com/en/success-stories/how-welding-industry-profits-from-smart-connected-products-iot-solution/.
• 34. https://www.fronius.com/en/welding-technology/info-centre/press/intelligent-welding-process-management-170521

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).