Analiza zastosowania sztucznej inteligencji w produkcji spawalniczej. Cz. II

• Autorzy: Klimpel Andrzej
• Języki publikacji: PL

Abstrakty

PL

Artykuł opisuje podstawy zastosowania sztucznej inteligencji, wykorzystującej nowoczesne systemy komputerowe do budowy cyfrowych bliźniaków, uczenia maszynowego, głębokiego uczenia wraz z architekturą głębokich sieci neuronowych.

Słowa kluczowe

PL

spawanie laserowe; spawanie GMA; spawanie hybrydowe

• Wydawca: ELAMED Media Group
• Czasopismo: Stal, Metale & Nowe Technologie
• Rocznik: 2023
• Tom: nr 5-6
• Strony: 24--31
• Opis fizyczny: Bibliogr. 34 poz., rys.

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”, 2021, 63, 121-129. https://doi.org/10.1016/j.jmapro.2020.04.043.
• 3. Johannes Günther et al.: Intelligent laser welding through representation, prediction, and control learning: An architecture with deep neural networks and reinforcement learning. „Mechatronics”, 2016, 34, 1-11. http://dx.doi.org/10.1016/j.mechatronics.2015.09.004.
• 4. Ch. Knaak et al.: Machine learning as a comparative tool to determine the relevance of signal features in laser welding. Procedia CIRP, 2018, 74, 623-627. 10th CIRP Conference on Photonic Technologies [LANE 2018].
• 5. Emmanuel Af rane Gyasi et al.: Survey on artif icial intelligence (AI) applied in welding: A future scenario of the influence of AI on technological, economic, educational and social changes. „Procedia Manufacturing”, 2019, 38, 702-714.
• 6. Qiyue Wang et al.: Deep learning-empowered digital twin for visualized weld joint growth monitoring and penetration control. „Journal of Manufacturing Systems”, 2020, 57, 429-439. https://doi.org/10.1016/j.jmsy.2020.10.002.
• 7. Baicun Wanga et al.: Intelligent welding system technologies: State-of-the-art review and perspectives. „Journal of Manufacturing Systems”, 2020, 56, 373–391.
• 8. Wang Cai et al.: Real-time monitoring of laser keyhole welding penetration state based on deep belief network. „Journal of Manufacturing Processes”, 2021, 72, 203-214. https://doi.org/10.1016/j.jmapro.2021.10.027.
• 9. Syed Quadir Moinuddin et al.: A study on weld defects classification in gas metal arc welding process using machine learning techniques. „Materials Today: Proceedings”, 2021, 43, 623-628. https://doi.org/10.1016/j.matpr.2020.12.159.
• 10. Chao Chen et al.: Prediction of welding quality characteristics during pulsed GTAW process of aluminum alloy by multisensory fusion and hybrid network model. „Journal of Manufacturing Processes”, 2021, 68, 209-224. https://doi.org/10.1016/j.jmapro.2020.08.028
• 11. Rishikesh Mahadevan R. et al.: Intelligent welding by using machine learning techniques. „Materials Today: Proceedings”, 2021, 46, 7402-7410. https://doi.org/10.1016/j.matpr.2020.12.1149.
• 12. Rogfel Thompson Martínez et al.: Analysis of GMAW proces with deep learning and machine learning techniques. „Journal of Manufacturing Processes”, 2021, 62, 695-703.https://doi.org/10.1016/j.jmapro.2020.12.052.
• 13. Fengjing Xu et al.: Application of sensing technology in inteligent robotic arc welding: A review. „Journal of Manufacturing Processes”, 2022, 79, 854-880. https://doi.org/10.1016/j.jmapro.2022.05.029.
• 14. Xiaoji Ma, YuMing Zhang: Reflection of illumination laser from gas metal arc weld pool surface. „Measurement Science and Technology”, 2019, 20, 115105 (8pp). doi:10.1088/0957-0233/20/11/115105.
• 15. Rodewald G.: Autoenkodery. Podstawy budowy wydajnych modeli uczenia maszynowego. „Zeszyty Naukowe WWSI”, 2022, no 26, vol. 16, pp. 21-60 DOI: 10.26348/znwwsi.26.21.
• 16. Olsson R. and Kaplan A.F.H.: Challenges to the interpretation of the electromagnetic feedback from laser welding. „Opt. Laser Eng.”, 2011, 49, 188-194.
• 17. You X. Gao and Katayama S.: 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-weldingin-automating-manufacturing-systems-ami/.
• 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. Klimpel A.: Podstawy teoretyczne cięcia laserowego metali. „Przegląd Spawalnictwa”, 2012, 6, 2-7.
• 24. Klimpel A.: Jakość cięcia laserowego metali. „STAL. Metale &Nowe Technologie”, 2012, maj-czerwiec, 48-54.
• 25. Klimpel A. 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. Mills B., Grant‐Jacob James A.: 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 proces parameters. „Optics and Laser Technology”, 2021, 141, 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”, 2017, 174, 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”, 2022, 149, 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”, 2020, 108, 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-industryprofits-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).