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Indian Railways on Fast Track with Welding Industry 4.0 : Application of Internet of Things and Artificial Intelligence
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The objective of this paper is to explain about application of Internet of Things (IoT) and Artificial Intelligence (AI) in welding of Indian Railways. The introduction of welding technology has also been followed by the country’s economic growth. Indian Railways has long been the single most significant infrastructure entity in India, with the railway track network expanding for many years. The new manufacturing sector is speeding the transition to digital and intelligent manufacturing, with the ongoing growth and maturity of cloud computing, big data, IoT and other innovations. Welding methods are also one of the fields where AI is tested and used early, with the help of information technology. Train maintenance and repair is usually carried out in demanding working conditions and frequently under demand from time. In such high demand and dynamic activities, it helps to decrease human error. In the welding of rail tracks and machine parts, IoT and AI will certainly offer many advantages in less time and with greater accuracy and precision. It will allow the Indian Railways to become more profitable and effective.
Keywords
Indian Railways, Internet of Things, Artificial Intelligence, Welding 4.0.
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- Avinash, B. (2020). Industry 4.0 and related technologies. ttps://www.apo-tokyo.org/ resources/articles/industry-4-0-and-related-technologies/
- Bonomi, F., Milito, R., Natarajan, P., Zhu, J. (2014). Fog computing: A Platform for Internet of Things and Analytics. In Big Data and Internet of Things: A Roadmap for Smart Environments (169-186). Springer.
- Chantry, B. (2021). Cloud based production monitoring reshapes weld performance tracking. https://www.lincolnelectric.com/en-us/support/process-and-theory/Pages/cloud-based-production-monitoring.aspx
- Chen, C., Lv, N., Chen, S. (2018). Data driven welding expert system structure based on internet of things, Transactions on Intelligent Welding Manufacturing, 45-60.
- Data assets. (2021) https://www.fronius.com/en/welding-technology/info-centre/magazine/2017/ successfully-leveraging-data-assets
- ESAB WeldCloud. (2021). https://www.esabna.com/us/en/weldcloud/index.cfm
- Indian Railways. (2020). https://icf.indianrailways.gov.in/view_section.jsp?lang=0&id=0,29
- Indian Railways. (2021). https://www. financialexpress.com/industry/indian-railways-to-introduce-ultrasonic-track-testing/772422/
- Ji, Z., Yanhong, Z., Baicun, W., & Jiyuan, Z. (2019). Human–Cyber–Physical Systems (HCPSs) in the context of new-generation intelligent manufacturing. Engineering, 5(4), 624-636. https://doi.org/10.1016/j.eng.2019.07.015
- Latz, B. (2018). How will the Internet of Things impact the welding & manufacturing industries. https://www.k-tig.com/2017-blog/how-will-the-internet-of-things-impact-the-welding-manufacturing-industries.
- Manca, D., Brambilla, S., & Colombo, S. (2013). Bridging between virtual reality and accident simulation for training of process-industry operators. Advances in Engineering Software, 55, 1-9. https://doi.org/10.1016/j.advengsoft.2012.09.002
- Nizam, M. S. H., Marizan, S., Zaki, S. A., & Mohd Zamzuri, A. R. (2016). Vision based identification and classification of weld defects in welding environments: A review. In Indian Journal of Science and Technology, 9(20), 1-15. https://doi.org/10.17485/ijst/2016/v9i20/82779
- Pan, Y. (2016). Heading toward Artificial Intelligence 2.0. Engineering, 2(4), 409-413. https://doi.org/10.1016/J.ENG.2016.04.018
- Posch, G., Jurgen, B., Krissanaphusit, A. (2017). Internet of Things / Industry 4.0 and Its Impact on Welding. Journal of Japan Welding Society. 86(4), 236-242.
- Real time welding data. (2021). https://www.metalformingmagazine.com/magazine/article/Default.asp?/2016/3/1/Captured:_Real_Time_Welding_Data_to_Optimize_Quality,_Efficiency
- Reisgen, U., Mann, S., Middeldorf, K., Sharma, R., Buchholz, G., Willms, K. (2019). Connected, digitalized welding production - industrie 4.0 in gas metal arc welding. Welding in the World. 63, 1121–1131. https://doi.org/10.1007/s40194-019-00723-2.
- Schuster, A., Kupke, M., & Larsen, L. (2017). Autonomous Manufacturing of Composite Parts by a Multi-Robot System. Procedia Manufacturing, 11, 249-255. https://doi.org/10.1016/j.promfg.2017.07.238
- Simoens, P., Dragone, M., & Saffiotti, A. (2018). The Internet of Robotic Things: A review of concept, added value and applications. International Journal of Advanced Robotic Systems, 15(1). https://doi.org/10.1177/1729881418759424
- Veikkolainen, M. (2017) Internet of Welding reaching for the top of competitiveness. https://weldingvalue.com/2017/05/internet-of-welding-reaching-for-the-top-of-competitiveness
- Villani, V., Pini, F., Leali, F., & Secchi, C. (2018). Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications. Mechatronics, 55, 248-266. https://doi.org/10.1016/j.mechatronics.2018.02.009
- Wang, B., Hu, S. J., Sun, L., & Freiheit, T. (2020). Intelligent welding system technologies: State-of-the-art review and perspectives. In Journal of Manufacturing Systems. 56, 373-391. https://doi.org/10.1016/j.jmsy.2020.06.020
- Welding data collection. (2021). https://www. fronius.com/en/welding-technology/world-of-welding/welding-data-collection
- Zhong, R.Y., Xu, X., Klotz, E., & Newman, S, T. (2017). Intelligent Manufacturing in the Context of Industry 4.0: A Review, Engineering, 3(5), 616–630.
- Zhou, J., Li, P., Zhou, Y., Wang, B., Zang, J., & Meng, L. (2018). Toward new-generation intelligent manufacturing. In Engineering, 4(1), 11-20. https://doi.org/10.1016/j.eng.2018.01.002
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