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Digital Twins of Hybrid Additive and Subtractive Manufacturing Systems–A Review

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Machining and Additive Manufacturing (CPIE 2023)

Abstract

Digital twins of hybrid additive and subtractive manufacturing systems refer to the creation of virtual replicas of these systems, which combine both additive and subtractive manufacturing processes. These digital twins are designed to simulate and optimize the entire manufacturing process, from the initial design to the finished product, using data from the physical system. Hybrid additive and subtractive manufacturing systems are becoming increasingly popular in modern manufacturing processes due to their ability to combine the benefits of both additive and subtractive processes. These systems can produce complex geometries with high precision and accuracy, while also being able to remove excess material and achieve smoother surface finishes. Digital twins of these systems allow manufacturers to simulate and optimize the entire manufacturing process in a virtual environment, before implementing it in the physical system. This enables them to identify and mitigate any potential issues or inefficiencies in the process, leading to reduced costs, improved quality, and faster time-to-market. The creation of digital twins involves the use of advanced modeling and simulation tools, as well as the integration of data from multiple sources, such as CAD models, sensor data, and historical performance data. These tools enable manufacturers to create accurate and realistic virtual replicas of their hybrid additive and subtractive manufacturing systems, which can be used for a variety of purposes, such as design optimization, process validation, and performance monitoring. Overall, digital twins of hybrid additive and subtractive manufacturing systems are a powerful tool for modern manufacturers, providing them with the ability to optimize their manufacturing processes and achieve better results with reduced costs and faster turnaround times.

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Abbreviations

DT:

Digital Twin

IOT:

Internet of Things

IIOT:

Indusrial Internet of Things

HASM:

Hybrid Additive and Subtractive Manufacturing Systems

References

  1. Gopal L, Singh H, Mounica P, Mohankumar N, Challa NP, Jayaraman P (2023) Digital twin and IOT technology for secure manufacturing systems. Meas: Sens 25. https://doi.org/10.1016/j.measen.2022.100661

  2. Bhattacharya M, Penica M, O’Connell E, Southern M, Hayes M (2023) Human-in-loop: a review of smart manufacturing deployments. Systems 11:35. https://doi.org/10.3390/systems11010035

    Article  Google Scholar 

  3. Isaja M, Nguyen P, Goknil A, Sen S, Husom EJ, Tverdal S, Anand A, Jiang Y, Pedersen KJ, Myrseth P, Stang J, Niavis H, Pfeifhofer S, Lamplmair P (2023) A blockchain-based framework for trusted quality data sharing towards zero-defect manufacturing. Comput Ind 146. https://doi.org/10.1016/j.compind.2023.103853

  4. Nath SV, van Schalkwyk P, Isaacs D (2021) Building industrial digital twins design, develop, and deploy digital twin solutions for real-world industries using Azure Digital Twins. Packt Publishing, Limited

    Google Scholar 

  5. Bamunuarachchi D, Georgakopoulos D, Banerjee A, Jayaraman PP (2021) Digital twins supporting efficient digital industrial transformation. Sensors 21. https://doi.org/10.3390/s21206829

  6. Damjanovic-Behrendt V, Behrendt W (2019) An open source approach to the design and implementation of digital twins for smart manufacturing. Int J Comput Integr Manuf 32:366–384. https://doi.org/10.1080/0951192X.2019.1599436

    Article  Google Scholar 

  7. IEEE Communications Society. Internet of Things, AHSNTC, IEEE Internet of Things (Initiative), Institute of Electrical and Electronics Engineers: GIoTS, Global IoT Summit: 2020 conference proceedings

    Google Scholar 

  8. Makarov VL, Bakhtizin AR, Beklaryan GL (2019) Developing digital twins for production enterprises. Bus Inform 13:7–16. https://doi.org/10.17323/1998-0663.2019.4.7.16

  9. Mu H, He F, Yuan L, Commins P, Wang H, Pan Z (2023) Toward a smart wire arc additive manufacturing system: a review on current developments and a framework of digital twin. J Manuf Syst 67:174–189. https://doi.org/10.1016/j.jmsy.2023.01.012

    Article  Google Scholar 

  10. Botín-Sanabria DM, Mihaita S, Peimbert-García RE, Ramírez-Moreno MA, Ramírez-Mendoza RA, de Lozoya-Santos JJ (2022) Digital twin technology challenges and applications: a comprehensive review

    Google Scholar 

  11. Phua A, Davies CHJ, Delaney GW (2022) A digital twin hierarchy for metal additive manufacturing

    Google Scholar 

  12. Rasheed A, San O, Kvamsdal T (2019) Digital twin: values, challenges and enablers

    Google Scholar 

  13. Xie X, Merino J, Moretti N, Pauwels P, Chang JY, Parlikad A (2023) Digital twin enabled fault detection and diagnosis process for building HVAC systems. Autom Constr 146. https://doi.org/10.1016/j.autcon.2022.104695

  14. Kritzinger W, Karner M, Traar G, Henjes J, Sihn W (2018) Digital twin in manufacturing: a categorical literature review and classification. In: IFAC-PapersOnLine. Elsevier B.V., pp 1016–1022

    Google Scholar 

  15. Cai Y, Wang Y, Burnett M (2020) Using augmented reality to build digital twin for reconfigurable additive manufacturing system. J Manuf Syst 56:598–604. https://doi.org/10.1016/j.jmsy.2020.04.005

    Article  Google Scholar 

  16. Stojanovic N, Milenovic D (2019) Data-driven digital twin approach for process optimization: an industry use case. In: Proceedings-2018 IEEE International conference on big data, big data 2018. Institute of Electrical and Electronics Engineers Inc., pp 4202–4211

    Google Scholar 

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Correspondence to Rajat Jain .

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Jain, R., Bharat, N., Bose, P.S.C. (2024). Digital Twins of Hybrid Additive and Subtractive Manufacturing Systems–A Review. In: Sharma, V.S., Dixit, U.S., Gupta, A., Verma, R., Sharma, V. (eds) Machining and Additive Manufacturing. CPIE 2023. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-6094-1_18

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  • DOI: https://doi.org/10.1007/978-981-99-6094-1_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6093-4

  • Online ISBN: 978-981-99-6094-1

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