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Progress in Additive Manufacturing

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22-01-2022 | Full Research Article

An integrated process and data framework for the purpose of knowledge management and closed-loop quality feedback in additive manufacturing

Authors: Mostafizur Rahman, David Brackett, Katy Milne, Alex Szymanski, Annestacy Okioga, Lina Huertas, Swati Jadhav

Published in: Progress in Additive Manufacturing | Issue 4/2022

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Abstract

The Additive Manufacturing (AM) process chain has many steps, each of which generates data, potentially, in different formats. This paper aims to show how these data may be used together to mature the process. However, there are many challenges to getting these data and using it to generate knowledge and close the feedback loop. The biggest current challenges which were common to the AM end uses are: identifying key process variables, knowing which data to capture during the process, understanding how to use in-line inspection to detect defects and managing and using the data collected during the whole AM process chain. The digital process itself is not digital, there is still a lot of work done manually, especially data and information handling, and very limited use of data analysis and knowledge management. This paper maps the processes and the data along the AM process chain and proposed an integrated process and data framework for Additive Manufacturing for the purpose of knowledge management and closed-loop feedback.

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Cotteleer M, Trouton S, Dobner E (2016) 3D opportunity and the digital thread. Available at: https://www2.deloitte.com/us/en/insights/focus/3d-opportunity/3d-printing-digital-thread-in-manufacturing.html. Accessed 15 Dec 2021

Gaikwad A, Yavari R, Montazeri M, Cole K, Bian L, Rao P (2020) Toward the digital twin of additive manufacturing: Integrating thermal simulations, sensing, and analytics to detect process faults. IISE Trans 52(11):1204–1217. https://doi.org/10.1080/24725854.2019.1701753CrossRef

Kim DB, Witherell P, Lu Y, Feng S (2017) Toward a digital thread and data package for metals-additive manufacturing. Smart Sustain Manuf Syst 1(1):75–99. https://doi.org/10.1520/SSMS20160003CrossRef

Gisario A, Kazarian M, Martina F, Mehrpouya M (2019) Metal additive manufacturing in the commercial aviation industry: a review. J Manuf Syst 53:124–149. https://doi.org/10.1016/j.jmsy.2019.08.005CrossRef

‘Rolls-Royce is accelerating the integration of 3D printing into its production process’, 3Dnatives, Dec. 14, 2020. https://www.3dnatives.com/en/rolls-royce-3d-printing-production-141220205/. Accessed 14 May 2021

‘GE Is Building The World’s Largest “Additive” Machine For 3D Printing Metals | GE News’. https://www.ge.com/news/reports/ge-building-worlds-largest-additive-machine-3d-printing-metals. Accessed 14 May 2021

Wegner A, Witt G (2012) Correlation of process parameters and part properties in laser sintering using response surface modeling. Phys Proc 39:1875–3892 (480490)CrossRef

Chua ZY, Ahn IH, Moon SK (2017) Process monitoring and inspection systems in metal additive manufacturing: Status and applications. Int J Precis Eng Manuf-Green Technol 4(2):235–245. https://doi.org/10.1007/s40684-017-0029-7CrossRef

Yoder S et al (2018) Characterization of topology optimized Ti-6Al-4V components using electron beam powder bed fusion. Addit Manuf 19:184–196. https://doi.org/10.1016/J.ADDMA.2017.12.001CrossRef

10.

Lu Y, Choi S, Witherell P (2015) Towards an integrated data schema design for additive manufacturing: Conceptual modeling, NIST. Available at: https://www.nist.gov/publications/towards-integrated-data-schema-design-additive-manufacturing-conceptual-modeling. Accessed 15 Dec 2021

11.

Majeed A, Lv J, Peng T (2018) A framework for big data driven process analysis and optimization for additive manufacturing. Rapid Prototyp J 25(2):308–321. https://doi.org/10.1108/RPJ-04-2017-0075CrossRef

12.

Dickens P, Minshall T (2015) Update report 2: what did the initial evidence reveal?. https://doi.org/10.13140/RG.2.1.1519.0001

13.

Tofail SAM, Koumoulos EP, Bandyopadhyay A, Bose S, O’Donoghue L, Charitidis C (2018) Additive manufacturing: scientific and technological challenges, market uptake and opportunities. Mater Today 21(1):22–37. https://doi.org/10.1016/j.mattod.2017.07.001CrossRef

14.

Ngo TD, Kashani A, Imbalzano G, Nguyen KTQ, Hui D (2018) Additive manufacturing (3D printing): A review of materials, methods, applications and challenges. Compos Part B Eng 143:172–196. https://doi.org/10.1016/j.compositesb.2018.02.012CrossRef

15.

Lyons JG, Devine DM (2019) Additive manufacturing: future challenges. In: Devine DM (ed) Polymer-based additive manufacturing: biomedical applications. Springer International Publishing, Cham, pp 255–264. https://doi.org/10.1007/978-3-030-24532-0_12CrossRef

16.

Gao W et al (2015) The status, challenges, and future of additive manufacturing in engineering. Comput-Aided Des 69:65–89. https://doi.org/10.1016/j.cad.2015.04.001CrossRef

17.

‘Additive manufacturing: challenges, trends, and applications—Osama Abdulhameed, Abdulrahman Al-Ahmari, Wadea Ameen, Syed Hammad Mian, 2019’. https://doi.org/10.1177/1687814018822880. Accessed 14 May 2021

18.

‘Precision Metal Additive Manufacturing’, Routledge & CRC Press. https://www.routledge.com/Precision-Metal-Additive-Manufacturing/Leach-Carmignato/p/book/9781138347717. Accessed 14 May 2021

19.

Zhou Y, Chen H, Tang YSGMU, (2014) Simulation and optimization framework for additive manufacturing processes. ieeexplore.ieee.org, pp 34–40

20.

Cummings IT, Bax ME, Fuller IJ, Wachtor AJ, Bernardin JD (2017) A framework for additive manufacturing process monitoring & control. Top Modal Anal Test 10:137–146. https://doi.org/10.1007/978-3-319-54810-4_14CrossRef

21.

Majeed A et al (2021) A big data-driven framework for sustainable and smart additive manufacturing. Robot Comput-Integr Manuf 67:102026. https://doi.org/10.1016/j.rcim.2020.102026CrossRef

22.

Togwe T, Tanju B, Eveleigh TJ (2018) Using a systems engineering framework for additive manufacturing. Syst Eng 21(5):466–475. https://doi.org/10.1002/sys.21447CrossRef

23.

Liu C, Le Roux L, Körner C, Tabaste O, Lacan F, Bigot S (2020) Digital twin-enabled collaborative data management for metal additive manufacturing systems. J Manuf Syst. https://doi.org/10.1016/j.jmsy.2020.05.010CrossRef

24.

Adedeji A (2013) Topology optimisation for additive manufacturing. Loughborough University’

25.

Hao L, Zhang D, Mellor S, J. International (2014) Additive manufacturing: a framework for implementation. Prod Econ Issue 149:194–201

26.

Schmid M, Levy G (2012) ‘Quality management and estimation of quality costs for additive manufacturing with SLS. Inspire, Gallen

27.

Liu X, Rosen DW (2010) Ontology based knowledge modeling and reuse approach of supporting process planning in layer-based additive manufacturing. In: 2010 international conference on manufacturing automation. IEEE, pp 261–266

28.

Mies D, Marsden W, Warde S (2016) Overview of additive manufacturing informatics: “A Digital Thread.” Integr Mater Manuf Innov 5(1):6. https://doi.org/10.1186/s40192-016-0050-7CrossRef

29.

Kim DB, Witherell P, Lipman R, Feng SC (2015) Streamlining the additive manufacturing digital spectrum: a systems approach. Addit Manuf 5:20–30

30.

Akande SO (2015) Development of quality system for additive manufacturing. Newcastle Univ, Newcastle

31.

Yeong WY, Chua CK (2013) A quality management framework for implementing additive manufacturing of medical devices. Virtual Phys Prototyp 8(3):193–199CrossRef

32.

Yoon S, Suh S-H (2016) Manufacturing information bus from the perspective of cyber physical manufacturing system (CPMS). IFAC-Pap 49(31):103–108. https://doi.org/10.1016/J.IFACOL.2016.12.169CrossRef

33.

Kamath C (2016) Data mining and statistical inference in selective laser melting. Int J Adv Manuf Technol 86(5):1659–1677. https://doi.org/10.1007/s00170-015-8289-2CrossRef

34.

Kamath C (2016) On the use of data mining techniques to build high-density, additively-manufactured parts. In: Lookman T, Alexander FJ, Rajan K (eds) Information science for materials discovery and design. Springer International Publishing, Cham, pp 141–155. https://doi.org/10.1007/978-3-319-23871-5_7CrossRef

35.

Meng L et al (2020) Machine learning in additive manufacturing: a review. JOM 72(6):2363–2377. https://doi.org/10.1007/s11837-020-04155-yCrossRef

36.

Wang C, Tan XP, Tor SB, Lim CS (2020) Machine learning in additive manufacturing: state-of-the-art and perspectives. Addit Manuf 36:101538. https://doi.org/10.1016/j.addma.2020.101538CrossRef

37.

Winkler M, Jacobs G, Stürmer S, Konrad C (2020)‘Application of data mining for the identification of parts for additive manufacturing. In: 2020 International Conference on intelligent engineering and management (ICIEM), Jun. 2020, pp 281–288. https://doi.org/10.1109/ICIEM48762.2020.9160307.

38.

Huber S, Wiemer H, Schneider D, Ihlenfeldt S (2019) DMME: Data mining methodology for engineering applications—a holistic extension to the CRISP-DM model. Proc CIRP 79:403–408. https://doi.org/10.1016/j.procir.2019.02.106CrossRef

39.

Okaro IA, Jayasinghe S, Sutcliffe C, Black K, Paoletti P, Green PL (2019) Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning. Addit Manuf 27:42–53. https://doi.org/10.1016/j.addma.2019.01.006CrossRef

Title: An integrated process and data framework for the purpose of knowledge management and closed-loop quality feedback in additive manufacturing
Authors: Mostafizur Rahman
David Brackett
Katy Milne
Alex Szymanski
Annestacy Okioga
Lina Huertas
Swati Jadhav
Publication date: 22-01-2022
Publisher: Springer International Publishing
Published in: Progress in Additive Manufacturing / Issue 4/2022
Print ISSN: 2363-9512
Electronic ISSN: 2363-9520
DOI: https://doi.org/10.1007/s40964-021-00246-7

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