Abstract
The traditional manufacturing methods have advanced greatly with the introduction of computers in the manufacturing sector. 3d printing is one of the most advanced and automated manufacturing techniques due to its capability to manufacture precise, complex, multi-functional and personalized designs. However, this process of selection of correct process parameters in 3d printing is affected by many attributes. So, the use of artificial intelligence in 3d printing has started to counter this problem at various stages of 3d printing process. In this work scientometric analysis of artificial intelligence in 3d printing is presented by the authors. The keywords visualization netrow is presented using VOSviewer software.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jeon JW, Ha YY (2000) A generalized approach for the acceleration and deceleration of industrial robots and CNC machine tools. IEEE Trans Ind Electron 47:133–139
Chen Y, Dong F (2013) Robot machining: recent development and future research issues. Int J Adv Manuf 66:1489–1497
Skylar-Scott MA, Mueller J, Visser CW, Lewis JA (2019) Voxelated soft matter via multimaterial multinozzle 3D printing. Nature 575:330–335
Gu GX, Libonati F, Wettermark SD, Buehler MJ (2017) Printing nature: unraveling the role of nacre’s mineral bridges. J Mech Behav Biomed Mater 76:135–144
Cui H, Hensleigh R, Yao D, Maurya D, Kumar P, Kang MG, Priya S, Zheng X (2019) Three-dimensional printing of piezoelectric materials with designed anisotropy and directional response. Nat Mater 18:234–241
Zhang Z, Demir KG, Gu GX (2019) Developments in 4D-printing: a review on current smart materials, technologies, and applications. Int J Smart Nano Mater 10:205–224
Vangelatos Z, Zhang Z, Gu GX, Grigoropoulos CP (2020) Tailoring the dynamic actuation of 3D-printed mechanical metamaterials through inherent and extrinsic instabilities. Adv Eng Mater 22:1901586
Zohdi TI (2014) Additive particle deposition and selective laser processing-a computational manufacturing framework. Comput Mech 54:171–191
Frketic J, Dickens T, Ramakrishnan S (2017) Automated manufacturing and processing of fiber-reinforced polymer (FRP) composites: an additive review of contemporary and modern techniques for advanced materials manufacturing. Addit Manuf 14:69–86
Scime L, Beuth J (2018) Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Addit Manuf 19, 114–126
Brackett D, Ashcroft I, Hague R. In Proceedings of the Solid Freeform Fabrication Symposium, pp 348–362
Rozvany G (2001) Aims, scope, methods, history and unified terminology of computeraided topology optimization in structural mechanics. Struct Multidiscipl Optim 21:90–108
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dadhwal, R., Kumar, R., Singh Chohan, J., Singh, S., Maurya, S. (2023). Research Trends and Applications of Artificial Intelligence in 3D Printing-A Scientometric Analysis. In: Maurya, S., Peddoju, S.K., Ahmad, B., Chihi, I. (eds) Cyber Technologies and Emerging Sciences. Lecture Notes in Networks and Systems, vol 467. Springer, Singapore. https://doi.org/10.1007/978-981-19-2538-2_39
Download citation
DOI: https://doi.org/10.1007/978-981-19-2538-2_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2537-5
Online ISBN: 978-981-19-2538-2
eBook Packages: EngineeringEngineering (R0)