Skip to main content
SpringerLink
Log in

A Systematic Review of Additive Manufacturing Solutions Using Machine Learning, Internet of Things, Big Data, Digital Twins and Blockchain Technologies: A Technological Perspective Towards Sustainability

  • Review article
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

New manufacturing expertise, along with user expectations for gradually modified products and facilities, is creating changes in manufacturing scale and distribution. Standardization is essential for every industrial manufactured sector that delivers goods to consumers. Digital manufacturing (DM) is a vital component in the scheduling of all knowledge-based manufacturing. Additive Manufacturing (AM) is recognized as a useful technique in the area of sustainable development goals (SDGs). Modern Development techniques are inspected as a tool for the practices that are being adopted. Additive Manufacturing (AM) was introduced as an advanced technology that includes a new era of complicated machinery and operating systems. Cloud manufacturing framework makes it much easier to gain access to a variety of AM resources while investing as little as possible. This paper contributes an overview of used technologies advancement in the era of Additive manufacturing such as IoT, Big Data, ML, Digital twins, and Blockchain, and their contribution to Industry 4.0 for better and effective design, development, and production while at the same time providing a richer and ethical environment.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. U. Kingdom and N. America, The Significance of Manufacturing 1 1.1, 2015, https://doi.org/10.1016/B978-0-12-799945-6.00001-6.

  2. Javaid M, Haleem A, Pratap R, Suman R, Rab S (2021) Role of additive manufacturing applications towards environmental sustainability. Adv Ind Eng Polym Res. https://doi.org/10.1016/j.aiepr.2021.07.005

    Article  Google Scholar 

  3. Colorado HA, Gutiérrez EI, Neves S (2020) Sustainability of additive manufacturing : the circular economy of materials and environmental perspectives. Integr Med Res 9(4):8221–8234. https://doi.org/10.1016/j.jmrt.2020.04.062

    Article  Google Scholar 

  4. Huang SH, Liu P, Mokasdar A (2013) Additive manufacturing and its societal impact: a literature review. Int J Adv Manuf Technol 67:1191–1203. https://doi.org/10.1007/s00170-012-4558-5

    Article  Google Scholar 

  5. Enrique M, Korner H, Albajez A, Santolaria J, Ng C (2020) Systematic literature review: integration of additive. Metals. https://doi.org/10.3390/met10081061

    Article  Google Scholar 

  6. A. S. Review et al., Digital twins for additive manufacturing. Appl Sci.

  7. Abdulhameed O, Al-ahmari A, Ameen W, Mian SH (2019) Additive manufacturing: challenges, trends, and applications. Adv Mech Eng 11(2):1–27. https://doi.org/10.1177/1687814018822880

    Article  Google Scholar 

  8. Alcácer V, Cruz-machado V (2019) Scanning the industry 4.0: a literature review on technologies for manufacturing systems. Eng Sci Tech Int J 22:899–919. https://doi.org/10.1016/j.jestch.2019.01.006

    Article  Google Scholar 

  9. Lu Y (2017) Journal of industrial information integration industry 4. 0: a survey on technologies, applications and open research issues. J Ind Inf Integr 6:1–10. https://doi.org/10.1016/j.jii.2017.04.005

    Article  Google Scholar 

  10. Ford S, Despeisse M (2016) Additive manufacturing and sustainability: an exploratory study of the advantages and challenges. J Clean Prod. https://doi.org/10.1016/j.jclepro.2016.04.150

    Article  Google Scholar 

  11. Attaran M (2017) The rise of 3-D printing : the advantages of additive manufacturing over traditional manufacturing. Bus Horiz 60(5):677–688. https://doi.org/10.1016/j.bushor.2017.05.011

    Article  Google Scholar 

  12. Kumar V, Dutta D, Cam CAD (1997) An assessment of data formats for layered manufacturing. Adv Eng Software 99780(96):151–164

    Article  Google Scholar 

  13. Hofmann E, Rüsch M (2017) Industry 4.0 and the current status as well as future prospects on logistics. Comput Ind 89:23–34. https://doi.org/10.1016/j.compind.2017.04.002

    Article  Google Scholar 

  14. Georgakopoulos D, Jayaraman PP, Fazia M, Villari M, Ranjan R (2016) Internet of things and edge cloud computing roadmap for manufacturing. IEEE Cloud Computing 3(4):66–73. https://doi.org/10.1109/MCC.2016.91

    Article  Google Scholar 

  15. Motyl B, Baronio G, Uberti S, Speranza D, Filippi S (2017) How will change the future engineers’ skills in the Industry 4. 0 framework ? A questionnaire survey. Procedia Manuf 11:1501–1509. https://doi.org/10.1016/j.promfg.2017.07.282

    Article  Google Scholar 

  16. Alkhader W, Alkaabi N, Salah K, Jayaraman R (2020) Blockchain-based traceability and management for additive manufacturing. Int J Adv Manuf Technol 4:1–14. https://doi.org/10.1109/ACCESS.2020.3031536

    Article  Google Scholar 

  17. Butt J, Mebrahtu H, Shirvani H (2015) Rapid prototyping by heat diffusion of metal foil and related mechanical testing. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-015-7882-8

    Article  Google Scholar 

  18. Sepasgozar SME, Shi A, Yang L, Shirowzhan S (2020) Additive manufacturing applications for industry 4.0: a systematic critical review. Buildings. https://doi.org/10.3390/buildings10120231

    Article  Google Scholar 

  19. Chen D, Heyer S, Ibbotson S, Salonitis K (2015) Direct digital manufacturing: definition evolution and sustainability implications. J Clean Prod 107:615

    Article  Google Scholar 

  20. Farooq MU, Waseem M (2020) A review on internet of things ( IoT ). Intern J Comput Appl 113(1):1–7

    Google Scholar 

  21. Barbosa GF, Aroca RV (2017) An IoT-based solution for control and monitoring of additive manufacturing an IoT-based solution for control and monitoring of additive manufacturing processes. J Powder Metall Min. https://doi.org/10.4172/2168-9806.1000158

    Article  Google Scholar 

  22. Yuanbin W, Pai Z, Tao P, Huayong Y, Jun ZOU (2020) Smart additive manufacturing current artificial intelligence-enabled methods and future perspectives. Sci China Technol Sci 63:1600–1611

    Article  Google Scholar 

  23. Majeed A, Zhang Y, Ren S, Lv J, Peng T, Waqar S (2021) A big data-driven framework for sustainable and smart additive manufacturing. Robot Comput Integr Manuf. https://doi.org/10.1016/j.rcim.2020.102026

    Article  Google Scholar 

  24. Abaker I et al (2015) The rise of ‘big data’ on cloud computing: review and open research issues. Inf Syst 47:98–115. https://doi.org/10.1016/j.is.2014.07.006

    Article  Google Scholar 

  25. Qi X, Chen G, Li Y, Cheng X, Li C (2019) Applying neural-network-based machine learning to additive manufacturing: current applications, challenges, and future perspectives. Engineering 5(4):721–729. https://doi.org/10.1016/j.eng.2019.04.012

    Article  Google Scholar 

  26. Batty M (2018) Digital twins. Enbiron Planning B. https://doi.org/10.1177/2399808318796416

    Article  Google Scholar 

  27. Wong KV, Hernandez A (2012) A Review of additive manufacturing. ISRN Mech Eng 2012:1–10. https://doi.org/10.5402/2012/208760

    Article  Google Scholar 

  28. Environment B, Lane BH (2015) Peel and tensile test investigation of aluminium 1050 foil parts made with a new additive manufacturing process Javaid Butt*. Habtom Mebrahtu and Hassan Shirvani. https://doi.org/10.1504/IJRAPIDM.2015.073550

    Article  Google Scholar 

  29. Butt J, Hewavidana Y, Mohaghegh V, Sadeghi-esfahlani S (2019) Hybrid manufacturing and experimental testing of glass fiber enhanced thermoplastic composites. J Manuf Mater Process. https://doi.org/10.3390/jmmp3040096

    Article  Google Scholar 

  30. 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.2018.02.012

    Article  Google Scholar 

  31. Munirathinam S (2019) Industry 4.0: industrial internet of things (IIOT ). Elsevier, Amsterdam

    Google Scholar 

  32. Tucker K et al (2018) Internet industry : a perspective review through internet of things and internet of everything. Int Manag Rev 14(2):26–35

    Google Scholar 

  33. Da Xu L, Member S, He W, Li S (2014) Internet of things in industries: a survey. IEEE Trans Industrial inf 10(4):2233–2243

    Article  Google Scholar 

  34. Kumar A (2017) Methods and materials for smart manufacturing: additive manufacturing, internet of things, flexible sensors and soft robotics. Manuf Lett. https://doi.org/10.1016/j.mfglet.2017.12.014

    Article  Google Scholar 

  35. Butt J, Onimowo DA, Gohrabian M, Sharma T, Shirvani H (2018) A desktop 3D printer with dual extruders to produce customised electronic circuitry. Front Mech Eng 13:1–7

    Article  Google Scholar 

  36. Zhong RY, Dai QY, Qu T, Hu GJ, Huang GQ (2013) RFID-enabled real-time manufacturing execution system for mass-customization production. Robot Comput-Integr Manuf 29:2012–2014

    Article  Google Scholar 

  37. Ashima R, Haleem A, Bahl S, Javaid M, Kumar S, Singh S (2021) Automation and manufacturing of smart materials in additive manufacturing technologies using internet of things towards the adoption of industry 4.0. Mater Today: Proc. https://doi.org/10.1016/j.matpr.2021.01.583

    Article  Google Scholar 

  38. Lee I, Lee K (2020) The internet of things (IoT ): applications, investments, and challenges for enterprises. Bus Horiz 58(4):431–440. https://doi.org/10.1016/j.bushor.2015.03.008

    Article  Google Scholar 

  39. Butt J (2020) Exploring the interrelationship between additive. Designs. https://doi.org/10.3390/designs4020013

    Article  Google Scholar 

  40. Ben-daya M, Hassini E, Bahroun Z (2017) Internet of things and supply chain management: a literature review. Int J Prod Res 7543:1–23. https://doi.org/10.1080/00207543.2017.1402140

    Article  Google Scholar 

  41. Tao F, Cheng Y, Da Xu L, Zhang L, Li BH (2014) CCIoT-CMfg: cloud computing and internet of things-based cloud manufacturing service system. IEEE Trans Ind Inform 10(2):1435–1442

    Article  Google Scholar 

  42. Saleh E et al (2017) 3D inkjet printing of electronics using UV conversion. Adv Mater Technol 1700134:1–8. https://doi.org/10.1002/admt.201700134

    Article  Google Scholar 

  43. Tao F, Zuo Y, Da Xu L, Lv L, Zhang L (2014) Internet of things and BOM-based life cycle assessment of energy-saving and emission-reduction of products. IEEE Trans Ind Inform 10(2):1252–1261

    Article  Google Scholar 

  44. Qian C, Zhang Y, Jiang C, Pan S, Rong Y (2019) Full length article a real-time data-driven collaborative mechanism in fixed-position assembly systems for smart manufacturing. Robot Comput Integr Manuf 61:101841. https://doi.org/10.1016/j.rcim.2019.101841

    Article  Google Scholar 

  45. Majeed A (2019) A framework for big data driven process analysis and optimization for additive manufacturing. Rapid Prototyp J 2:308–321. https://doi.org/10.1108/RPJ-04-2017-0075

    Article  Google Scholar 

  46. Conner BP et al (2014) Making sense of 3-D printing: creating a map of additive manufacturing products and services. Addit Manuf. https://doi.org/10.1016/j.addma.2014.08.005

    Article  Google Scholar 

  47. Comput JPD, Assunção MD, Calheiros RN, Bianchi S, Netto MAS, Buyya R (2015) Big data computing and clouds: trends and future directions. J Parallel Distrib Comput 79–80:3–15. https://doi.org/10.1016/j.jpdc.2014.08.003

    Article  Google Scholar 

  48. Bi K, Lin D, Liao Y, Hang C, Pedram W (2021) Additive manufacturing embraces big data. Prog Addit Manuf 6(2):181–197. https://doi.org/10.1007/s40964-021-00172-8

    Article  Google Scholar 

  49. Ren S, Zhang Y, Liu Y, Sakao T, Huisingh D, Almeida CMVB (2018) A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: a framework, challenges and future research directions. J Clean Prod. https://doi.org/10.1016/j.jclepro.2018.11.025

    Article  Google Scholar 

  50. K. S. Aggour, V. S. Kumar, P. Cuddihy, and J. W. Williams, Federated multimodal big data storage & analytics platform for additive manufacturing, pp. 1729–1738, 2019.

  51. Ko H, Witherell P, Lu Y, Rosen DW (2020) Machine learning and knowledge graph based design rule construction for additive manufacturing. Addit Manuf. https://doi.org/10.1016/j.addma.2020.101620

    Article  Google Scholar 

  52. 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.2020.101538

    Article  Google Scholar 

  53. Meng L, Mcwilliams B, Jarosinski W, Park H, Jung Y, Lee J (2020) Machine learning in additive manufacturing: a review. JOM 72(6):2363–2377. https://doi.org/10.1007/s11837-020-04155-y

    Article  Google Scholar 

  54. Everton SK, Hirsch M, Stravroulakis P, Leach RK, Clare AT (2016) Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. JMADE 95:431–445. https://doi.org/10.1016/j.matdes.2016.01.099

    Article  Google Scholar 

  55. Wu M, Phoha VV, Moon YB, Belman AK (2019) IMECE2016–67641 detecting malicious defects in 3D printing process using machine learning and image classification. ASME. https://doi.org/10.1115/IMECE2016-67641

    Article  Google Scholar 

  56. B. Kappes, S. Moorthy, D. Drake, H. Geerlings, and A. Stebner, Machine learning to optimize additive manufacturing parameters for laser powder bed fusion of inconel 718. Springer.

  57. D. Dujak and D. Sajter, Blockchain applications in supply. Springer.

  58. Francisco K, Swanson D (2018) The supply chain has no clothes : technology adoption of blockchain for supply chain transparency. Logistics. https://doi.org/10.3390/logistics2010002

    Article  Google Scholar 

  59. Buczak AL, Guven E (2016) A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor 18(2):1153–1176

    Article  Google Scholar 

  60. Barenji AV, Li Z, Wang WM, Huang GQ, David A (2019) Blockchain-based ubiquitous manufacturing: a secure and reliable cyber-physical system. Int J Prod Res. https://doi.org/10.1080/00207543.2019.1680899

    Article  Google Scholar 

  61. Zhang Y, Xu X, Liu A, Lu Q, Member S, Xu L (2019) Blockchain-based trust mechanism for IoT-based smart manufacturing system. IEEE Trans Comput Soc Syst 6:1–9. https://doi.org/10.1109/TCSS.2019.2918467

    Article  Google Scholar 

  62. Mandolla C, Messeni A, Percoco G, Urbinati A (2019) Computers in industry building a digital twin for additive manufacturing through the exploitation of blockchain: a case analysis of the aircraft industry. Comput Ind 109:134–152. https://doi.org/10.1016/j.compind.2019.04.011

    Article  Google Scholar 

  63. Kurpjuweit S, Schmidt CG, Kl M, Wagner SM (2019) Blockchain in additive manufacturing and its impact on supply chains. J Bus Logist 42:1–25. https://doi.org/10.1111/jbl.12231

    Article  Google Scholar 

  64. Casino F, Dasaklis TK, Patsakis C (2019) A systematic literature review of blockchain-based applications: current status, classification and open issues. Telemat Inform 36:55–81. https://doi.org/10.1016/j.tele.2018.11.006

    Article  Google Scholar 

  65. Agi MAN, Kumar A (2022) Blockchain technology in the supply chain: an integrated theoretical perspective of organizational adoption. Int J Prod Econ. https://doi.org/10.1016/j.ijpe.2022.108458

    Article  Google Scholar 

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

    Article  Google Scholar 

  67. Mark A et al (2018) Security of additive manufacturing: attack taxonomy and survey. Addit Manuf. https://doi.org/10.1016/j.addma.2018.03.015

    Article  Google Scholar 

  68. Wang Y, Han JH, Beynon-davies P (2019) Understanding blockchain technology for future supply chains: a systematic literature review and research agenda. Supply Chain Manag: An Int J 1:62–84. https://doi.org/10.1108/SCM-03-2018-0148

    Article  Google Scholar 

  69. Christidis K, Member GS (2016) Blockchains and smart contracts for the internet of things. IEEE Access 4:2292–2303. https://doi.org/10.1109/ACCESS.2016.2566339

    Article  Google Scholar 

  70. Lu H, Weng C (2018) Smart manufacturing technology, market maturity analysis and technology roadmap in the computer and electronic product manufacturing industry. Technol Forecast Soc Chang. https://doi.org/10.1016/j.techfore.2018.03.005

    Article  Google Scholar 

  71. Pei E, Sanfilippo M, Stief P, Dantan J, Etienne A, Siadat A (2018) ScienceDirect sciencedirect sciencedirect towards unified additive manufacturing towards an an unified additive manufacturing model for digital chain management purpose model for digital chain management purpose a new methodology to analyze the existing products an assembly oriented product family identification. Procedia CIRP 70:428–433. https://doi.org/10.1016/j.procir.2018.03.146

    Article  Google Scholar 

  72. Kurfess T, Cass WJ (2016) Rethinking additive manufacturing and intellectual property protection rethinking additive manufacturing and intellectual property protection. Res Technol Manag. https://doi.org/10.5437/08956308X5705256

    Article  Google Scholar 

  73. Khajavi S, Partanen J, Tuomi J (2021) Risk reduction in new product launch: a hybrid approach combining direct digital manufacturing and tool-based manufacturing. Comput Ind. https://doi.org/10.1016/j.compind.2015.08.008

    Article  Google Scholar 

  74. Tuegel EJ, Ingraffea AR, Eason TG, Spottswood SM (2011) Reengineering aircraft structural life prediction using a digital twin. Int J Aerosp Eng. https://doi.org/10.1155/2011/154798

    Article  Google Scholar 

  75. Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F (2017) Digital twin-driven product design, manufacturing and service with big data. Comput Methods Appl Sci. https://doi.org/10.1007/s00170-017-0233-1

    Article  Google Scholar 

  76. Yli-huumo J, Ko D, Choi S, Park S, Smolander K (2016) Where Is current research on blockchain technology ? A systematic review. PLoS ONE 11:1–27. https://doi.org/10.1371/journal.pone.0163477

    Article  Google Scholar 

  77. Attaran M, Gunasekaran A (2019) Blockchain-enabled technology: the emerging technology set to reshape and decentralise many industries. Int J Appl Decis Sci. https://doi.org/10.1504/IJADS.2019.102642

    Article  Google Scholar 

  78. Debroy T, Zhang W, Turner J, Babu SS (2017) Building digital twins of 3D printing machines. Scr Mater 135:119–124. https://doi.org/10.1016/j.scriptamat.2016.12.005

    Article  Google Scholar 

  79. Loh L, Song J, Guo F, Bi G (2018) Analytical solution of temperature distribution in a nonuniform medium due to a moving laser beam and a double beam scanning strategy in the selective laser melting process. J Heat Transf 140:1–6. https://doi.org/10.1115/1.4040256

    Article  Google Scholar 

  80. Wu F, Sun Z, Chen W, Liang Z (2021) The effects of overhang forming direction on thermal behaviors during additive manufacturing TI-6Al-4V alloy. Materials. https://doi.org/10.3390/ma14133749

    Article  Google Scholar 

  81. Ren K, Chew Y, Zhang YF, Fuh JYH, Bi GJ (2020) Thermal field prediction for laser scanning paths in laser aided additive manufacturing by physics-based machine learning. Comput Methods appl Mech Eng. https://doi.org/10.1016/j.cma.2019.112734

    Article  Google Scholar 

  82. Augustin R et al (2020) Sciencedirect sciencedirect sciencedirect existing products for schnellhardt an c assembly oriented family the development of design a digital twin for machining application in aerospace industry processes for the application in aerospace industry a new methodology functional and physical. Procedia CIRP 93:1399–1404. https://doi.org/10.1016/j.procir.2020.04.017

    Article  Google Scholar 

  83. Mukherjee T, Debroy T (2019) Short communication a digital twin for rapid qualification of 3D printed metallic components. Appl Mater Today 14:59–65. https://doi.org/10.1016/j.apmt.2018.11.003

    Article  Google Scholar 

  84. Gaikwad A, Yavari R, Montazeri M, Cole K, Rao P (2019) Toward the digital twin of additive manufacturing: integrating thermal simulations, sensing, and analytics to detect process faults. Des Manuf. https://doi.org/10.1080/24725854.2019.1701753

    Article  Google Scholar 

  85. Xu X (2012) Robotics and computer-Integrated manufacturing from cloud computing to cloud manufacturing Ubiquitous product Life cycle support. Robot Comput Integr Manuf 28(1):75–86. https://doi.org/10.1016/j.rcim.2011.07.002

    Article  Google Scholar 

  86. Bouzary H, Chen FF (2018) Service optimal selection and composition in cloud manufacturing: a comprehensive survey. Intern J Adv Manuf Technol. https://doi.org/10.1007/s00170-018-1910-4

    Article  Google Scholar 

  87. Wu M, Song Z, Moon YB (2017) Detecting cyber-physical attacks in cybermanufacturing systems with machine learning methods. J Intell Manuf. https://doi.org/10.1007/s10845-017-1315-5

    Article  Google Scholar 

  88. Singh S, Mahanty B, Tiwari MK (2018) Framework and modelling of inclusive manufacturing system. Int J Comput Integr Manuf. https://doi.org/10.1080/0951192X.2018.1550678

    Article  Google Scholar 

  89. Singh R et al (2021) Cloud manufacturing, internet of things-assisted manufacturing and 3D printing technology: reliable tools for sustainable construction. Sustainability. https://doi.org/10.3390/su13137327

    Article  Google Scholar 

  90. Zhou J, Yao X (2016) A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-016-9034-1

    Article  Google Scholar 

  91. Ren L, Zhang L (2013) Cloud manufacturing: from concept to practice. Enterp Inf Syst. https://doi.org/10.1080/17517575.2013.839055

    Article  Google Scholar 

  92. Sun K, Wei T, Ahn BY, Seo JY, Dillon SJ, Lewis JA (2013) 3D Printing of interdigitated Li-Ion microbattery architectures. Adv Mater. https://doi.org/10.1002/adma.201301036

    Article  Google Scholar 

  93. M. Applications, Monitoring Applications, 2021.

  94. Han T, Kundu S, Nag A, Yongzhao Xu (2019) 3D printed sensors for biomedical applications a review. Sensors. https://doi.org/10.3390/s19071706

    Article  Google Scholar 

  95. Ni Y, Ji R, Long K, Bu T, Chen K, Zhuang S (2017) A review of 3D-printed sensors. Appl Spectrosc Rev. https://doi.org/10.1080/05704928.2017.1287082

    Article  Google Scholar 

  96. Mahale RS, Vasanth S, Krishna H, Peramenahalli S (2022) Sensor-based additive manufacturing technologies. Biointerface Res Appl Chem 12(3):3513–3521

    Google Scholar 

  97. Muth JT, Vogt DM, Truby RL, Kolesky DB, Wood RJ, Lewis JA (2014) Embedded 3D printing of strain sensors within highly stretchable elastomers. Adv Mater 26:6307–6312. https://doi.org/10.1002/adma.201400334

    Article  Google Scholar 

  98. Mannoor MS et al (2013) 3D printed bionic ears. Nano Lett. https://doi.org/10.1021/nl4007744

    Article  Google Scholar 

  99. Suaste-gómez E, Rodríguez-roldán G, Reyes-cruz H, Terán-jiménez O (2016) Developing an ear prosthesis fabricated in polyvinylidene fluoride by a 3D printer with sensory intrinsic properties of pressure and temperature. Sensors. https://doi.org/10.3390/s16030332

    Article  Google Scholar 

  100. Mehrotra P (2016) ScienceDirect biosensors and their applications—a review. J Oral Biol Craniofacial Res 2015:1–7. https://doi.org/10.1016/j.jobcr.2015.12.002

    Article  Google Scholar 

  101. Singh H, Shimojima M, Shiratori T, Van An L, Sugamata M, Yang M (2015) “Application of 3D printing technology in increasing the diagnostic performance of enzyme-linked immunosorbent assay (ELISA) for infectious diseases. Sensors. https://doi.org/10.3390/s150716503

    Article  Google Scholar 

  102. Chan HN et al (2015) Simple, cost-effective 3D printed microfluidic components for disposable, point-of-care colorimetric analysis. ACS Sens. https://doi.org/10.1021/acssensors.5b00100

    Article  Google Scholar 

  103. Lee W et al (2014) Ultrarapid detection of pathogenic bacteria using a 3D immunomagnetic flow assay. Anal Chem. https://doi.org/10.1021/ac501436d

    Article  Google Scholar 

  104. Lee W, Kwon D, Choi W, Jung GY, Jeon S (2015) 3D-Printed microfluidic device for the detection of pathogenic bacteria using size-based separation in helical channel with trapezoid cross-section. Sci Rep. https://doi.org/10.1038/srep07717

    Article  Google Scholar 

  105. A. Manuscript, www.rsc.org/analyst, 2014, https://doi.org/10.1039/C4AN01612B.

  106. Spence DM, Chen C, Wang Y, Lockwood SY, Spence DM (2014) Themed issue: probe and chip approaches to cell analysis. Analyst. https://doi.org/10.1039/c3an02357e

    Article  Google Scholar 

Download references

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, India

    Ruby Pant, Rajesh Singh & Anita Gehlot

  2. Department of ECE, School of Engineering, SR University, Warangal, Telangana, 506371, India

    Shaik Vaseem Akram

  3. Lovely Professional University, Phagwara, Jalandhar, Punjab, 144001, India

    Lovi Raj Gupta & Amit Kumar Thakur

Authors
  1. Ruby Pant
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajesh Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anita Gehlot
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaik Vaseem Akram
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lovi Raj Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amit Kumar Thakur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amit Kumar Thakur.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose. The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pant, R., Singh, R., Gehlot, A. et al. A Systematic Review of Additive Manufacturing Solutions Using Machine Learning, Internet of Things, Big Data, Digital Twins and Blockchain Technologies: A Technological Perspective Towards Sustainability. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10116-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11831-024-10116-4

Search

Navigation