{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T13:05:32Z","timestamp":1763643932507,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of National Education of the Republic of T\u00dcRK\u0130YE"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Machines"],"abstract":"<jats:p>Three-dimensional printing, also referred to as additive manufacturing, offers a wide range of product diversity, design flexibility, and competitive product costs, making it a key technology in the Industry 4.0 era. With a growing demand for customer-oriented manufacturing strategies in the industry, 3D printing holds the potential to revolutionize traditional manufacturing systems by enabling the production of high-value-added complex products at reduced costs. This systematic literature review paper aims to analyze the ongoing research on Industry 4.0-based digital solutions in the field of monitoring and control to facilitate the adoption of 3D technologies. The study utilizes a systematic literature review method to provide detailed analyses. Specific keywords and a comprehensive database are employed for this study. Furthermore, the paper surveys the existing advancements in 3D printing machinery, focusing on process monitoring and control methods, as well as their impact on sustainability. The discussion section evaluates the literature review results for potential implementation in small and medium-sized enterprises.<\/jats:p>","DOI":"10.3390\/machines11070712","type":"journal-article","created":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T00:53:04Z","timestamp":1688518384000},"page":"712","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Systematic Literature Review: Industry 4.0 Based Monitoring and Control Systems in Additive Manufacturing"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8454-7342","authenticated-orcid":false,"given":"Idil","family":"Tartici","sequence":"first","affiliation":[{"name":"Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester M13 9PL, UK"},{"name":"Department of Mechatronics Engineering, Iskenderun Technical University, Hatay 31 200, Turkey"}]},{"given":"Zekai Murat","family":"Kilic","sequence":"additional","affiliation":[{"name":"Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester M13 9PL, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3683-726X","authenticated-orcid":false,"given":"Paulo","family":"Bartolo","sequence":"additional","affiliation":[{"name":"Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester M13 9PL, UK"},{"name":"Singapore Centre for 3D Printing, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"61","DOI":"10.2507\/29th.daaam.proceedings.009","article-title":"\u201cIndustry 4.0\u201d in Europe and East Asia","volume":"29","author":"Takakuwa","year":"2018","journal-title":"Ann. DAAAM Proc."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tartici, I., Kilic, Z.M., and Da Silva Bartolo, P.J. (2023). Impact of Additive Manufacturing in SMEs, Springer Nature.","DOI":"10.1007\/978-981-19-0561-2_10"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1108\/JMTM-02-2018-0030","article-title":"Adopting additive manufacturing in SMEs: Exploring the challenges and solutions","volume":"29","author":"Martinsuo","year":"2018","journal-title":"J. Manuf. Technol. Manag."},{"key":"ref_4","first-page":"e2021024","article-title":"Additive Manufacturing: Application Perspectives in Small and Medium Enterprises","volume":"20","author":"Beltrami","year":"2021","journal-title":"Chiang Mai Univ. J. Nat. Sci."},{"key":"ref_5","unstructured":"Commission, E. (2016). User Guide to the SME Definition, European Commission. Available online: https:\/\/op.europa.eu\/en\/publication-detail\/-\/publication\/79c0ce87-f4dc-11e6-8a35-01aa75ed71a1."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Okoli, C., and Schabram, K. (2010). A Guide to Conducting a Systematic Literature Review of Information Systems Research. Res. Methods Methodol. Account. Ej.","DOI":"10.2139\/ssrn.1954824"},{"key":"ref_7","first-page":"93","article-title":"Guidance on Conducting a Systematic Literature Review","volume":"39","author":"Xiao","year":"2019","journal-title":"JPER"},{"key":"ref_8","unstructured":"Booth, A., Sutton, A., Clowes, M., and Martyn-St James, M. (2021). Systematic Approaches to a Successful Literature Review, SAGE Publications Ltd."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.mattod.2017.07.001","article-title":"Additive manufacturing: Scientific and technological challenges, market uptake and opportunities","volume":"21","author":"Tofail","year":"2018","journal-title":"Mater. Today"},{"key":"ref_10","first-page":"102058","article-title":"In-situ sensing, process monitoring and machine control in Laser Powder Bed Fusion: A review","volume":"45","author":"McCann","year":"2021","journal-title":"Addit. Manuf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1016\/j.promfg.2017.09.043","article-title":"Intelligent nozzle design for the Laser Metal Deposition process in the Industry 4.0","volume":"13","author":"Arrizubieta","year":"2017","journal-title":"Procedia Manuf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1007\/s11740-021-01020-y","article-title":"Modeling Fused Filament Fabrication using Artificial Neural Networks","volume":"15","author":"Oehlmann","year":"2021","journal-title":"Prod. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kadam, V., Kumar, S., Bongale, A., Wazarkar, S., Kamat, P., and Patil, S. (2021). Enhancing surface fault detection using machine learning for 3d printed products. Appl. Syst. Innov., 4.","DOI":"10.3390\/asi4020034"},{"key":"ref_14","first-page":"365","article-title":"A model-based approach to refine process parameters in smart manufacturing","volume":"23","author":"Kim","year":"2015","journal-title":"CERA"},{"key":"ref_15","first-page":"102089","article-title":"Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing","volume":"46","author":"Gunasegaram","year":"2021","journal-title":"Addit. Manuf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"040401","DOI":"10.1088\/2515-7639\/ac09fb","article-title":"The case for digital twins in metal additive manufacturing","volume":"4","author":"Gunasegaram","year":"2021","journal-title":"J. Phys. Mater."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101958","DOI":"10.1016\/j.rcim.2020.101958","article-title":"A generic tri-model-based approach for product-level digital twin development in a smart manufacturing environment","volume":"64","author":"Zheng","year":"2020","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Okwudire, C., Huggi, S., Supe, S., Huang, C., and Zeng, B. (2018). Low-Level Control of 3D Printers from the Cloud: A Step toward 3D Printer Control as a Service. Inventions, 3.","DOI":"10.20944\/preprints201808.0235.v1"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106774","DOI":"10.1016\/j.cie.2020.106774","article-title":"Smart production planning and control in the Industry 4.0 context: A systematic literature review","volume":"149","author":"Bueno","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"213916","DOI":"10.1109\/ACCESS.2020.3040544","article-title":"Towards Reinforcing Healthcare 4.0: A Green Real-Time IIoT Scheduling and Nesting Architecture for COVID-19 Large-Scale 3D Printing Tasks","volume":"8","author":"Darwish","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2841","DOI":"10.1080\/00207543.2019.1671627","article-title":"Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0","volume":"58","author":"Elhoone","year":"2020","journal-title":"Int. J. Prod. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1515\/mper-2016-0030","article-title":"Smart product design and production control for effective mass customization in the industry 4.0 concept","volume":"7","author":"Zawadzki","year":"2016","journal-title":"MPER"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1108\/RPJ-11-2016-0195","article-title":"The FaaS system using additive manufacturing for personalized production","volume":"24","author":"Kang","year":"2018","journal-title":"Rapid Prototyp. J."},{"key":"ref_24","first-page":"102028","article-title":"Examining the impact of Cloud ERP on sustainable performance: A dynamic capability view","volume":"51","author":"Gupta","year":"2020","journal-title":"IJIM"},{"key":"ref_25","first-page":"418","article-title":"Incremental processing of polymer materials using the INDUSTRY 4.0 network structure","volume":"66","author":"Paszkiewicz","year":"2021","journal-title":"Polim.\/Polym."},{"key":"ref_26","first-page":"21","article-title":"Analysis of possible SDN use in the rapid prototyping process as part of the Industry 4.0","volume":"67","author":"Mazur","year":"2019","journal-title":"Bull. Pol. Acad. Sci. Tech. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"121199","DOI":"10.1016\/j.ijpharm.2021.121199","article-title":"Smartphone-enabled 3D printing of medicines","volume":"609","author":"Xu","year":"2021","journal-title":"Int. J. Pharm."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1177\/0954405418755826","article-title":"Inline measurement strategy for additive manufacturing","volume":"233","author":"Bordron","year":"2019","journal-title":"Proc. Inst. Mech. Eng. Part B"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1108\/IR-11-2019-0237","article-title":"A novel multi-brand robotic software interface for industrial additive manufacturing cells","volume":"47","author":"Zhu","year":"2020","journal-title":"Ind. Robot."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1284","DOI":"10.1007\/s11666-021-01205-y","article-title":"New Process Implementation to Enhance Cold Spray-Based Additive Manufacturing","volume":"30","author":"Wu","year":"2021","journal-title":"J. Therm. Spray Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1109\/JPROC.2021.3054628","article-title":"Optimizing Quality Inspection and Control in Powder Bed Metal Additive Manufacturing: Challenges and Research Directions","volume":"109","author":"Vinco","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.mfglet.2019.02.001","article-title":"Deep Learning for Distortion Prediction in Laser-Based Additive Manufacturing using Big Data","volume":"20","author":"Francis","year":"2019","journal-title":"Manuf. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Omairi, A., and Ismail, Z.H. (2021). Towards machine learning for error compensation in additive manufacturing. Appl. Sci., 11.","DOI":"10.3390\/app11052375"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1007\/s10044-020-00865-w","article-title":"Improved quality assessment of colour surfaces for additive manufacturing based on image entropy","volume":"23","author":"Okarma","year":"2020","journal-title":"Pattern Anal. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1080\/00401706.2021.1961870","article-title":"Statistical Modeling and Monitoring of Geometrical Deviations in Complex Shapes With Application to Additive Manufacturing","volume":"64","author":"Scimone","year":"2022","journal-title":"Technometrics"},{"key":"ref_36","first-page":"312","article-title":"Towards a digital twin of laser powder bed fusion with a focus on gas flow variables","volume":"65","author":"Klingaa","year":"2021","journal-title":"JMP"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"34","DOI":"10.35219\/awet.2019.05","article-title":"Condition-based maintenance model for the optimization of smart manufacturing processes","volume":"30","author":"Rusu","year":"2019","journal-title":"Ann. \"Dunarea De Jos\" Univ. Galati Fascicle XII Weld. Equip. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5441","DOI":"10.17973\/MMSJ.2021_12_2021115","article-title":"Use of Smart 3D Printing Technology in Conventional Engineering Production to Detect and Prevent the Occurrence of Defects","volume":"2021","author":"Sproch","year":"2021","journal-title":"MM Sci. J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.tips.2021.06.002","article-title":"Disrupting 3D printing of medicines with machine learning","volume":"42","author":"Elbadawi","year":"2021","journal-title":"Trends Pharmacol. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1016\/j.jconrel.2021.07.046","article-title":"Machine learning predicts 3D printing performance of over 900 drug delivery systems","volume":"337","author":"Elbadawi","year":"2021","journal-title":"J. Control. Release"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1038\/s41578-020-00235-2","article-title":"3D-printed multifunctional materials enabled by artificial-intelligence-assisted fabrication technologies","volume":"6","author":"Zhu","year":"2021","journal-title":"Nat. Rev. Mater."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"113958","DOI":"10.1016\/j.addr.2021.113958","article-title":"Connected healthcare: Improving patient care using digital health technologies","volume":"178","author":"Awad","year":"2021","journal-title":"Adv. Drug Deliv. Rev."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"100036","DOI":"10.1016\/j.egyai.2020.100036","article-title":"The future of sustainable chemistry and process: Convergence of artificial intelligence, data and hardware","volume":"2","author":"Tai","year":"2020","journal-title":"Energy AI"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"O\u2019Reilly, C.S., Elbadawi, M., Desai, N., Gaisford, S., Basit, A.W., and Orlu, M. (2021). Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development. Pharmaceutics, 13.","DOI":"10.3390\/pharmaceutics13122187"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2051","DOI":"10.1007\/s12065-020-00390-z","article-title":"Designing a multi-agent system architecture for managing distributed operations within cloud manufacturing","volume":"14","author":"Mastrandrea","year":"2021","journal-title":"Evol. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"104583","DOI":"10.1016\/j.resconrec.2019.104583","article-title":"Industry 4.0 and circular economy: Operational excellence for sustainable reverse supply chain performance","volume":"153","author":"Dev","year":"2020","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"102026","DOI":"10.1016\/j.rcim.2020.102026","article-title":"A big data-driven framework for sustainable and smart additive manufacturing","volume":"67","author":"Majeed","year":"2021","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1007\/s12289-021-01633-9","article-title":"Sustainable manufacturing of ultra-fine aluminium alloy 6101 wires using controlled high levels of mechanical strain and finite element modeling","volume":"14","author":"Caruso","year":"2021","journal-title":"Int. J. Mater. Form."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4045","DOI":"10.1080\/00207543.2019.1572932","article-title":"Sustainable and flexible industrial human machine interfaces to support adaptable applications in the Industry 4.0 paradigm","volume":"57","author":"Ardanza","year":"2019","journal-title":"Int. J. Prod. Res."},{"key":"ref_50","first-page":"2492","article-title":"IoE Applications to Fight Against Pandemics: 3D Printing Aiding in Sustainable Technology","volume":"16","author":"Salem","year":"2021","journal-title":"ARPN J. Eng. Appl. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, L., Du, P., and Jin, R. (2021). MOSS-multi-modal best subset modeling in smart manufacturing. Sensors, 21.","DOI":"10.3390\/s21010243"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Menolotto, M., Komaris, D.S., Tedesco, S., O\u2019flynn, B., and Walsh, M. (2020). Motion capture technology in industrial applications: A systematic review. Sensors, 20.","DOI":"10.3390\/s20195687"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Dobrilovic, D., Brtka, V., Stojanov, Z., Jotanovic, G., Perakovic, D., and Jausevac, G. (2021). A model for working environment monitoring in smart manufacturing. Appl. Sci., 11.","DOI":"10.3390\/app11062850"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Borghetti, M., Cant\u00f9, E., Sardini, E., and Serpelloni, M. (2020). Future sensors for smart objects by printing technologies in Industry 4.0 scenario. Energies, 13.","DOI":"10.3390\/en13225916"},{"key":"ref_55","first-page":"657","article-title":"Toward Open-Source Hardware and Software for the Mining Industry: A Case Study of Low-Cost Environmental Monitoring System for Non-Metallic Underground Mines","volume":"36","author":"Mardonova","year":"2019","journal-title":"Min. Metall. Explor."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Portalo, J.M., Gonz\u00e1lez, I., and Calder\u00f3n, A.J. (2021). Monitoring System for Tracking a PV Generator in an Experimental Smart Microgrid: An Open-Source Solution. Sustainability, 13.","DOI":"10.3390\/su13158182"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Izquierdo-Monge, O., Redondo-Plaza, A., Pe\u00f1a-Carro, P., Zorita-Lamadrid, \u00c1., Alonso-G\u00f3mez, V., and Hern\u00e1ndez-Callejo, L. (2023). Open Source Monitoring and Alarm System for Smart Microgrids Operation and Maintenance Management. Electronics, 12.","DOI":"10.3390\/electronics12112471"}],"container-title":["Machines"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1702\/11\/7\/712\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:06:11Z","timestamp":1760126771000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1702\/11\/7\/712"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,4]]},"references-count":57,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["machines11070712"],"URL":"https:\/\/doi.org\/10.3390\/machines11070712","relation":{},"ISSN":["2075-1702"],"issn-type":[{"type":"electronic","value":"2075-1702"}],"subject":[],"published":{"date-parts":[[2023,7,4]]}}}