Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
review-article

Scientometric analysis and systematic review of smart manufacturing technologies applied to the 3D printing polymer material extrusion system

Published: 10 November 2022 Publication History

Abstract

As the 3D printing polymer material extrusion process is moving beyond niche markets and into large-scale manufacturing, still commercial systems employed by this process work in an open-loop environment where no feedback or control solution is provided from batch-to-batch production. This issue causes significant differences in part quality and generates lower production efficiency. However, there are substantial innovations in terms of smart manufacturing (SM) technologies, where the use of integrated smart sensors, the internet-of-things (IoT), big data, and artificial intelligence (AI) tools, that applied can let the systems evolve into a closed-loop higher rentability mass production process. This study investigates the available smart manufacturing technologies applied to evaluate the current state-of-the-art. This paper used scientometric analysis to analyze the most important contributions in this area. A systematic review aims to verify the results and understand the publications related to the polymer material extrusion process in detail. The analysis concludes that the most investigated aspect is the relation between the mechanical properties of materials and the high anisotropy presented in the process. The conclusions show that different sensors have been integrated, such as digital cameras, thermal cameras, thermocouples, and accelerometers, among others. They all obtain metrics and use data models to make supported decisions. Furthermore, AI algorithms have been applied to the process, and significant progress has been made to detect quality failures or part defects. Finally, as a substantial conclusion, it has been found that there is still no system in the market that can provide integral feedback control and process adjustment in real-time. This brings a positive opportunity to improve and achieve a fully smart manufacturing system in the 3D printing polymer material extrusion process.

References

[1]
Aggour KS, Gupta VK, Ruscitto D, Ajdelsztajn L, Bian X, Brosnan KH, Kumar NC, Dheeradhada V, Hanlon T, Iyer N, and Karandikar J Artificial intelligence/machine learning in manufacturing and inspection: A GE perspective MRS Bulletin 2019 44 7 545-558
[2]
Ahn SH, Montero M, Odell D, Roundy S, and Wright PK Anisotropic material properties of fused deposition modeling ABS Rapid Prototyping Journal 2002 8 4 248-257
[3]
Ahuett-Garza H and Kurfess T A brief discussion on the trends of habilitating technologies for Industry 4.0 and Smart manufacturing Manufacturing Letters 2018 15 60-63
[4]
Alsayyed B, Siadat A, Alghamdy M, Ahmad R, and Alsayyed B Material selection methodology for additive manufacturing applications Procedia CIRP 2019 84 486-490
[5]
Anderegg DA, Bryant HA, Ruffin DC, Skrip SM, Fallon JJ, Gilmer EL, and Bortner MJ In-situ monitoring of polymer flow temperature and pressure in extrusion based additive manufacturing Additive Manufacturing 2019 26 76-83
[6]
Arinez JF, Chang Q, Gao RX, Xu C, and Zhang J Artificial intelligence in advanced manufacturing: Current status and future outlook Journal of Manufacturing Science and Engineering 2020
[7]
Ashima R, Haleem A, Bahl S, Javaid M, Mahla SK, and Singh S Automation and manufacturing of smart materials in additive manufacturing technologies using Internet of Things towards the adoption of industry 4.0 Materials Today: Proceedings 2021 45 5081-5088
[8]
Ayrilmis N, Kariz M, Kwon JH, and Kitek Kuzman M Effect of printing layer thickness on water absorption and mechanical properties of 3D-printed wood/PLA composite materials International Journal of Advanced Manufacturing Technology 2019 102 5–8 2195-2200
[9]
Baca D and Ahmad R The impact on the mechanical properties of multi-material polymers fabricated with a single mixing nozzle and multi-nozzle systems via fused deposition modeling International Journal of Advanced Manufacturing Technology 2020 106 9–10 4509-4520
[10]
Balletti C, Ballarin M, and Guerra F 3D printing: State of the art and future perspectives Journal of Cultural Heritage 2017 26 172-182
[11]
Banadaki YM On the use of machine learning for additive manufacturing technology in industry 4.0 Journal of Computer Science and Information Technology 2019
[12]
Banjanin B, Vladić G, Pál M, Baloš S, Dramićanin M, Rackov M, and Kneţević I Consistency analysis of mechanical properties of elements produced by FDM additive manufacturing technology Revista Materia 2018
[13]
Basgul C, MacDonald DW, Siskey R, and Kurtz SM Thermal localization improves the interlayer adhesion and structural integrity of 3D printed PEEK lumbar spinal cages Materialia 2020
[14]
Baumann F and Roller D Vision based error detection for 3D printing processes MATEC Web of Conferences 2016
[15]
Baumann F, Schön M, Eichhoff J, and Roller D Concept development of a sensor array for 3D printer Procedia CIRP 2016 51 24-31
[16]
Bisheh MN, Chang SI, and Lei S A layer-by-layer quality monitoring framework for 3D printing Computers and Industrial Engineering 2021
[17]
Cai Y, Wu J, and Zaheer M Analysis the research hotspots and key technical of intelligent manufacturing ACM International Conference Proceeding Series 2022
[18]
Cantrell JT, Rohde S, Damiani D, Gurnani R, DiSandro L, Anton J, Young A, Jerez A, Steinbach D, Kroese C, and Ifju P Experimental characterization of the mechanical properties of 3D-printed ABS and polycarbonate parts Rapid Prototyping Journal 2017 23 4 811-824
[19]
Chen C Searching for intellectual turning points: Progressive knowledge domain visualization Proceedings of the National Academy of Sciences of the United States of America 2004 101 SUPPL. 1 5303-5310
[20]
Chen MY, Skewes J, Woodruff MA, Dasgupta P, and Rukin NJ Multi-colour extrusion fused deposition modelling: A low-cost 3D printing method for anatomical prostate cancer models Scientific Reports 2020 10 1 3-7
[21]
Christiyan KGJ, Chandrasekhar U, and Venkateswarlu K A study on the influence of process parameters on the Mechanical Properties of 3D printed ABS composite IOP Conference Series: Materials Science and Engineering 2016
[22]
Coogan TJ and Kazmer DO Healing simulation for bond strength prediction of FDM Rapid Prototyping Journal 2017 23 3 551-561
[23]
Coogan TJ and Kazmer DO In-line rheological monitoring of fused deposition modeling Journal of Rheology 2019 63 1 141-155
[24]
Costa SF, Duarte FM, and Covas JA Thermal conditions affecting heat transfer in FDM/FFE: A contribution towards the numerical modelling of the process: This paper investigates convection, conduction and radiation phenomena in the filament deposition process Virtual and Physical Prototyping 2015 10 1 35-46
[25]
Craveiro F, Duarte JP, Bartolo H, and Bartolo PJ Additive manufacturing as an enabling technology for digital construction: A perspective on Construction 4.0 Automation in Construction 2019 103 March 251-267
[26]
Cruz S, Paulino A, Duraes J, and Mendes M Real-time quality control of heat sealed bottles using thermal images and artificial neural network Journal of Imaging 2021
[27]
Dadhwal R, Kumar R, Singh Chohan J, Singh S, and Maurya S Research trends and applications of artificial intelligence in 3D printing-A scientometric analysis 2023 Springer
[28]
Dave HK, Patadiya NH, Prajapati AR, and Rajpurohit SR Effect of infill pattern and infill density at varying part orientation on tensile properties of fused deposition modeling-printed poly-lactic acid part Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 2021 235 10 1811-1827
[29]
Delli U and Chang S Automated process monitoring in 3D printing using supervised machine learning Procedia Manufacturing 2018 26 865-870
[30]
Deng X, Zeng Z, Peng B, Yan S, and Ke W Mechanical properties optimization of poly-ether-ether-ketone via fused deposition modeling Materials 2018
[31]
Dezaki ML, Mohd Ariffin MKA, and Ariffin MKAM The effects of combined infill patterns on mechanical properties in fdm process Polymers 2020 12 12 1-20
[32]
Dilberoglu UM, Gharehpapagh B, Yaman U, and Dolen M The role of additive manufacturing in the era of Industry 4.0 Procedia Manufacturing 2017 11 June 545-554
[33]
Dinwiddie RB, Love LJ, and Rowe JC Real-time process monitoring and temperature mapping of a 3D polymer printing process Thermosense: Thermal Infrared Applications XXXV 2013 8705 87050L
[34]
Dudescu C and Racz L Effects of raster orientation, infill rate and infill pattern on the mechanical properties of 3D printed materials ACTA Universitatis Cibiniensis 2017 69 1 23-30
[35]
Durgun I and Ertan R Experimental investigation of FDM process for improvement of mechanical properties and production cost Rapid Prototyping Journal 2014 20 3 228-235
[36]
Duty, C., Failla, J., Kim, S., Lindahl, J., Post, B., Love, L., & Kunc, V. (2020). Reducing mechanical anisotropy in extrusion-based printed parts. In: Solid Freeform Fabrication 2017: Proceedings of the 28th annual international solid freeform fabrication symposium—An additive manufacturing conference, SFF 2017 (pp. 1602–1611).
[37]
Duty C, Failla J, Kim S, Smith T, Lindahl J, and Kunc V Z-Pinning approach for 3D printing mechanically isotropic materials Additive Manufacturing 2019 27 March 175-184
[38]
Elkaseer A, Schneider S, and Scholz G Experiment-based process modeling and optimization for high-quality and resource-efficient Applied Science 2020 10 2899
[39]
Fatimatuzahraa, A. W., Farahaina, B., & Yusoff, W. A. Y. Y. (2011). The effect of employing different raster orientations on the mechanical properties and microstructure of Fused Deposition Modeling parts. In ISBEIA 2011 - 2011 IEEE symposium on business, engineering and industrial applications (pp. 22–27).
[40]
Ferraris E, Zhang J, Hooreweder BV, and Van Hooreweder B Thermography based in-process monitoring of Fused Filament Fabrication of polymeric parts CIRP Annals 2019 68 1 213-216
[41]
Frazier WE Metal additive manufacturing: A review Journal of Materials Engineering and Performance 2014 23 6 1917-1928
[42]
Galeta T, Raos P, Stojšić J, and Pakši I Influence of structure on mechanical properties of 3D printed objects Procedia Engineering 2016 149 June 100-104
[43]
Gardan J Additive manufacturing technologies: State of the art and trends International Journal of Production Research 2016 54 10 3118-3132
[44]
Goh GD, Sing SL, and Yeong WY A review on machine learning in 3D printing: Applications, potential, and challenges Artificial Intelligence Review 2021
[45]
Gonabadi, H., Yadav, & A., & Bull, S. J. (n.d.). The effect of processing parameters on the mechanical characteristics of PLA produced by a 3D FFF printer.
[46]
Granovsky YV Is it possible to measure science? V. V. Nalimov’s research in scientometrics Scientometrics 2001 52 127-150
[47]
Guo N and Leu MC Additive manufacturing: Technology, applications and research needs Frontiers of Mechanical Engineering 2013
[48]
Han Y and Jia G Optimizing product manufacturability in 3D printing Frontiers of Computer Science 2017 11 2 347-357
[49]
Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for industrie 4.0 scenarios. In Proceedings of the Annual Hawaii International Conference on System Sciences (Vol. 2016-March, pp. 3928–3937). IEEE Computer Society.
[50]
Herron, C., Ivus, M., Kotak, A. (2021). Just Press "Print": Canada’s additive manufacturing ecosystem. In Information and Communications Technology Council (ICTC), Ottawa, Canada.
[51]
Holzmond O and Li X In situ real time defect detection of 3D printed parts Additive Manufacturing 2017 17 135-142
[52]
Hossain, M. S., Ramos, J., Espalin, D., Perez, M., Wicker, R., & Keck, W. M. Improving tensile mechanical properties of FDM-manufactured specimens via modifying build parameters (2013)
[53]
Hu, J. (2020). Study on STL-based slicing process for 3D printing. In Solid freeform fabrication 2017: Proceedings of the 28th annual international solid freeform fabrication symposium—An additive manufacturing conference, SFF 2017 (pp. 885–895).
[54]
Huang, T., Wang, S., & He, K. (2015). Quality control for fused deposition modeling based additive manufacturing: Current research and future trends. In Proceedings of 2015 the 1st international conference on reliability systems engineering, ICRSE 2015. Institute of Electrical and Electronics Engineers Inc.
[55]
Jain P and Kuthe AM Feasibility study of manufacturing using rapid prototyping: FDM approach Procedia Engineering 2013 63 4-11
[56]
Jan van Eck, N., & Waltman, L. (2022). VOSviewer Manual. Manual for VOSviewer version 1.6.18.
[57]
Jawad Qureshi, A. (2015). Design for scalability and strength Optimisation for components created through FDM process. https://www.researchgate.net/publication/281068948
[58]
Kang HS, Lee JY, Choi S, Kim H, Park JH, Son JY, Kim BH, and Noh SD Smart manufacturing: Past research, present findings, and future directions International Journal of Precision Engineering and Manufacturing - Green Technology 2016 3 1 111-128
[59]
Karakurt I and Lin L 3D printing technologies: Techniques, materials, and post-processing Current Opinion in Chemical Engineering 2020 28 134-143
[60]
Kariz M, Sernek M, and Kuzman MK Effect of humidity on 3D-printed specimens from wood-pla filaments Wood Research 2018 63 5 917-922
[61]
Kazemian A and Khoshnevis B Real-time extrusion quality monitoring techniques for construction 3D printing Construction and Building Materials 2021 303 January 124520
[62]
Khan MF, Alam A, Siddiqui MA, Alam MS, Rafat Y, Salik N, and Al-Saidan I Real-time defect detection in 3D printing using machine learning Materials Today: Proceedings 2020 42 521-528
[63]
Khandpur MS, Galati M, Minetola P, Marchiandi G, Fontana L, and Stiuso V Development of a low-cost monitoring system for open 3 d printing IOP Conference Series: Materials Science and Engineering 2021
[64]
Khoo ZX, Teoh JEM, Liu Y, Chua CK, Yang S, An J, Leong KF, and Yeong WY 3D printing of smart materials: A review on recent progresses in 4D printing Virtual and Physical Prototyping 2015 10 3 103-122
[65]
Kim D-S and Tran-Dang H Industrial sensors and controls in communication networks 2019 Cham Springer
[66]
Kopsacheilis, C., Charalampous, P., Kostavelis, I., & Tzovaras, D. (2020) In situ visual quality control in 3D printing. In 11th international conference on information visualization theory and applications. https://orcid.org/0000-0002-9399-4387
[67]
Korner MEH, Lambán MP, Albajez JA, Santolaria J, Corrales LDCN, and Royo J Systematic literature review: Integration of additive manufacturing and industry 4.0 Metals 2020 10 8 1-24
[68]
Kuclourya T, Monroy R, Cuan-Urquizo E, Roman-Flores A, and Ahmad R Scientometric analysis and critical review of fused deposition modeling in the plastic recycling context Cleaner Waste Systems 2022 2 April 100008
[69]
Kumar R, Rogall C, Thiede S, Herrmann C, and Sangwan KS Development of a decision support system for 3D printing processes based on cyber physical production systems Procedia CIRP 2021 98 348-353
[70]
Kusiak A Smart manufacturing International Journal of Production Research 2018 56 1–2 508-517
[71]
Lambos N, Vosniakos GC, and Papazetis G Low-cost automatic identification of nozzle clogging in material extrusion 3D printers Procedia Manufacturing 2020 51 274-279
[72]
Lee JY, An J, and Chua CK Fundamentals and applications of 3D printing for novel materials Applied Materials Today 2017 7 120-133
[73]
Lee WC, Wei CC, and Chung SC Development of a hybrid rapid prototyping system using low-cost fused deposition modeling and five-axis machining Journal of Materials Processing Technology 2014 214 11 2366-2374
[74]
Li C, Cabrera D, Sancho F, Cerrada M, Sánchez RV, and Estupinan E From fault detection to one-class severity discrimination of 3D printers with one-class support vector machine ISA Transactions 2021 110 357-367
[75]
Li C, Cabrera D, Sancho F, Sanchez RV, Cerrada M, De Oliveira JV, and De Oliveira JV One-shot fault diagnosis of three-dimensional printers through improved feature space learning IEEE Transactions on Industrial Electronics 2021 68 9 8768-8776
[76]
Li L and Liu J Multi-view feature modeling for design-for-additive manufacturing Multi-view feature modeling for design-for-additive manufacturing Advanced Engineering Informatics 2018
[77]
Li, S., Freije, E., & Yearling, P. (2017). Monitoring 3D printer performance using internet of things (IoT) application. In ASEE annual conference and exposition, conference proceedings (Vol. 2017-June). American Society for Engineering Education.
[78]
Lidong W and Guanghui W Big data in cyber-physical systems, digital manufacturing and industry 4.0 International Journal of Engineering and Manufacturing 2016 6 4 1-8
[79]
Liu J, Chen Q, Ahmad R, and Zheng Y Level set-based heterogeneous object modeling and optimization Computer-Aided Design 2019
[80]
Liu Z, Wang Y, Wu B, Cui C, Guo Y, and Yan C A critical review of fused deposition modeling 3D printing technology in manufacturing polylactic acid parts International Journal of Advanced Manufacturing Technology 2019 102 9–12 2877-2889
[81]
Lu Y and Ju F Smart manufacturing systems based on cyber-physical manufacturing services (CPMS) IFAC-PapersOnLine 2017 50 1 15883-15889
[82]
Luo X, Zhang L, Ren L, and Lali Y A dynamic and static data based matching method for cloud 3D printing Robotics and Computer-Integrated Manufacturing 2020 61 September 2019 101858
[83]
Lyons, K. (2016). DETC2016-59721 Enabling smart manufacturing technologies for decision- making. In Proc ASME des eng tech conf (pp. 1–10)
[84]
Ma G, Li Z, Wang L, Wang F, and Sanjayan J Mechanical anisotropy of aligned fiber reinforced composite for extrusion-based 3D printing Construction and Building Materials 2019 202 770-783
[85]
Malekipour E, Attoye S, and El-Mounayri H Investigation of layer based thermal behavior in fused deposition modeling process by infrared thermography Procedia Manufacturing 2018 26 1014-1022
[86]
Maschio F, Pandya M, and Olszewski R Experimental validation of plastic mandible models produced by a “low-cost” 3-dimensional fused deposition modeling printer Medical Science Monitor 2016 22 943-957
[87]
Mehrpouya M, Dehghanghadikolaei A, Fotovvati B, Vosooghnia A, Emamian SS, and Gisario A The potential of additive manufacturing in the smart factory industrial 4.0: A review Applied Science 2019
[88]
Mittal S, Khan MA, Romero D, and Wuest T A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs) Journal of Manufacturing Systems 2018 49 November 194-214
[89]
Molero E, Fernández JJ, Rodríguez-Alabanda O, Guerrero-Vaca G, and Romero PE Use of data mining techniques for the prediction of surface roughness of printed parts in polylactic acid (PLA) by fused deposition modeling (FDM): A practical application in frame glasses manufacturing Polymers 2020
[90]
Monostori, L. (2014). Cyber-physical production systems: Roots, expectations and R&D challenges. In Procedia CIRP (Vol. 17, pp. 9–13). Elsevier B.V.
[91]
Morales NG, Fleck TJ, and Rhoads JF The effect of interlayer cooling on the mechanical properties of components printed via fused deposition Additive Manufacturing 2018 24 243-248
[92]
Najjartabar Bisheh M, Chang SI, and Lei S A layer-by-layer quality monitoring framework for 3D printing Computers and Industrial Engineering 2021
[93]
Ngo TD, Kashani A, Imbalzano G, Nguyen KTQ, and Hui D Additive manufacturing (3D printing): A review of materials, methods, applications and challenges Composites Part B: Engineering 2018 143 February 172-196
[94]
Nguyen NA, Bowland CC, and Naskar AK A general method to improve 3D-printability and inter-layer adhesion in lignin-based composites Applied Materials Today 2018 12 138-152
[95]
Nguyen NA, Bowland CC, and Naskar AK Mechanical, thermal, morphological, and rheological characteristics of high performance 3D-printing lignin-based composites for additive manufacturing applications Data in Brief 2018 19 936-950
[96]
Nuchitprasitchai S, Roggemann M, and Pearce JM Factors effecting real-time optical monitoring of fused filament 3D printing Progress in Additive Manufacturing 2017 2 3 133-149
[97]
Onu P and Mbohwa C Industry 4.0 opportunities in manufacturing SMEs: Sustainability outlook Materials Today: Proceedings 2021 44 1925-1930
[98]
Osswald T and Menges G Material science of polymers for engineers Hanser 2012
[99]
Paraskevoudis K, Karayannis P, and Koumoulos EP Real-time 3d printing remote defect detection (Stringing) with computer vision and artificial intelligence Processes 2020 8 11 1-15
[100]
Peng F, Vogt BD, and Cakmak M Complex flow and temperature history during melt extrusion in material extrusion additive manufacturing Additive Manufacturing 2018 22 197-206
[101]
Petsiuk, A., & Pearce, J. M. (2021). Towards smart monitored AM: Open source in-situ layer-wise 3D printing image anomaly detection using histograms of oriented gradients and a physics-based rendering engine.
[102]
Pham T and Dimov SSA Rapid manufacturing: The technologies and applications of rapid prototyping and rapid tooling 2001 New York Springer
[103]
Piedra-Cascón W, Krishnamurthy VR, Att W, and Revilla-León M 3D printing parameters, supporting structures, slicing, and post-processing procedures of vat-polymerization additive manufacturing technologies: A narrative review Journal of Dentistry 2021
[104]
Rayna T and Striukova L From rapid prototyping to home fabrication: How 3D printing is changing business model innovation Technological Forecasting and Social Change 2016 102 214-224
[105]
Sathies T, Senthil P, and Anoop MS A review on advancements in applications of fused deposition modelling process Rapid Prototyping Journal 2020 26 4 669-687
[106]
Schöppner, V., Bagsik, A., & Paderborn, K. (2011). Mechanical properties of fused deposition modeling parts manufactured with ULTEM 9085. In Proceedings of 69th annual technical conference of the society of plastics engineers (Vol. 2, pp. 1–5).
[107]
Sepasgozar SME, Shi A, Yang L, Shirowzhan S, and Edwards DJ Additive manufacturing applications for industry 4.0: A systematic critical review Buildings 2020 10 12 1-35
[108]
Shaffer S, Yang K, Vargas J, Di Prima MA, and Voit W On reducing anisotropy in 3D printed polymers via ionizing radiation Polymer 2014 55 23 5969-5979
[109]
Shahmirzadi MR, Gholampour A, Kashani A, and Ngo TD Shrinkage behavior of cementitious 3D printing materials: Effect of temperature and relative humidity Cement and Concrete Composites 2021 124 104238
[110]
Shahrubudin N, Lee TC, and Ramlan R An overview on 3D printing technology: Technological, materials, and applications Procedia Manufacturing 2019 35 1286-1296
[111]
Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, Shekelle P, and Stewart LA Preferred reporting items for systematic review and meta-analysis protocols (prisma-p) 2015: Elaboration and explanation BMJ 2015
[112]
Shim JS, Kim JE, Jeong SH, Choi YJ, and Ryu JJ Printing accuracy, mechanical properties, surface characteristics, and microbial adhesion of 3D-printed resins with various printing orientations Journal of Prosthetic Dentistry 2020 124 4 468-475
[113]
Silverman, A. E. (2019). Artificial intelligence and legal reasoning In Mind, machine, and metaphor (pp. 3–33). Routledge.
[114]
Singh R and Garg HK Fused deposition modelling—A state of art review and future applications 2016 Elsevier Ltd.
[115]
Srinivasan R, Giannikas V, McFarlane D, and Thorne A Customising with 3D printing: The role of intelligent control Computers in Industry 2018 103 38-46
[116]
Straub J Initial work on the characterization of additive manufacturing (3D printing) using software image analysis Machines 2015 3 2 55-71
[117]
Su HN and Lee PC Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in Technology Foresight Scientometrics 2010 85 1 65-79
[118]
Sunny, B. C., Benedict, S., Rajan, M. P., & Srinivas, M. (2019). Impact of printing parameters on energy consumption of 3D printers using IoT cloud architecture. In 2019 IEEE 16th India council international conference, INDICON 2019—symposium proceedings. Institute of Electrical and Electronics Engineers Inc.
[119]
Syrlybayev D, Zharylkassyn B, Seisekulova A, Akhmetov M, Perveen A, and Talamona D Optimisation of strength properties of FDM printed parts—A critical review Polymers 2021
[120]
Tetsuka H and Shin SR Materials and technical innovations in 3D printing in biomedical applications Journal of Materials Chemistry B 2020 8 15 2930-2950
[121]
Tlegenov Y, Hong GS, and Lu WF Nozzle condition monitoring in 3D printing Robotics and Computer-Integrated Manufacturing 2018 54 45-55
[122]
Torrado AR and Roberson DA Failure analysis and anisotropy evaluation of 3D-printed tensile test specimens of different geometries and print raster patterns Journal of Failure Analysis and Prevention 2016 16 1 154-164
[123]
Torrado AR, Shemelya CM, English JD, Lin Y, Wicker RB, and Roberson DA Characterizing the effect of additives to ABS on the mechanical property anisotropy of specimens fabricated by material extrusion 3D printing Additive Manufacturing 2015 6 16-29
[124]
Tymrak BM, Kreiger M, and Pearce JM Mechanical properties of components fabricated with open-source 3-D printers under realistic environmental conditions Materials and Design 2014 58 242-246
[125]
Upadhyay, K., Dwivedi, R., & Singh, A. K. (2016). Determination and comparison of the anisotropic strengths of fused deposition modeling P400 ABS. In Advances in 3D printing and additive manufacturing technologies (pp. 9–28). Springer.
[126]
Vanaei HR, Raissi K, Deligant M, Shirinbayan M, Fitoussi J, Khelladi S, and Tcharkhtchi A Toward the understanding of temperature effect on bonding strength, dimensions and geometry of 3D-printed parts Journal of Materials Science 2020 55 29 14677-14689
[127]
Walsh GS, Przychodzen J, and Przychodzen W Supporting the SME commercialization process: The case of 3D printing platforms Small Enterprise Research 2017 24 3 257-273
[128]
Wang B, Tao F, Fang X, Liu C, Liu Y, and Freiheit T Smart manufacturing and intelligent manufacturing: A comparative review Engineering 2021 7 6 738-757
[129]
Wickramasinghe S, Do T, and Tran P FDM-Based 3D printing of polymer and associated composite: A review on mechanical properties, defects and treatments Polymers 2020 12 7 1-42
[130]
Wu HC and Chen TCT Quality control issues in 3D-printing manufacturing: A review Rapid Prototyping Journal 2018 24 3 607-614
[131]
Xu LD, He W, and Li S Internet of things in industries: A survey IEEE Transactions on Industrial Informatics 2014
[132]
Yang H, Kumara S, Bukkapatnam STS, and Tsung F The internet of things for smart manufacturing: A review IISE Transactions 2019 51 11 1190-1216
[133]
Yao, X., Zhou, J., Zhang, J., & Boer, C. R. (2017). From intelligent manufacturing to smart manufacturing for Industry 4.0 driven by next generation artificial intelligence and further on. In Proceedings—2017 5th international conference on enterprise systems: industrial digitalization by enterprise systems, ES 2017 (pp. 311–318).
[134]
Yin J, Lu C, Fu J, Huang Y, and Zheng Y Interfacial bonding during multi-material fused deposition modeling (FDM) process due to inter-molecular diffusion Materials and Design 2018 150 104-112
[135]
Yu H, Hong H, Cao S, and Ahmad R Topology optimization for multipatch fused deposition modeling 3D printing Applied Sciences (switzerland) 2020
[136]
Zhang S, He K, Cabrera D, Li C, Bai Y, and Long J Transmission condition monitoring of 3d printers based on the echo state network Applied Sciences (switzerland) 2019
[137]
Zheng P, Wang H, Sang Z, Zhong RY, Liu Y, Liu C, Mubarok K, Yu S, and Xu X Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives Frontiers of Mechanical Engineering 2018 13 2 137-150
[138]
Zheng Y, Zhang W, Lopez DMB, and Ahmad R Scientometric analysis and systematic review of multi-material additive manufacturing of polymers Polymers 2021
[139]
Zhong RY, Xu X, Klotz E, and Newman ST Intelligent manufacturing in the context of Industry 4.0: A review Engineering 2017 3 5 616-630
[140]
Zhou X, Hsieh SJ, and Sun Y Experimental and numerical investigation of the thermal behaviour of polylactic acid during the fused deposition process Virtual and Physical Prototyping 2017 12 3 221-233

Index Terms

  1. Scientometric analysis and systematic review of smart manufacturing technologies applied to the 3D printing polymer material extrusion system
      Index terms have been assigned to the content through auto-classification.

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image Journal of Intelligent Manufacturing
      Journal of Intelligent Manufacturing  Volume 35, Issue 1
      Jan 2024
      448 pages

      Publisher

      Springer-Verlag

      Berlin, Heidelberg

      Publication History

      Published: 10 November 2022
      Accepted: 25 October 2022
      Received: 14 June 2022

      Author Tags

      1. FDM
      2. Industry 4.0
      3. Artificial intelligence
      4. Mechanical properties
      5. Thermal properties
      6. Smart manufacturing

      Qualifiers

      • Review-article

      Funding Sources

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 0
        Total Downloads
      • Downloads (Last 12 months)0
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 13 Sep 2024

      Other Metrics

      Citations

      View Options

      View options

      Get Access

      Login options

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media