Machine learning-enabled optimization of extrusion-based 3D printing

Rahmani Dabbagh, Sajjad; Ozcan, Oguzhan; Tasoglu, Savas

doi:10.1016/j.ymeth.2022.08.002

Cited by 94Additive Manufacturing and 3D Printing TechnologiesMachine Learning in Materials ScienceInnovations in Concrete and Construction Materials

Machine learning-enabled optimization of extrusion-based 3D printing

Rahmani Dabbagh, Sajjad, Ozcan, Oguzhan, Tasoglu, Savas·2022·Methods

DOI 10.1016/j.ymeth.2022.08.002

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Abstract

Machine learning (ML) and three-dimensional (3D) printing are among the fastest-growing branches of science. While ML can enable computers to independently learn from available data to make decisions with minimal human intervention, 3D printing has opened up an avenue for modern, multi-material, manufacture of complex 3D structures with a rapid turn-around ability for users with limited manufacturing experience. However, the determination of optimum printing parameters is still a challenge, increasing pre-printing process time and material wastage. Here, we present the first integration of ML and 3D printing through an easy-to-use graphical user interface (GUI) for printing parameter optimization. Unlike the widely held orthogonal design used in most of the 3D printing research, we, for the first time, used nine different computer-aided design (CAD) images and in order to enable ML algorithms to distinguish the difference between designs, we devised a self-designed method to calculate the "complexity index" of CAD designs. In addition, for the first time, the similarity of the print outcomes and CAD images are measured using four different self-designed labeling methods (both manually and automatically) to figure out the best labeling method for ML purposes. Subsequently, we trained eight ML algorithms on 224 datapoints to identify the best ML model for 3D printing applications. The "gradient boosting regression" model yields the best prediction performance with an R-2 score of 0.954. The ML-embedded GUI developed in this study enables users (either skilled or unskilled in 3D printing and/or ML) to simply upload a design (desired to print) to the GUI along with desired printing temperature and pressure to obtain the approximate similarity in the case of actual 3D printing of the uploaded design. This ultimately can prevent error-and-trial steps prior to printing which in return can speed up overall design-to-end-product time with less material waste and more cost-efficiency.

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During printing, AI algorithms can dynamically adjust parameters such as extrusion speed, nozzle temperature, and layer height to maintain consistent print quality and reduce material waste.
“During the printing process, AI algorithms can dynamically adjust parameters such as extrusion speed [44,44], nozzle temperature, and layer height to maintain consistent print quality and reduce material waste [37].”
From Towards the development of foods 3D printer: Trends and technologies for foods printing (2024) · cites this paper as [37]

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Sorted by citations

Advances in materials and technologies for digital light processing 3D printing
Nam, J., & Kim, M. (2024) · Nano Convergence · doi:10.1186/s40580-024-00452-3 · Cited by 118 · see citing works
Review of machine learning applications in additive manufacturing
Inayathullah, S., & Buddala, R. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103676 · Cited by 107 · see citing works
AI-Based Metamaterial Design
Tezsezen, E., Yigci, D., Ahmadpour, A., & Taşoğlu, S. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c04486 · Cited by 95 · see citing works
Optimization of 4D/3D printing via machine learning: A systematic review
Alli, Y. A., Anuar, H., Manshor, M. R., Okafor, C. E., Kamarulzaman, A. F., Akçakale, N., Nazeri, F. N. M., Bodaghi, M., Suhr, J., & Nasir, N. A. M. (2024) · Hybrid Advances · doi:10.1016/j.hybadv.2024.100242 · Cited by 98 · see citing works
A Survey and Perspective on Industrial Cyber-Physical Systems (ICPS): From ICPS to AI-Augmented ICPS
Chae, J., Lee, S., Jang, J., Hong, S., & Park, K. (2023) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2023.3323600 · Cited by 71 · see citing works
Machine learning-driven 3D printing: A review
Zhang, X., Chu, D., Zhao, X., et al. (2024) · Applied Materials Today · doi:10.1016/j.apmt.2024.102306 · Cited by 69 · see citing works
A review of deep learning in metal additive manufacturing: Impact on process, structure, and properties
Du, Y., Mukherjee, T., Li, R., Hou, Z., Dutta, S., Arnold, C. B., Elwany, A. H., Kung, S., Tang, J., & DebRoy, T. (2025) · Progress in Materials Science · doi:10.1016/j.pmatsci.2025.101587 · Cited by 73 · see citing works
Point‐of‐Care Diagnostic Platforms for Loop‐Mediated Isothermal Amplification
Atçeken, N., Alseed, M. M., Dabbagh, S. R., Yetisen, A. K., & Taşoğlu, S. (2022) · Advanced Engineering Materials · doi:10.1002/adem.202201174 · Cited by 63 · see citing works
AI-based additive manufacturing for future food: Potential applications, challenges and possible solutions
Yu, Q. J., Zhang, M., MUJUMDAR, A. S., & Li, J. (2024) · Innovative Food Science & Emerging Technologies · doi:10.1016/j.ifset.2024.103599 · Cited by 63 · see citing works
Three-Dimensional Printing Technologies for Drug Delivery Applications: Processes, Materials, and Effects
Mancilla-de-la-Cruz, J., Rodríguez-Salvador, M., An, J., et al. (2022) · International Journal of Bioprinting · doi:10.18063/ijb.v8i4.622 · Cited by 58 · see citing works
Towards the development of foods 3D printer: Trends and technologies for foods printing
Neamah, H. A., & Tandio, J. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e33882 · Cited by 43 · see citing works
“During the printing process, AI algorithms can dynamically adjust parameters such as extrusion speed [44,44], nozzle temperature, and layer height to maintain consistent print quality and reduce material waste [37].”
BackgroundMethodInfluential
Shape Fidelity Evaluation of Alginate-Based Hydrogels through Extrusion-Based Bioprinting
Temirel, M., Dabbagh, S. R., & Taşoğlu, S. (2022) · Journal of Functional Biomaterials · doi:10.3390/jfb13040225 · Cited by 43 · see citing works
Advancing 3D Printing through Integration of Machine Learning with Algae‐Based Biopolymers
Sofian, A. D. A. B. A., Lim, H. R., Chew, K. W., & Show, P.-L. (2024) · ChemBioEng Reviews · doi:10.1002/cben.202300054 · Cited by 43 · see citing works
Bioprinting in Microgravity
Sarabi, M. R., Yetisen, A. K., & Taşoğlu, S. (2023) · ACS Biomaterials Science & Engineering · doi:10.1021/acsbiomaterials.3c00195 · Cited by 39 · see citing works
Three-Dimensional Printing Quality Inspection Based on Transfer Learning with Convolutional Neural Networks
Yang, C., Huang, W.-K., & Lin, K. (2023) · Sensors · doi:10.3390/s23010491 · Cited by 36 · see citing works
Machine Learning in Predicting and Optimizing Polymer Printability for 3D Bioprinting
Yu, J., Yao, D., Wang, L., & Xu, M. (2025) · Polymers · doi:10.3390/polym17131873 · Cited by 32 · see citing works
“Constructing scaffolds with complex geometries and high-precision details such as porous networks, biomimetic hierarchical structures, or finely branched vasculature requires elevated levels of printing accuracy and material stability [74,75].”
The Role of 3D Printing in Advancing Automated Manufacturing Systems: Opportunities and Challenges
Kantaros, A., Drosos, C., Papoutsidakis, M. G., Pallis, E., & Ganetsos, T. K. (2025) · Automation · doi:10.3390/automation6020021 · Cited by 27 · see citing works
“Additionally, real-world case studies were analyzed, such as those from Dabbagh et al. [56] and Melton Machine & Control Co. [57], to identify practical applications and challenges.”
Bayesian machine learning optimization of microneedle design for biological fluid sampling
Tarar, C., Aydın, E., Yetisen, A. K., et al. (2023) · Sensors & Diagnostics · doi:10.1039/d3sd00103b · Cited by 26 · see citing works
Machine Learning-Enabled Optimization of Interstitial Fluid Collection via a Sweeping Microneedle Design
Tarar, C., Aydın, E., Yetisen, A. K., et al. (2023) · ACS Omega · doi:10.1021/acsomega.3c01744 · Cited by 26 · see citing works
Machine Vision-Based Surface Defect Detection Study for Ceramic 3D Printing
Zhou, J., Li, H., Lin, L., et al. (2024) · Machines · doi:10.3390/machines12030166 · Cited by 25 · see citing works
“Compared to the above, 3D printing has a simple molding, short cycle, is easy to adjust, etc., but because of the extrusion molding process, the material price is more expensive and the printing process is prone to defects and cannot produce printed products that [36].”
Machine learning-assisted extrusion-based 3D bioprinting for tissue regeneration applications
Krishna, D. V., & Sankar, M. R. (2023) · Annals of 3D Printed Medicine · doi:10.1016/j.stlm.2023.100132 · Cited by 25 · see citing works
Machine learning‐enabled optimization of melt electro‐writing three‐dimensional printing
Abdullah, A. C., Özarslan, O., Farshi, S. S., et al. (2024) · Aggregate · doi:10.1002/agt2.495 · Cited by 22 · see citing works
A review of current research and prospects of fused deposition modelling: application, materials, performance, process variables, parameter optimization, and numerical study
Siyoum, Y. B., Kindie, F. G., & Gebeyehu, M. A. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-15615-7 · Cited by 20 · see citing works
Additive manufacturing of polymers and composites for applications in aerospace and aeronautics
Aliberti, F., Longo, R., Raimondo, M., Pantani, R., Vertuccio, L., & Guadagno, L. (2025) · Materials Horizons · doi:10.1039/d5mh01403d · Cited by 20 · see citing works
3D bioprinting of natural materials and their AI-Enhanced printability: A review
Grira, S., Mozumder, M. S., Mourad, A. I., Ramadan, M., Khalifeh, H. A., & AlKhedher, M. A. (2025) · Bioprinting · doi:10.1016/j.bprint.2025.e00385 · Cited by 19 · see citing works
Designing Emulsion Gels for 3D Food Printing: Structure, Stability, and Functional Applications
de Farias, B. S., da Cunha, L. B., Christ-Ribeiro, A., Jaeschke, D. P., Gonçalves, J. O., Fernandes, S. S., Cadaval, T. R. S., & Pinto, L. A. D. A. (2025) · Surfaces · doi:10.3390/surfaces8030064 · Cited by 21 · see citing works
Artificial intelligence in additive Manufacturing: advances in smart materials, lattice optimization, and process intelligence
Zaman, S., Mahmud, M. S., Mollick, A. A., Lhaden, T., Dantzler, J. Z. R., Arroyo, S., Goona, N. K., Mesbah, M., Ahsan, M. A., & Lin, Y. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-18072-y · Cited by 17 · see citing works
A Path towards SDGs: Investigation of the Challenges in Adopting 3D Concrete Printing in India
Shivendra, B. T., Shahaji, Chandra, S. S., et al. (2024) · Infrastructures · doi:10.3390/infrastructures9090166 · Cited by 16 · see citing works
Material Extrusion Filament Width and Height Prediction via Design of Experiment and Machine Learning
Shi, X., Sun, Y., Tian, H., Abhilash, P. M., Luo, X., & Liu, H. (2023) · Micromachines · doi:10.3390/mi14112091 · Cited by 17 · see citing works
“Machine learning has been successfully applied in various ﬁelds such as healthcare, energy, materials, and manufacturing [33–36].”
Method
Research on 3D printing concrete mechanical properties prediction model based on machine learning
Zhang, Y., Cui, S., Yang, B., et al. (2025) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2025.e04254 · Cited by 16 · see citing works
3D printing of biologics—what has been accomplished to date?
Lu, A., Williams, R. O., & Maniruzzaman, M. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103823 · Cited by 14 · see citing works
Recent advances in artificial-intelligence enhanced additive manufacturing of heat exchangers for thermal management: a review
Li, Y., Su, Q., Sheng, G., et al. (2025) · Materials & Design · doi:10.1016/j.matdes.2025.114339 · Cited by 13 · see citing works
Machine learning based approach for surface roughness prediction in precision dental prototyping
Sharma, A., Saini, R. S., Kaushik, A., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-17487-z · Cited by 12 · see citing works
“XGBoost is an enhanced version of the Gradient Boosting algorithm also used for the construction of the models of the regression type of supervised machine learning 89 .”
Overview of 3D Printed Exoskeleton Materials and Opportunities for Their AI-Based Optimization
Rojek, I., Dorożyński, J., Mikołajewski, D., et al. (2023) · Applied Sciences · doi:10.3390/app13148384 · Cited by 12 · see citing works
Unraveling the influence of solvent composition on Drop-on-Demand binder jet 3D printed tablets containing calcium sulfate hemihydrate
Lu, A., Duggal, I., Daihom, B. A., et al. (2023) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2023.123652 · Cited by 12 · see citing works
Accurately Predicting Multiple Performance of 3D Printing Photopolymers Using Ensemble Learning
Gao, W., Wang, H., Xu, Y., Yang, Y., Gu, Q., Duan, X., Wang, B., & Zhou, X. (2024) · ACS Applied Polymer Materials · doi:10.1021/acsapm.3c03102 · Cited by 11 · see citing works
Machine learning-driven optimization of energy absorption in PLA composites reinforced with carbon fibers through FFF additive manufacturing
Hasanzadeh, R. (2025) · Rapid Prototyping Journal · doi:10.1108/rpj-06-2025-0219 · Cited by 9 · see citing works
Defect detection in 3D printing: a review of image processing and machine vision techniques
Armin, E., Ebrahimian, S., Sanjari, M., et al. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-16382-1 · Cited by 8 · see citing works
Parameter optimization for accurate and repeatable strut width in the 3D printing of composite bone scaffolds
Bappy, M. M., Epps, E. V., Priddy, L. B., et al. (2024) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2024.09.057 · Cited by 7 · see citing works
Machine learning-driven optimization for surface roughness prediction of vertical orientation measurements on 3D printed components
Islahudin, N., Nugroho, D. S., Wijaya, D. K., et al. (2025) · Cleaner Engineering and Technology · doi:10.1016/j.clet.2025.101046 · Cited by 7 · see citing works
Emerging technologies for personalised oro-maxillofacial bone regeneration: Design strategies and fabrication techniques
Guo, H., Li, Q., Zreiqat, H., et al. (2025) · Biomaterials · doi:10.1016/j.biomaterials.2025.123709 · Cited by 7 · see citing works
The research on nozzles for microscale printing of high viscosity pastes containing micron-sized particles
Wu, S., Wang, Z., Cai, Y., et al. (2024) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2024.07.030 · Cited by 6 · see citing works
Advances and obstacles to unify the concept of “printability” in Ceramics Direct Ink Writing
Maillard, M., Fournier, S., García‐Tuñón, E., et al. (2025) · Open Ceramics · doi:10.1016/j.oceram.2025.100808 · Cited by 6 · see citing works
Enhancing predictive modeling for additive manufacturing components using hybrid machine learning algorithms
Shaikh, M., Patel, P., Jatti, V. S., et al. (2025) · Discover Materials · doi:10.1007/s43939-025-00437-x · Cited by 6 · see citing works
An end-to-end AI framework for data-driven evaluation and optimisation of direct ink writing-based 3D printing
Zhang, Q., Chen, Y., Zhou, A.-W., Ma, M., Zhou, L., Shi, H., Liu, M.-X., Iliescu, C., Zhang, X.-S., & Zhang, Y. (2025) · Virtual and Physical Prototyping · doi:10.1080/17452759.2025.2584948 · Cited by 5 · see citing works
Predicting Three-Dimensional (3D) Printing Product Quality with Machine Learning-Based Regression Methods
Tatar, A. B. (2025) · Firat University Journal of Experimental and Computational Engineering · doi:10.62520/fujece.1604379 · Cited by 5 · see citing works
Optimizing 3D printing through enhancements in inverse autoencoder models and guided decoders for predicting mechanical properties
Nguyen, P., Hoang, T.-D., & Nguyen, T. Q. (2025) · Progress in Additive Manufacturing · doi:10.1007/s40964-025-01151-z · Cited by 4 · see citing works
“Instead of manually testing each parameter, machine learning models can learn from previous data to predict the optimal printing parameters based on the desired mechanical properties [15–17].”
Toward advance/digitalized FFF: real-time multimodal synchronized data acquisition and ML/DL-driven process optimization
Nikooharf, M. H., Shirinbayan, M., Ghodsian, N., Aminharati, P., Bahlouli, N., Fitoussi, J., & Benfriha, K. (2025) · Progress in Additive Manufacturing · doi:10.1007/s40964-025-01187-1 · Cited by 4 · see citing works
Coaxial Bioprinting in Regenerative Medicine: Advances and Emerging Applications
Guanche, I. D., Joshua, T., Munkwitz, S. E., Torquati, M. S., Shah, H., Tadisina, K. K., Witek, L., Nayak, V. V., & Coelho, P. G. (2025) · Tissue Engineering Part B Reviews · doi:10.1177/19373341251381677 · Cited by 4 · see citing works
Machine learning in tissue engineering: a comprehensive review of applications, challenges, and prospects
Das, I. J., Samal, H. B., Nayak, P., & Swain, S. S. (2026) · Journal of Biomaterials Science Polymer Edition · doi:10.1080/09205063.2026.2675360 · Cited by 4 · see citing works
Advances in data-driven integrated design synthesis optimization and prediction of carbon nanotube
Li, Q., Jin, Q., Gao, C., Zhang, X., Zhao, X., He, Y., Chu, D., & Bai, W. (2025) · Carbon letters · doi:10.1007/s42823-025-00952-0 · Cited by 4 · see citing works
Revolutionizing Architecture: 3D Printing in Large Construction Industry and Strategic Innovations for Enhanced Performance
Murtaza, G., & Baldinelli, G. (2025) · Applied Research · doi:10.1002/appl.70033 · Cited by 5 · see citing works
Penalization and deep learning algorithms in Holographic Direct Sound Printing to improve print uniformity
Derayatifar, M., Habibi, M., Bhat, R., & Packirisamy, M. (2025) · Additive manufacturing · doi:10.1016/j.addma.2025.104782 · Cited by 2 · see citing works
Recent Advances in Textile Printing Techniques
Rabbi, M. B. U., Ferdous, T., Dina, R. B., et al. (2025) · Springer eBooks · doi:10.1007/978-981-96-4975-4_13 · Cited by 2 · see citing works
Machine learning integration for smarter, more adaptive systems in large-scale 3D printing
Barbhuiya, S., Qazi, N., Das, B. B., & Katare, V. D. (2026) · Proceedings of the Institution of Civil Engineers - Structures and Buildings · doi:10.1680/jstbu.25.00139 · Cited by 2 · see citing works
Prediction of 3D printability from rheological and mechanical characteristics of rice and vegetable-derived pastes using machine learning
Kamata, T., Umeda, T., Nei, D., et al. (2026) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2026.113077 · Cited by 2 · see citing works
Integration of 3D Printing and Machine Learning in Sustainable Construction: Feasibility and Challenges
Alkhawaldeh, A. A., Alhassan, M. A., Sawalha, A. A., et al. (2025) · Lecture notes in civil engineering · doi:10.1007/978-3-031-92029-5_1 · Cited by 2 · see citing works
Power spectral density moment of having defective 3D printed plastic beams under moving load based on deep learning
Nguyen, T. Q., Nguyen, N. N., & Tran, X. V. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02120-5 · Cited by 1 · see citing works
Examination of 3D printing parameters using machine learning
Altuğ, M., & Yılmaz, Y. (2025) · Pamukkale University Journal of Engineering Sciences · doi:10.65206/pajes.91679 · Cited by 1 · see citing works
A Review of Artificial Intelligence in 3D Printing. Part A: From Scaffold Design to Pre-Print Optimization
Shah, H., Munkwitz, S. E., Iglesias, N. J., et al. (2025) · Journal of Craniofacial Surgery · doi:10.1097/scs.0000000000012136 · Cited by 1 · see citing works
How internal voids affect the mechanical properties of 3D-printed thermoplastic polyurethane: A study based on porous morphology and machine learning analysis
Liu, W., Gong, M., Gao, F., et al. (2026) · Polymer · doi:10.1016/j.polymer.2026.129824 · Cited by 1 · see citing works
Neural network framework for predicting deposition thickness and electrical resistance in printed electronics
Ravindranath, A. N. K., Domala, S. S., Kannan, P., et al. (2026) · npj Flexible Electronics · doi:10.1038/s41528-025-00471-y · Cited by 1 · see citing works
“LightGBM (Fig.”
Influential
Artificial intelligence for marine oil spill management: Recent advances and future directions
Wang, Z., Huang, Y., Zhang, G., et al. (2026) · Marine Environmental Research · doi:10.1016/j.marenvres.2026.108108 · Cited by 1 · see citing works
Post-processing technologies for additively manufactured metals: surface engineering approaches for superior service performance
Mehta, A., Vasudev, H., Kumar, R. S., Singh, J., Prasad, B., Singh, S., Ramesha, H., & Prasad, C. D. (2026) · Canadian Metallurgical Quarterly · doi:10.1080/00084433.2026.2674528 · Cited by 1 · see citing works
Geometry-based pattern and trajectory design for calibration of machine parameters in extrusion-based bioprinting
Díez, J. M., Sánchez, L. P., Amilibia, M. Z., Cáceres, M. M. G., Mejia-Parra, D., Arbelaiz, A., Lin, X., & Cortes, C. A. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-18388-9 · Cited by 1 · see citing works
“A significant bottleneck in existing research is the reliance on manual placement during imaging and assessment [12, 15, 17, 31, 32, 34, 36, 37, 43], the proposed framework includes reference markers for automated registration, allowing the use of a vision system to align the original pattern with…”
Influential
Extrusion‐Based Additive Manufacturing of Advanced Ceramics: Strengthening Mechanisms and Process Optimization Review
Bakhtiari, M., Sheikh-Ansari, M. H., Borgaonkar, A., Dadkhah, M., & Saboori, A. (2026) · Advanced Engineering Materials · doi:10.1002/adem.71088 · Cited by 2 · see citing works
A Review of Current Progress and Application of Machine Learning on 3D-Printed Concrete
Nguyen, H. A. T., Thach, T. N., Le, Q. H., et al. (2023) · Lecture notes in civil engineering · doi:10.1007/978-981-99-7434-4_71 · Cited by 1 · see citing works
A Point-Line-Area Paradigm: 3D Printing for Next-Generation Health Monitoring Sensors
Ming, M., Yin, X., Luo, Y., et al. (2025) · Sensors · doi:10.3390/s25185777 · Cited by 1 · see citing works
Precision biomaterials printing: The way forward, a paradigm shift
Mozafari, M. (2023) · Methods · doi:10.1016/j.ymeth.2023.04.005
Optimization Analysis of Product Design Process Based on Reinforcement Learning Algorithm
Hu, Y., & Li, Q. (2024) · Frontiers in artificial intelligence and applications · doi:10.3233/faia231449
Biopolymeric additive manufacturing: The bidirectional interplay of synthetic and natural systemsPreprint
Lew, A. J., Lai, A. F., & Wyman, S. P. (2024) · Preprints.org · doi:10.20944/preprints202403.0657.v1
A Novel Method for Pore Morphology Prediction: Multi-Ghost Encoder Generative Network
Yang, Y., Feng, J., & Liu, X. (2024) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc62350.2024.10588070
“The application potential of this technique includes identifying and analyzing the patterns of processing parameters on porosity and material properties by processing large-scale data, so as to build predictive models or optimization strategies [4].”
Method
Application of Image Recognition Technology in Nozzle Cleaning for Material Extrusion Additive Manufacturing Processes
Kim, H.-C., Bae, Y.-H., Yun, H., et al. (2024) · Journal of the Korean Society of Manufacturing Process Engineers · doi:10.14775/ksmpe.2024.23.11.020
A Review of Machine Learning for Additive Manufacturing
Gavcar, B. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-70935-7_8
Autonomous Intuition for Advanced Manufacturing Systems: Benefits and Tradeoffs of Presampling for Autonomous Parameterization
Braham, E. J., Johnson, M. V., Fassler, A., et al. (2025) · Advanced Intelligent Discovery · doi:10.1002/aidi.202400037
Multimaterial printing for medical design
Renter, W. J., Habib, A. K. M. A., Khalil, M., Tomori, M., Khatri, N. R., & Egan, P. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29228-6.00017-7
AI Assisted 3D Food Printing Technology: An Overview
Rasheeda, M., Remya, S., Mohan, C. O., et al. (2025) · Royal Society of Chemistry eBooks · doi:10.1039/9781837679119-00268
PLA Damage Properties in FDM Printing: A Hybrid Experimental and Machine Learning Study
Dhouioui, M., Fraj, B. B., Hentati, H., et al. (2025) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-94889-3_15
3D printing for therapeutic strategy
Patel, P., Patel, D., Patel, G. C., et al. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-16100-1.00008-4
Nanofluidics, 3D nano-printing, and organoids for biomedical technology
Khondakar, K. R., Das, S., & Mazumdar, H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26492-4.00005-0
AI assisted mechanical and performance testing of lightweight composites
Gupta, N., & Chen, G.-L. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-18852-7.00008-7
Construction of Various Materials Using Stationary or Mobile Systems Ranging from Polymers and Concrete to Metals and Ceramics Using Advanced Sintering: A Review
(2025) · Journal of Materials and Polymer Science · doi:10.47485/2832-9384.1080
Intelligent Additive Manufacturing is Unleashing the Power of 3D Printing with Machine Learning
Vempati, S., Rao, S. P., Srinivasulu, et al. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-95-0375-9_33
Artificial intelligence-enabled personalisation of oral drug delivery: From data-driven design to on-demand manufacturing
Abdalla, Y., Nandiraju, L. P., Yue, H., Monsales, C. B. D., Yeung, C., & Basit, A. W. (2026) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2026.115855 · Cited by 3 · see citing works
Prospects of Utilizing Additive Manufacturing Technologies in Photovoltaic Cell Making
AlJassmi, A., Khan, S., Alashkar, A., et al. (2025) · Advances in Science and Engineering Technology International Conferences (ASET) · doi:10.1109/aset66891.2025.11428021
“For instance, Dabbagh et al. [59] used Machine Learning (ML) to optimize extrusion-based printing parameters such as temperature and pressure instead of manual tuning.”
A parameter-aware graph neural network for estimation of manufacturing resources in additive manufacturing
Giovenali, N., Bruno, G., Chiabert, P., & Segonds, F. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-17892-2
Multiscale study of the intelligent optimization of dimensional stability and mechanical properties of structures obtained by additive manufacturing
Nikooharftamiz, M. (2024) · doi:10.70675/177df798z18ecz44d3zadfcz1d64e0752b16
Applications of Artificial Intelligence and Machine Learning in 3D Concrete Printing
Chourasia, A., Pal, B., Kapoor, A., et al. (2026) · Materials horizons · doi:10.1007/978-981-95-8557-1_12
AI-Enabled Polymer Processing Technology
Ray, S. S., Banerjee, R., Bandyopadhyay, J., et al. (2025) · Carl Hanser Verlag GmbH & Co. KG eBooks · doi:10.1007/978-1-56990-327-8_10
Data-driven multi-objective optimization of 3D-printed biodegradable polymer optical fiber preforms
Liu, X., Luo, Y., Mou, C., et al. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae8747
Hybrid PINN–Bayesian multi-fidelity learning for cross alloy defect severity modeling and scalable control in metal additive manufacturing
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Authors: Rahmani Dabbagh, S., Ozcan, O., & Tasoglu, S.
Venue: Methods
Publisher: Elsevier BV
Published: 2022-10
Volume: 206
Pages: 27-40
ISSN: 1046-2023 (ISSN-L)
DOI: 10.1016/j.ymeth.2022.08.002
OpenAlex: W4291002276
Semantic Scholar: 8c22d14b605943b77065bb467795dae30593ed1a
Cited by: 94
References: 99
Citations by year: 2026: 19 · 2025: 39 · 2024: 20 · 2023: 13 · 2022: 3
FWCI: 5.11
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Methods (publishedVersion)
PubMed (publishedVersion)
Digital Collections portal (Koç University) (submittedVersion)
Topics: Physical Sciences › Engineering › Automotive Engineering › Additive Manufacturing and 3D Printing Technologies
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Engineering › Building and Construction › Innovations in Concrete and Construction Materials
Keywords: material extrusion, printing parameter optimization, machine learning, gradient boosting regression, graphical user interface, material waste reduction
MeSH terms: Machine Learning, Algorithms, Humans, Computer-Aided Design, Printing, Three-Dimensional

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Machine learning-enabled optimization of extrusion-based 3D printing

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