Energy Efficiency Analysis of 3D Printing Machines (Additive Manufacturing) in Supporting Local MSMEs: Poverty Alleviation Opportunities in the Digital Era
Downloads
Background. The rapid development of additive manufacturing, particularly 3D printing technology, has created new opportunities for small and medium enterprises (SMEs) to engage in flexible and low-volume production within the digital economy. Energy efficiency has become a critical factor influencing the operational feasibility of this technology, especially for SMEs that operate with limited financial resources and infrastructure. Understanding the energy performance of 3D printing machines is therefore essential for evaluating their potential role in supporting sustainable entrepreneurship and poverty alleviation.
Purpose. This study aims to analyze the energy efficiency of 3D printing machines in additive manufacturing processes and examine their potential contribution to strengthening local SMEs and expanding economic opportunities in the digital era.
Method. The research employs a quantitative experimental design combined with descriptive analysis. Energy consumption data were collected using digital power meters during standardized printing experiments involving several desktop 3D printers commonly used by SMEs. Statistical analysis was conducted to compare machine efficiency and evaluate the relationship between printing parameters and electricity consumption.
Results. The findings indicate significant differences in energy consumption across machine types and printing configurations. Optimized parameter settings and efficient machine architecture reduce electricity usage and operational costs. Evidence from SME production contexts demonstrates that energy-efficient additive manufacturing supports faster prototyping and flexible small-scale production.
Conclusion. Energy-efficient 3D printing technology represents a promising tool for strengthening SME productivity and fostering inclusive economic development in the digital era.
Aktepe, E., & Ergün, U. (2025). Machine Learning Approaches for FDM-Based 3D Printing: A Literature Review. Applied Sciences, 15(18), 10001. https://doi.org/10.3390/app151810001
An, Q., Li, D., Liao, W., Liu, T., & Li, W. (2025). Lightweight Electromagnetic Wave?Absorbing Metastructures: Advances in 3D Printing?Based Design and Manufacturing. Advanced Materials Technologies, 10(17), e00581. https://doi.org/10.1002/admt.202500581
Azher, K., Nazir, A., Farooq, M. U., Haq, M. R. U., Ali, Z., Dalaq, A. S., Abubakar, A. A., Hussain, S., Syed, M. N., Ullah, A., Laghari, R. A., & Khan, S. (2025). Revolutionizing the Future of Smart Materials: A Review of 4D Printing, Design, Optimization, and Machine Learning Integration. Advanced Materials Technologies, 10(12), 2401369. https://doi.org/10.1002/admt.202401369
Branco, F., Cunha, J., Mendes, M., Sousa, J. J., & Vitorino, C. (2025). 3D Bioprinting Models for Glioblastoma: From Scaffold Design to Therapeutic Application. Advanced Materials, 37(18), 2501994. https://doi.org/10.1002/adma.202501994
Chi, X., Xue, J., Jia, L., Yao, J., Miao, H., Wu, L., Liu, T., Tian, X., & Li, D. (2025). Machine Learning-Based Online Monitoring and Closed-Loop Controlling for 3D Printing of Continuous Fiber-Reinforced Composites. Additive Manufacturing Frontiers, 4(2), 200196. https://doi.org/10.1016/j.amf.2025.200196
Dai, Y., Wang, P., Mishra, A., You, K., Zong, Y., Lu, W. F., Chow, E. K., Preshaw, P. M., Huang, D., Chew, J. R. J., Ho, D., & Sriram, G. (2025). 3D Bioprinting and Artificial Intelligence?Assisted Biofabrication of Personalized Oral Soft Tissue Constructs. Advanced Healthcare Materials, 14(13), 2402727. https://doi.org/10.1002/adhm.202402727
Docherty, N., Macdonald, D., Gordon, A., Dobrea, A., Mani, V., Fu, Y., Pang, S., Jimenez, M., & Corrigan, D. K. (2025). Maximising the translation potential of electrochemical biosensors. Chemical Communications, 61(71), 13359–13377. https://doi.org/10.1039/D5CC02322J
Ejeromedoghene, O., Kumi, M., Akor, E., & Zhang, Z. (2025). The application of machine learning in 3D/4D printed stimuli-responsive hydrogels. Advances in Colloid and Interface Science, 336, 103360. https://doi.org/10.1016/j.cis.2024.103360
Elsayed, M. S., El-Kouedi, A. Y., & Shokry, T. E. (2025). Effect of aging on the marginal fit of milled and printed zirconia crowns: An in-vitro study. BMC Oral Health, 25(1), 221. https://doi.org/10.1186/s12903-025-05542-0
Geng, Z., Wu, Z., Wang, X., Zhang, L., She, W., & Tan, M. J. (2025). A novel 3D printing scheme for lunar construction with extremely low binder utilization. Additive Manufacturing, 99, 104657. https://doi.org/10.1016/j.addma.2025.104657
Grira, S., Mozumder, M. S., Mourad, A.-H. I., Ramadan, M., Khalifeh, H. A., & Alkhedher, M. (2025). 3D bioprinting of natural materials and their AI-Enhanced printability: A review. Bioprinting, 46, e00385. https://doi.org/10.1016/j.bprint.2025.e00385
Han, L., Li, K., Wang, Z., Men, W., Wu, X., Sun, X., Zhang, J., & Cheng, J. (2025). 3D Printing Flexible Wearable Electronics with Diversified Environmentally Adaptive for Biomechanical Energy Harvesting and Personal Electromagnetic Safety. Advanced Functional Materials, 35(37), 2424743. https://doi.org/10.1002/adfm.202424743
Jian, W., Chen, Y., & Feng, X. (2025). 3D Conformal Curvy Electronics: Design, Fabrication, and Application. ACS Nano, 19(16), 15177–15188. https://doi.org/10.1021/acsnano.5c03179
Jin, Y., Xue, S., & He, Y. (2025). Flexible Pressure Sensors Enhanced by 3D?Printed Microstructures. Advanced Materials, 37(27), 2500076. https://doi.org/10.1002/adma.202500076
Kantaros, A., Petrescu, F. I. T., & Ganetsos, T. (2025). From Stents to Smart Implants Employing Biomimetic Materials: The Impact of 4D Printing on Modern Healthcare. Biomimetics, 10(2), 125. https://doi.org/10.3390/biomimetics10020125
Kelly, D., Sergis, V., Ventura I Blanco, L., Mason, K., & Daly, A. C. (2025). Autonomous Control of Extrusion Bioprinting Using Convolutional Neural Networks. Advanced Functional Materials, 35(30), 2424553. https://doi.org/10.1002/adfm.202424553
Lee, S.-M., & Park, S.-H. (2025). Autonomous in-situ defect detection and correction in additive-lathe 3D printing process using variational autoencoder model. Additive Manufacturing, 98, 104635. https://doi.org/10.1016/j.addma.2024.104635
Li, Z., Zeng, K., Guo, Z., Wang, Z., Yu, X., Li, X., & Cheng, L. (2025). All?in?One: An Interwoven Dual?Phase Strategy for Acousto?Mechanical Multifunctionality in Microlattice Metamaterials. Advanced Functional Materials, 35(20), 2420207. https://doi.org/10.1002/adfm.202420207
Liu, T., Zhang, T., Chen, Y., Wang, W., Jiang, Y., Huang, Y., & Wang, C. C. L. (2025). Neural Co-Optimization of Structural Topology, Manufacturable Layers, and Path Orientations for Fiber-Reinforced Composites. ACM Transactions on Graphics, 44(4), 1–17. https://doi.org/10.1145/3730922
Lu, J., Shi, X., Zhou, Z., Lu, N., Chu, G., Jin, H., Zhu, L., & Chen, A. (2025). Enhancing Fracture Healing with 3D Bioprinted Hif1a?Overexpressing BMSCs Hydrogel: A Novel Approach to Accelerated Bone Repair. Advanced Healthcare Materials, 14(3), 2402415. https://doi.org/10.1002/adhm.202402415
Mao, Z., Suzuki, S., Nabae, H., Miyagawa, S., Suzumori, K., & Maeda, S. (2025). Machine learning-enhanced soft robotic system inspired by rectal functions to investigate fecal incontinence. Bio-Design and Manufacturing, 8(3), 482–494. https://doi.org/10.1631/bdm.2400152
Miko?ajewska, E., Miko?ajewski, D., Miko?ajczyk, T., & Paczkowski, T. (2025). A Breakthrough in Producing Personalized Solutions for Rehabilitation and Physiotherapy Thanks to the Introduction of AI to Additive Manufacturing. Applied Sciences, 15(4), 2219. https://doi.org/10.3390/app15042219
Najeeb, M., & Islam, S. (2025). Artificial intelligence (AI) in restorative dentistry: Current trends and future prospects. BMC Oral Health, 25(1), 592. https://doi.org/10.1186/s12903-025-05989-1
Nida, S., Moses, J. A., & Anandharamakrishnan, C. (2025). 3D printed food package casings from sugarcane bagasse: A waste valorization study. Biomass Conversion and Biorefinery, 15(2), 1835–1845. https://doi.org/10.1007/s13399-021-01982-0
Omigbodun, F. T., & Oladapo, B. I. (2025). AI-Optimized Lattice Structures for Biomechanics Scaffold Design. Biomimetics, 10(2), 88. https://doi.org/10.3390/biomimetics10020088
Pancholi, S., Gupta, M. K., Bartoszuk, M., Vashishtha, G., Ross, N. S., Korkmaz, M. E., Krolczyk, G. M., & Petru, J. (2025). Transforming Additive Manufacturing with Artificial Intelligence: A Review of Current and Future Trends. Archives of Computational Methods in Engineering, 32(8), 4691–4722. https://doi.org/10.1007/s11831-025-10283-y
Rojek, I., Miko?ajewski, D., Kempi?ski, M., Galas, K., & Piszcz, A. (2025). Emerging Applications of Machine Learning in 3D Printing. Applied Sciences, 15(4), 1781. https://doi.org/10.3390/app15041781
S., A., S., R., Rusho, M. A., & Yishak, S. (2025). Bridging Plant Biotechnology and Additive Manufacturing: A Multicriteria Decision Approach for Biopolymer Development. Advances in Polymer Technology, 2025(1), 9685300. https://doi.org/10.1155/adv/9685300
Scheideler, W. J., & Im, J. (2025). Recent Advances in 3D Printed Electrodes – Bridging the Nano to Mesoscale. Advanced Science, 12(9), 2411951. https://doi.org/10.1002/advs.202411951
Schossler, R. T., Ullah, S., Alajlan, Z., & Yu, X. (2025). Data-driven analysis in 3D concrete printing: Predicting and optimizing construction mixtures. AI in Civil Engineering, 4(1), 1. https://doi.org/10.1007/s43503-024-00044-4
Shang, X., Talbot, A., Li, E., Wen, H., Lyu, T., Zhang, J., & Zou, Y. (2025). Accurate inverse process optimization framework in laser directed energy deposition. Additive Manufacturing, 102, 104736. https://doi.org/10.1016/j.addma.2025.104736
Shin, J., Kang, R., Hyun, K., Li, Z., Kumar, H., Kim, K., Park, S. S., & Kim, K. (2025). Machine Learning?Enhanced Optimization for High?Throughput Precision in Cellular Droplet Bioprinting. Advanced Science, 12(20), 2412831. https://doi.org/10.1002/advs.202412831
Soori, M., Jough, F. K. G., Dastres, R., & Arezoo, B. (2025). Additive Manufacturing Modification by Artificial Intelligence, Machine Learning, and Deep Learning: A Review. Additive Manufacturing Frontiers, 4(2), 200198. https://doi.org/10.1016/j.amf.2025.200198
Subramani, R., Ali Rusho, M., & Jia, X. (2025). Machine learning-driven sustainable optimization of rapid prototyping via FDM: Enhancing mechanical strength, energy efficiency, and SDG contributions of thermoplastic composites. Applied Chemical Engineering, 8(2). https://doi.org/10.59429/ace.v8i2.5621
Tang, T., Zhang, M., Jia, H., Adhikari, B., & Guo, Z. (2025). Real-time freshness monitoring of fruits and vegetables integrating 3D-printed alginate-based colorimetric sensors with deep convolutional neural networks. Chemical Engineering Journal, 520, 166387. https://doi.org/10.1016/j.cej.2025.166387
Wang, C., Li, J., Wang, T., & Wang, X. (2025). Additive manufacturing of furniture corner guards based on thermoplastic polyurethane filament. BioResources, 20(3), 5398–5406. https://doi.org/10.15376/biores.20.3.5398-5406
Weeks, R. D., Ruddock, J. M., Berrigan, J. D., Lewis, J. A., & Hardin, James. O. (2025). In?Situ Rheology Measurements via Machine?Learning Enhanced Direct?Ink?Writing. Advanced Intelligent Systems, 7(1), 2400293. https://doi.org/10.1002/aisy.202400293
Wittek, A., Strizek, B., & Recker, F. (2024). Innovations in ultrasound training in obstetrics. Archives of Gynecology and Obstetrics, 311(3), 871–880. https://doi.org/10.1007/s00404-024-07777-8
Yang, C., Shen, Z., Cui, Y., Zhang, N., Zhang, L., Yan, R., & Chen, X. (2025). Terahertz molecular vibrational sensing using 3D printed anapole meta-biosensor. Biosensors and Bioelectronics, 278, 117351. https://doi.org/10.1016/j.bios.2025.117351
Yang, J., Yang, K., An, X., Fan, Z., Li, Y., Yin, L., Long, Y., Pan, G., Liu, H., & Ni, Y. (2025). Highly Flexible, Stretchable, and Compressible Lignin?Based Hydrogel Sensors with Frost Resistance for Advanced Bionic Hand Control. Advanced Functional Materials, 35(11), 2416916. https://doi.org/10.1002/adfm.202416916
Yang, W., Zheng, C., Sun, L., Bie, Z., Yue, Y., Li, X., Sun, W., Ikeda, T., Wang, J., & Jiang, L. (2025). Spatiotemporal Programmability of 3D Chiral Color Units Driven by Ink Spontaneous Diffusion toward Customized Printing. Advanced Materials, 37(4), 2411988. https://doi.org/10.1002/adma.202411988
Yuan, L.-X., Liu, C.-Y., Yang, J.-P., & Wang, Z.-J. (2025). Soft Crawling and Flipping Robots Based on Liquid Metal-Liquid Crystal Elastomer Composites. Chinese Journal of Polymer Science, 43(4), 588–596. https://doi.org/10.1007/s10118-025-3276-z
Zhang, L., Wang, S., & Hou, Y. (2025). Magnetic Micro/nanorobots in Cancer Theranostics: From Designed Fabrication to Diverse Applications. ACS Nano, 19(8), 7444–7481. https://doi.org/10.1021/acsnano.4c10382
Zhang, Y., Cui, S., Yang, B., Wang, X., & Liu, T. (2025). Research on 3D printing concrete mechanical properties prediction model based on machine learning. Case Studies in Construction Materials, 22, e04254. https://doi.org/10.1016/j.cscm.2025.e04254
Zhang, Z., Zhou, X., Fang, Y., Xiong, Z., & Zhang, T. (2025). AI-driven 3D bioprinting for regenerative medicine: From bench to bedside. Bioactive Materials, 45, 201–230. https://doi.org/10.1016/j.bioactmat.2024.11.021
Zhao, Y. C., Wang, Z., Zhao, H., Yap, N. A., Wang, R., Cheng, W., Xu, X., & Ju, L. A. (2025). Sensing the Future of Thrombosis Management: Integrating Vessel-on-a-Chip Models, Advanced Biosensors, and AI-Driven Digital Twins. ACS Sensors, 10(3), 1507–1520. https://doi.org/10.1021/acssensors.4c02764
Zhu, Z., Zhao, Y., Zhang, Y., Zhang, S., Li, W., Ye, G., Ma, X., Zhang, X., & Bi, H. (2025). Self-healing, highly stretchable, and 3D printable thiol-functionalized cellulose nanofibers/waterborne polyurethane composites for flexible electronic monitoring. Chemical Engineering Journal, 506, 159835. https://doi.org/10.1016/j.cej.2025.159835
Zolfagharian, A., Demoly, F., Lakhi, M., Rolfe, B., & Bodaghi, M. (2025). Bistable Mechanisms 3D Printing for Mechanically Programmable Vibration Control. Advanced Engineering Materials, 2402233. https://doi.org/10.1002/adem.202402233
Copyright (c) 2026 Anton Nugroho, Wawan Wawan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
