Heated Chamber in Fused Deposition Modeling: A Content Analysis of Research Trends and Methodological Gaps (2020-2026)

Heated chamber control has become a widely cited strategy for improving dimensional accuracy and mechanical performance in fused deposition modeling (FDM), yet the literature that establishes this claim has not itself been examined as a body of research. This study presents a focused content analysis of 13 peer-reviewed sources addressing heated chamber, enclosure, or thermal-environment control in FDM. Each source was coded by publication year and by whether it employed a structured statistical design method (Design of Experiments or Response Surface Methodology), a Statistical Process Control (SPC) framework, or a purely experimental or descriptive approach without a named statistical structure. Results show a modest but steady publication stream since 2020, with a small peak in 2023 and 2025, indicating that heated-chamber-specific research remains a comparatively narrow, still-emerging sub-literature within the broader FDM field. Only two of the thirteen sources employed a structured statistical design method, and only one employed an explicit Statistical Process Control framework; the remaining ten relied on experimental or descriptive approaches without a named statistical structure. This imbalance indicates that, despite widespread agreement in the literature that chamber temperature is a critical process variable, few studies have applied rigorous experimental design or process-monitoring frameworks to quantify or sustain its effects. The findings reinforce the case for statistically structured, process-control-oriented research on heated chamber control and illustrate how a small, focused literature corpus can be repurposed as a dataset for identifying methodological gaps in an applied engineering research area.
Keywords: heated chamber, fused deposition modeling, content analysis, design of experiments, statistical process control, research trends

Engineering – Additive Manufacturing / Fused Deposition Modeling (FDM)

1. Alatefi, M., Al-Ahmari, A. M., AlFaify, A. Y., & Saleh, M. (2023). A framework for multivariate statistical quality monitoring of additive manufacturing: Fused filament fabrication process. Processes, 11(4), 1216. https://doi.org/10.3390/pr11041216 [Google Scholar] [Crossref]

2. Boytsov, E., Blaginin, S., & Sinkov, A. (2022). Why we need a heated chamber for 3D printing with 'high performance' polymers? In Materials Research Proceedings (pp. 237-246). Association of American Publishers. https://doi.org/10.21741/9781644901755-42 [Google Scholar] [Crossref]

3. Castillo, M., Monroy, R., & Ahmad, R. (2023). Design of experiments to compare the mechanical properties of polylactic acid using material extrusion three-dimensional-printing thermal parameters based on a cyber-physical production system. Sensors, 23. https://doi.org/10.3390/s23249833 [Google Scholar] [Crossref]

4. Cozzolino, E., Napolitano, F., Papa, I., Squillace, A., & Astarita, A. (2025). Influence of the heated-bed material on PLA mechanical properties and energy consumption in the FDM process. Arabian Journal for Science and Engineering, 50(3), 2443-2453. https://doi.org/10.1007/s13369-024-09170-8 [Google Scholar] [Crossref]

5. Demir, S. (2025). Optimization of mechanical properties in FDM 3D-printed PLA parts: Evaluating the interaction of key parameters. Journal of Materials Engineering and Performance, 34(5), 28566-28580. [Google Scholar] [Crossref]

6. Ekşi, S., & Karakaya, C. (2025). Effects of process parameters on tensile properties of 3D-printed PLA parts fabricated with the FDM method. Polymers, 17(14), 1934. https://doi.org/10.3390/polym17141934 [Google Scholar] [Crossref]

7. Fang, L., Yan, Y., Agarwal, O., Kang, S. H., Yao, S., & Seppala, J. E. (2020). Effects of environmental temperature and humidity on the geometry and strength of polycarbonate specimens prepared by fused filament fabrication. Materials, 13, 1-16. https://doi.org/10.3390/ma13194414 [Google Scholar] [Crossref]

8. Napolitano, F., Cozzolino, E., Papa, I., Astarita, A., & Squillace, A. (2022). Experimental integrated approach for mechanical characteristic optimization of FDM-printed PLA in an energy-saving perspective. International Journal of Advanced Manufacturing Technology, 121, 3551-3565. https://doi.org/10.1007/s00170-022-09535-z [Google Scholar] [Crossref]

9. Ruiz-González, S., et al. (2024). Eco-design applied to FDM 3D printing technology. In 2024 5th International Conference in Electronic Engineering, Information Technology & Education (EEITE) (pp. 1-6). Chania, Greece. https://doi.org/10.1109/EEITE61750.2024.10654395 [Google Scholar] [Crossref]

10. Tan, W. S., Tanoto, Y. Y., Jonoadji, N., & Christian, A. A. (2021). The effect of cooling and temperature in 3D printing process with fused deposition modelling technology on the mechanical properties with polylactic acid recycled material. International Review of Mechanical Engineering, 15, 615-621. https://doi.org/10.15866/ireme.v15i12.21573 [Google Scholar] [Crossref]

11. Thumsorn, S., Prasong, W., Ishigami, A., Kurose, T., Kobayashi, Y., & Ito, H. (2023). Influence of ambient temperature and crystalline structure on fracture toughness and production of thermoplastic by enclosure FDM 3D printer. Journal of Manufacturing and Materials Processing, 7. https://doi.org/10.3390/jmmp7010044 [Google Scholar] [Crossref]

12. Yu, N., Sun, X., Wang, Z., Zhang, D., & Li, J. (2021). Effects of auxiliary heat on the interlayer bonds and mechanical performance of polylactide manufactured through fused deposition modeling. Polymer Testing, 104. https://doi.org/10.1016/j.polymertesting.2021.107390 [Google Scholar] [Crossref]

13. Zurnacı, E., & Alıcıoğlu, F. (2026). Mechanical strength of PLA parts manufactured by FDM using RSM and fuzzy logic. Materials Testing, 68(1), 50-66. https://doi.org/10.1515/mt-2025-0307 [Google Scholar] [Crossref]

• Analysis of the Impact of Lean Principles on the U-Bolt and Nut Manufacturing Processes
• A Digital Twin-Driven Deep Learning Framework for Real-Time Fault Prediction and Predictive Maintenance in Smart Manufacturing
• Beyond Generic Differentiation: Product Novelty, Quality Reliability and Perceived Performance in Kenya’s Cement Industry