Generative Lightweighting of Actuator-Integrated Robotic Structures Under Decentralized Additive Manufacturing Constraints

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Volume: 12 | Issue: 1 | Year 2026 | Subscription
International Journal of Mechanics and Design
Received Date: 02/26/2026
Acceptance Date: 02/27/2026
Published On: 2026-03-15
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By: Pedro Portugal.

School of Engineering and Sciences, Tecnológico de Monterrey

Abstract

This study presents the implementation and structural validation of a constraint-driven generative design methodology for the development of lightweight actuator-integrated robotic components fabricated using decentralized additive manufacturing. Building upon a previously formalized constraint-driven framework, the method was applied to the synthesis of a modular robotic link representative of actuator-integrated structural elements commonly used in legged robotic systems. The baseline component was defined to preserve critical functional interfaces, including actuator mounting geometry, modular mechanical connections, and fabrication feasibility within consumer-grade fused deposition modeling environments. Generative refinement was performed within a bounded design space under representative quasi-static loading conditions derived from worst-case operational scenarios. The resulting geometries demonstrated clear structural redistribution along primary load paths, producing mechanically interpretable and fabrication-ready designs without compromising functional interface integrity.

Comparative structural evaluation using finite element analysis indicated improved stress distribution and a mass reduction of up to 64.9% relative to the baseline design, while maintaining acceptable structural safety margins. The optimized component was successfully fabricated using consumer-grade additive manufacturing without requiring post-generation geometric correction, support-driven redesign, or dimensional compensation. Dimensional inspection and analytical dynamic validation confirmed structural plausibility under representative operational loading conditions. These results demonstrate that constraint-driven generative design enables the reliable synthesis of actuator-integrated robotic components that are lightweight, mechanically credible, and directly manufacturable using decentralized additive manufacturing. The methodology provides a practical pathway for improving structural efficiency and manufacturability in modular robotic systems developed outside centralized industrial production environments.

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How to cite this article: Pedro Portugal Generative Lightweighting of Actuator-Integrated Robotic Structures Under Decentralized Additive Manufacturing Constraints. International Journal of Mechanics and Design. 2026; 12(1): -p.

How to cite this URL: Pedro Portugal, Generative Lightweighting of Actuator-Integrated Robotic Structures Under Decentralized Additive Manufacturing Constraints. International Journal of Mechanics and Design. 2026; 12(1): -p. Available from:https://journalspub.com/publication/ijmd/article=26019

Refrences:

  1. Semini, C., Tsagarakis, N. G., Guglielmino, E., Focchi, M., Cannella, F., & Caldwell, D. G. (2015). Design of HyQ – a hydraulically and electrically actuated quadruped robot. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 225(6), 831–849. https://doi.org/10.1177/0959651811402275
  2. Andrade, R. L., Figueiredo, J., Fonseca, P., Vilas-Boas, J. P., Silva, M. T., & Santos, C. P. (2024). Human-Robot Joint Misalignment, Physical Interaction, and Gait Kinematic Assessment in Ankle-Foot Orthoses. Sensors, 24(1), 246. https://doi.org/10.3390/s24010246
  3. Yu, Y., Liu, J., You, Y., Tan, Q., Xu, X., Zheng, Y., & Fan, Z. (2024). Modeling and motion analysis of flexible legged robots using the finite particle method. Thin-Walled Structures, 205(Part B), 112491. https://doi.org/10.1016/j.tws.2024.112491
  4. Yim, M., Shen, W.-M., Salemi, B., Rus, D., Moll, M., Lipson, H., … Chirikjian, G. S. (2007). Modular self-reconfigurable robot systems: Challenges and opportunities for the future. IEEE Robotics & Automation Magazine, 14(1), 43–52. https://doi.org/10.1109/MRA.2007.339623
  5. Moubarak, P., & Ben-Tzvi, P. (2012). Modular and reconfigurable mobile robotics. Robotics and Autonomous Systems, 60(12), 1648–1663.https://doi.org/10.1016/j.robot.2012.09.002
  6. Gibson, I., Rosen, D. W., & Stucker, B. (2021). Additive manufacturing technologies: 3D printing, rapid prototyping, and direct digital manufacturing (3rd ed.). Springer. https://doi.org/10.1007/978-3-030-56127-7
  7. Brackett, D., Ashcroft, I., & Hague, R. (2011). Topology optimization for additive manufacturing. Proceedings of the Solid Freeform Fabrication Symposium, 348–362.
  8. Thompson, M. K., Moroni, G., Vaneker, T., Fadel, G., Campbell, R. I., Gibson, I., … Martina, F. (2016). Design for additive manufacturing: Trends, opportunities, considerations, and constraints. CIRP Annals, 65(2), 737–760. https://doi.org/10.1016/j.cirp.2016.05.004
  9. Bledt, G., Powell, M. J., Katz, B., Di Carlo, J., Wensing, P. M., & Kim, S. (2018). Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2245–2252. https://doi.org/10.1109/IROS.2018.8594448
  10. Elliot W. Hawkes, Mark R. Cutkosky. 2018. Design of Materials and Mechanisms for Responsive Robots. Annual Review Control, Robotics, and Autonomous Systems. 1:359-384. https://doi.org/10.1146/annurev-control-060117-104903
  11. Correia, Arménio & Coelho, Rodrigo & Braga, Daniel & Guedes, Mafalda & Baptista, Ricardo & Infante, V.. (2025). Effect of Service Temperature on the Mechanical and Fatigue Behaviour of Metal–Polymer Friction Stir Composite Joints. Polymers. 17. 1366. 10.3390/polym17101366
  12. Mohammadi Esfarjani, S., Dadashi, A., & Azadi, M. (2022). Topology optimization of additive-manufactured metamaterial structures: A review focused on multi-material types. Forces in Mechanics, 7, 100100. https://doi.org/10.1016/j.finmec.2022.100100
  13. Erden, Mustafa Suphi & Leblebicioglu, Kemal. (2006). Torque distribution for a six-legged robot. 2006. 10.1109/SIU.2006.1659771.
  14. Bikas, Harry & Stavropoulos, Panagiotis. (2019). A design framework for additive manufacturing. The International Journal of Advanced Manufacturing Technology. 103. 10.1007/s00170-019-03627-z.
  15. Thompson, M. K., Moroni, G., Vaneker, T., Fadel, G., Campbell, R. I., Gibson, I., … Martina, F. (2016). Design for additive manufacturing: Trends, opportunities, considerations, and constraints. CIRP Annals, 69(2), 779–800. https://doi.org/10.1016/j.cirp.2016.05.004
  16. Rankouhi, B., Javadpour, S., Delfanian, F. et al. Failure Analysis and Mechanical Characterization of 3D Printed ABS With Respect to Layer Thickness and Orientation. J Fail. Anal. and Preven. 16, 467–481 (2016). https://doi.org/10.1007/s11668-016-0113-2
  17. Domingo-Espin, M., Puigoriol-Forcada, J. M., Garcia-Granada, A. A., Llumà, J., Borros, S., & Reyes, G. (2015). Mechanical property characterization and simulation of fused deposition modeling Polycarbonate parts. Materials & Design, 155, 670–680. https://doi.org/10.1016/j.matdes.2015.06.074
  18. Birosz, M. T., Safranyik, F., & Andó, M. (2022). Build orientation optimization of additive manufactured parts for better mechanical performance by utilizing the principal stress directions. Journal of Manufacturing Processes, 84, 1094–1102. https://doi.org/10.1016/j.jmapro.2022.10.038
  19.  Almesmari, A., Baghous, N., Ejeh, C. J., Barsoum, I., & Abu Al-Rub, R. K. (2023). Review of additively manufactured polymeric metamaterials: design, fabrication, testing and modeling. Polymers, 15(19), 3858. https://doi.org/10.3390/polym15193858
  20. Francalanza, E., Fenech, A., & Cutajar, P. (2018). Generative design in the development of a robotic manipulator. Procedia CIRP, 67, 244–249. https://doi.org/10.1016/j.procir.2017.12.207
  21. Walia, K., Khan, A., & Breedon, P. (2021). Polymer-Based Additive Manufacturing: Process Optimisation for Low-Cost Industrial Robotics Manufacture. Polymers, 13(16), 2809. https://doi.org/10.3390/polym13162809
  22. Huang, R., Riddle, M., Graziano, D., Warren, J., Das, S., Nimbalkar, S., Cresko, J., & Masanet, E. (2016). Energy and emissions saving potential of additive manufacturing: The case of lightweight aircraft components. Journal of Cleaner Production, 135, 1559–1570. https://doi.org/10.1016/j.jclepro.2015.04.109
  23. Yuan, S., Shen, F., Chua, C. K., & Zhou, K. (2019). Polymeric composites for powder-based additive manufacturing: Materials and applications. Progress in Polymer Science, 91, 141–168. https://doi.org/10.1016/j.progpolymsci.2018.11.001