Spiral Torsion Springs (STS) are generally manufactured employing medium/high-carbon steel alloys shaped as thin rods with rectangular cross section. Meanwhile, plastic materials (e.g. ABS or PLA), currently used in freeform manufacturing processes, may not be suited for several applications, owing to the low material yield strength and the rather poor fatigue life. Despite the above-mentioned limitations, the main advantages of a 3D printing process, as compared to more traditional manufacturing techniques, are the design flexibility and the possibility to directly integrate elastic components within a joint mechanism produced as a single (monolithic) part. In particular, provided that the external forces acting on the spring coils are maintained within a certain threshold and that the spring geometry is suitably optimized, a reliable 3D-printed STS alternative to traditional steel springs is actually feasible. Given these premises, the main purpose of the present paper is to propose a model-based optimization algorithm that allows to optimally size STS for user-specified torque-deflection characteristics. Optimal STS geometries are then realized in ABS via Fused Deposition Manufacturing, and subsequently tested with a purposely-designed experimental set-up. Furthermore, the behavior of each STS sample (in terms of stiffness and equivalent Von Mises stress) is evaluated by means of non-linear finite elements analysis, in order to check the correspondence with the expected behavior. Finally, numerical and experimental results are provided, which demonstrate the prediction capabilities of the proposed modeling/optimization techniques, and confirm that well-behaved STS can be conceived and produced. Envisaged applications concern the development of smart structures for robot design, such as multi-articulated compliant robotic chains that can be used as low-cost manipulators (i.e. arm) or as mini-manipulators (i.e. fingers). The proposed approach effectively simplifies the production and the assembly of the mechanism, also allowing for an easier integration of embedded sensory-actuation systems.

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