DTA

Digital Theses Archive

 

Tesi etd-02072020-175732

Type of thesis
Dottorato
Author
LUNNI, DARIO
URN
etd-02072020-175732
Title
Smart Materials and Manufacturing Technologies for Soft Systems
Scientific disciplinary sector
Istituto di Biorobotica
Course
Istituto di Biorobotica - BIOROBOTICS
Committee
Tutor MAZZOLAI, BARBARA
Relatore CIANCHETTI, MATTEO
Tutor Dott. SINIBALDI, EDOARDO
Keywords
  • Bioinspiration
  • Electrospinning
  • Smart Materials
  • Soft Systems
Exam session start date
;
Availability
completa
Abstract
Soft systems are recently gaining increasing attention from the engineering and robotics point of view because of the potential capability to adapt to unpredicted conditions. Soft materials are already used in some industrial applications confirming the practical advantage given by the high conformability of such devices.<br>On one side, these systems require a peculiar approach in design, with a deep focus on the choice of materials that allow the integration into the device body of new functionalities. On the other side, new materials integrated in the structure need different manufacturing technologies to assemble and build such systems.<br>In this envision, the main objective of this thesis is to create intelligent systems implementing smart materials through unconventional manufacturing technologies. The studied devices presented here are: a new extruder exploiting innovative additive manufacturing deposition strategy for growing robots, soft bistable structures based on hygroscopic electrospun nanofibers and soft robotic arms implementing smart sensing used for control strategies.<br><br>Regarding the first device, we present a new design for material extrusion as additive manufacturing technology for growing robots. The conceptual design is proposed and based on the deposition of thermoplastic material. To guide the design of the system, we first studied the thermal properties through approximated models considering PLA (poly-lactic acid) as feeding material. The final shape and constituent materials are then accordingly selected. We obtained a simple design that allows miniaturization and a fast assembly of the system; and we demonstrate the feasibility of the design by testing the assembled system. We also show the accuracy of our thermal prediction by comparing the thermal distribution obtained from FEM simulations with experimental data, obtaining a maximal error of 8°C. Preliminary experimental growth results are encouraging regarding the potentialities of this approach that can potentially achieve 0.15 mm/s of growth speed. Our results suggest that this strategy can be explored and exploited for enabling the growth from the tip of artificial systems enouncing robots’ plasticity.<br><br>The second studied device implements hygroscopic nanofibers manufactured through electrospinning technology. The system was inspired by the tissue composition and structure of a plant exploiting bistability: the Dionaea muscipula. The leaves of this plant provide a remarkable example of an optimized structure that, owing to the synergistic integration of bistability, material and geometrical properties, permits to overcome the performance limits of purely diffusive processes. We present a hygroscopic bistable structure (HBS) obtained by bonding pre-stretched PDMS layers prior to depositing electrospun PEO nanofibers. A hygroresponsive bilayer (HBL) is also obtained by electrospinning of PEO on an unstretched PDMS layer. We mechanically characterized the hygroscopic material (Young’s modulus and hygroscopic expansion) so as to predict the response time of a bending HBL in response to a step humidity variation. The HBS response time (1 s) is sensibly lower than the one of purely diffusive HBL (10 s) thanks to bistability. An illustrative implementation is also presented, exploiting a HBS to trigger the curvature of a PDMS optical focusing system. The developed plant-inspired soft bistable structure could be also used for sensing (e. g., humidity), energy harvesting as well as advanced soft robotics applications.<br><br>Apart from the plant-inspired devices, we developed model-based control systems for soft arms implementing smart sensing technologies. First, we developed a control system for a variable section soft arm. The main goal of this control system was to obtain a target curvature at a desired section of the arm combining input shaping and feedback integral control in order to overcome modeling errors and constant disturbances. Second, on a similar soft arm, we integrated innovative methodologies to realize a smart sensing system. The system is based on a low-cost plastic optical fiber (POF) embedded in the body structure during the robotic arm fabrication. The POF is used as curvature sensor together with a simplified steady-state model in an Adaptive Extended Kalman Filter (AEKF). Sensory feedback was obtained through accelerometers, used as quantitative benchmark for the AEKF. The AEKF estimation turned out to be more accurate (RMS error &lt; 5°) than the model prediction alone and the soft sensor alone, thus supporting the proposed fully soft proprioception strategy.<br>
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